TigerGraph, provider of an ML and AI graph analytics platform, is releasing TigerGraph graph database v 3.9.3, introducing extended support for workload management, Kubernetes, and OpenCypher. Developed in collaboration with TigerGraph customers,

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Lastly, TigerGraph is a distributed and scalable graph database that supports the property graph model, GSQL query language, and TigerGraph Cloud service for network analysis and machine learning.

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CWI’s Database Architectures group was recognized for the paper “The LDBC Social Network Benchmark: Business Intelligence Workload”.

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For instance, China Mobile – the world’s largest mobile service provider with more than 900 million subscribers – uses TigerGraph to detect phone-based fraud in real time by analyzing the calling patterns of pre-paid subscribers. Subscribers are then ...

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TigerGraph, a firm offering a technology called “scalable graph database” for enterprises, has partnered with Merkle Science, a predictive blockchain monitoring and investigative platform, to achieve this. Merkle Science, with TigerGraph, will enhance ...

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An inmate at a New York correctional facility, Glenn Rodriguez, was due for parole soon. The man has been on his best behavior and was looking forward to being released and starting a new life. To Glenn’s horror, he was denied parole. An AI algorithm the parole board used gave this inmate a poor score, and as it wasn’t explainable, no one knew that something was going terribly wrong. Mr. Rodriguez fought his case and eventually was released after spending another unnecessary year in prison. Unfortunately, this type of mistake can occur whenever AI is deployed. If we don’t see the reasoning behind algorithms’ decisions, we can’t spot the problem. You can prevent this issue in your organization by following the explainable AI principles while . developing your artificial intelligence solution So, what is explainable artificial intelligence (XAI)? How to decide on the right level of explainability for your sector? And which challenges to expect on the way? What is explainable AI, and why should you care? When speaking of AI, many people think of black-box algorithms that take millions of input data points, work their magic, and deliver unexplainable results that users are supposed to trust. This kind of model is created directly from data, and not even its engineers can explain its outcome. Black-box models, such as neural networks, possess superior skills when it comes to challenging prediction tasks. They produce results of remarkable accuracy, but no one can understand how algorithms arrived at their predictions. In contrast, users can understand the rationale behind its decisions with explainable white box AI, making it increasingly popular in business settings. These models are not as technically impressive as black-box algorithms. Still, their transparency is a tradeoff as it offers a higher level of reliability and is preferable in highly regulated industries. What is explainable AI? Explainable AI (XAI) refers to a set of techniques, design principles, and processes that help developers/organizations add a layer of transparency to AI algorithms so that they can justify their predictions. XAI can describe AI models, their expected impact, and potential biases. Human experts can understand the resulting predictions and build trust and confidence in the results with this technology. When speaking of explainability, it all boils down to what you want to explain. There are two possibilities: Explaining AI model’s pedigree: how the model was trained, which data was used, which types of bias are possible, and how they can be mitigated. Explaining the overall model: this is also called “model interpretability.” There are two approaches to this technique: : a more understandable model, such as a decision tree, is used as an approximation of a more cumbersome AI model. Even though this technique gives a simple overview of what to expect, it remains an approximation and can differ from real-life results. Proxy modeling : designing AI models in a way that forces simple easy-to-explain behavior. This technique can result in less powerful models as it eliminates some complicated tools from the developer’s toolkit. Design for interpretability Source Explainable AI principles The US National Institute of Standards and Technology (NIST) developed : four explainable AI principles The system should be able to explain its output and provide supporting evidence (at least). There are several explanation types: ○ Explanations that benefit the end-user ○ Explanations that are designed to gain trust in the system ○ Explanations that are expected to meet regulatory requirements ○ Explanations that can help with algorithm development and maintenance ○ Explanations that benefit the model’s owner, such as movie recommendation engines The given explanation has to be meaningful, enabling users to complete their tasks. If there is a range of users with diverse skill sets, the system needs to provide several explanations catering to the available user groups This explanation needs to be clear and accurate, which is different from output accuracy The system has to operate within its designed knowledge limits to ensure a reasonable outcome Explainable AI example XAI can provide a detailed model-level explanation of why a particular decision was made. This explanation comes in a set of understandable rules. Following a simplified loan application example below, when applicants are denied a loan, they will receive a straightforward justification: everyone over 40 years old saves less than $433 per month and applies for credit with a payback period of over 38 years will be denied a loan. The same goes for younger applicants who save less than $657 per month. Why is explainable AI important? In some industries, an explanation is necessary for AI algorithms to be accepted. This can be either due to regulations and/or human factors. Think about brain tumor classification. No doctor will be comfortable preparing for a surgery solely based on “the algorithm said so.” And what about loan granting? Clients who got their application denied would want to understand why. Yes, there are more tolerant use cases where an explanation is not essential. For instance, predictive maintenance applications are not a matter of life or death, but even then, employees would feel more confident knowing why particular equipment might need preemptive repair. Senior management often understands the value of AI applications, but they also have their concerns. According to Gaurav Deshpande, VP of Marketing at TigerGraph, there is always a “but” in executives’ reasoning: “…but if you can’t explain how you arrived at the answer, I can’t use it. This is because of the risk of bias in the black box AI system that can lead to lawsuits and significant liability and risk to the company brand as well as the balance sheet.” The ideal XAI solution is the one that is reasonably accurate and can explain its results to practitioners, executives, and end-users. Incorporating explainable AI principles into intelligent software: Brings relief to system users. They understand the reasoning and can get behind the decisions being made. For example, a loan officer will be more comfortable informing a customer their loan application was declined if he can understand how the decision was made. Ensures compliance. By verifying the provided explanation, users can see whether the algorithm’s rules are sound and in accordance with the law and ethics. Allows for system optimization. When designers and developers see the explanation, they can spot what is going wrong and fix it. Eliminates bias. When users view the explanation, they can spot any presence of biased judgment, override the system’s decision, and correct the algorithm to avoid similar scenarios in the future. Empowers employees to act upon the system’s output. For example, an XAI might predict that a particular corporate customer will not renew their software license. The first reaction of the manager can be to offer a discount. But what if the reason behind leaving was poor customer service? The system will tell this in its explanation. Empowers people to take action. XAI enables the parties affected by certain decisions to challenge and potentially change the outcome (such as mortgage granting situations). Which industries need XAI the most? Healthcare Finances Automotive Manufacturing 1. Explainable AI in healthcare AI has many . Various AI-powered medical solutions can save doctors’ time on repetitive tasks, allowing them to primarily focus on patient-facing care. Additionally, algorithms are good at diagnosing various health conditions as they can be trained to spot minor details that escape the human eye. However, when doctors cannot explain the outcome, they are hesitant to use this technology and act on its recommendations. applications in healthcare One example comes from Duke University Hospital. A team of researchers installed a machine learning application called , which would send an alert when a patient was at risk of developing sepsis. The researchers discovered that doctors were skeptical of the algorithm and reluctant to act on its warnings because they did not understand it. Sepsis Watch This lack of trust is passed to patients who are hesitant to be examined by AI. Harvard Business Review published a study where participants were invited to take a free assessment of their stress level. registered for the test when they knew a human doctor would do the evaluation. Only 26% signed up when an algorithm was performing the diagnosis. 40% of the participants When it comes to diagnosing and treatments, the decisions made can be life-changing. No surprise that doctors are desperate for transparency. Luckily, with explainable AI, this becomes a reality. For example, Keith Collins, CIO of SAS, mentioned his company is already developing such a technology. Here is what he said: “We’re presently working on a case where physicians use AI analytics to help detect cancerous lesions more accurately. The technology acts as the physician’s ‘virtual assistant,’ and it explains how each variable in an MRI image, for example, contributes to the technology identifying suspicious areas as probable for cancer while other suspicious areas are not.” 2. XAI in finances Finance is another heavily regulated industry where decisions need to be explained. It is vital that AI-powered solutions are auditable; otherwise, they will struggle to enter the market. AI can help assign credit scores, assess insurance claims, and optimize investment portfolios, among other applications. However, if the algorithms provide biased output, it can result in reputational loss and even lawsuits. Not long ago, with its Apple Card product, which was inherently biased against women, lowering their credit limits. Apple’s co-founder, Steve Wozniak, confirmed this claim. He recalled that together with his wife, they have no separate bank accounts nor separate assets, and still when applying for Apple Card, his granted limit was ten times higher than his wife’s. As a result of this unfortunate event, the company was investigated by the New York State Department of Financial Services. Apple made headlines With explainable AI, one can avoid such scandalous situations by justifying the output. For example, loan granting is one use case that can benefit from XAI. The system would be able to justify its final recommendation and give clients a detailed explanation if their loan application was declined. This allows users to improve their credit profiles and reapply later. 3. Explainable AI in the automotive industry Autonomous vehicles operate on vast amounts of data, requiring AI to analyze and make sense of it all. However, the system’s decisions need to be transparent for drivers, technologists, authorities, and insurance companies in case of any incidents. Also, it is crucial to understand how vehicles will behave in case of an emergency. Here is how Paul Appleby, former CEO of a data management software company Kinetica, voiced his concern: “If a self-driving car finds itself in a position where an accident is inevitable, what measures should it take? Prioritize the protection of the driver and put pedestrians in grave danger? Avoid pedestrians while putting the passengers’ safety at risk?” These are tough questions to answer, and people would disagree on how to handle such situations. But it is important to set guidelines that the algorithm can follow in such cases. This will help passengers decide whether they are comfortable traveling in a car designed to make certain decisions. Additionally, after an incident, the provided explanation will help developers improve the algorithm in the future. 4. Explainable artificial intelligence in manufacturing AI has many applications in manufacturing, including predictive maintenance, inventory management, and logistics optimization. With its analytical capabilities, this technology can add to the “tribal knowledge” of human employees. But it is easier to adopt decisions when you understand the logic behind them. Heena Purohit, Senior Product Manager for IBM Watson IoT, explains how their AI-based maintenance product approaches explainable AI. The system offers human employees several options on how to repair a piece of equipment. Every option includes a percentage confidence interval. So, the user can still consult their “tribal knowledge” and expertise when making a choice. Also, each recommendation can project the knowledge graph output together with the input used in the training phase. Challenges on the way to explainable AI The need to compromise on the predictive power Black box algorithms, such as neural networks, have high predictive power but offer no output justification. As a result, users need to blindly trust the system, which can be challenging in certain circumstances. White box AI offers the much-needed explainability, but its algorithms need to remain simple, compromising on predictive power. For example, where algorithms produce remarkable results classifying brain tumors and spotting breast cancer faster than humans. However, when doctors decide on patients’ treatment, it can be a life and death situation, and they want to understand why the algorithm came up with this diagnosis. It can be daunting for doctors to rely on something they do not understand. AI has applications in radiology The concept of explainability There is no universal definition of explainability. It is often a subjective concept. Users might expect one type of explanation, while developers are willing to provide something else. Also, different audiences require tailored justifications, which results in one XAI system having to explain the same output in several different ways. Security and robustness-related issues With XAI, if clients gain access to the algorithm’s decision-making process, they might apply adversarial behaviors, meaning they will take deliberate actions to change their behavior to influence the output. One published a concern that someone with technical skills can recover parts of the dataset used for algorithm training after seeing the explanation, thereby violating privacy regulations. study How to get started with responsible and explainable AI When your company is preparing to deploy responsible XAI solutions, the first thing would be to determine what exactly you need to explain and to whom. Some of the questions addressed during the planning stage can be: Is it a particular decision or the overall model? Who is your audience? Are they technical users? Do you need to provide different explanations for every output, or does one explanation suffice for everyone involved? Next, your company needs to decide on the degree of explainability. Not all AI-based tools require the same degree of interpretability. six application criticality components that will help you determine your desired XAI level: PwC identifies Decision impact: how will the decision affect all the involved parties? This is not limited to revenue-related aspects. For example, an incorrect cancer diagnosis will have a dire impact, while recommending an irrelevant movie will not be a big issue. Control level: is it an autonomous AI system that makes decisions and acts upon them, or does the system’s output recommend actions to humans who can choose whether to follow or not? Risks: any potential harm that the algorithms can cause, such as impacting operations, sales, employees, any ethical, legal, environmental, or health-related considerations. Regulations: the legal framework of the industry and whether the decisions need to be explained. Reputation: how the system interacts with stakeholders — within the business and with the society in general. Rigor: if the application is accurate and able to generalize its conclusions to unseen data. Finally, even after explainable artificial intelligence is in place, it is best to take action to ensure your data usage is ethical. Perform timely audits of your algorithms. A strong explainability feature can reveal any bias sneaking into your software. However, if you settled on a limited explainability solution, bias could remain unnoticed. Also, you can subscribe to the . Or even develop your own set of ethical principles of data usage, as Microsoft did. They included fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Partnership on AI consortium On a final note… Implementing traditional , let alone its explainable and responsible version. Despite the obstacles, it will bring relief to your employees, who will be more motivated to act upon the system’s recommendations when they understand the rationale behind it. Moreover, XAI will help you comply with your industry’s regulations and ethical considerations. AI is full of challenges If you have an idea of an explainable AI solution to build, or if you are still unsure of how explainable your software needs to be, . consult ITRex XAI experts

Explainable AI Principles: What Should You Know About XAI

Meet the Winners of Startups in North and Central America

Thank you to the problem solvers who campaigned extra hard in the first-ever competition! HackerNoon Startups of the Year focuses on recognizing the passion and stamina of those willing to find and create effective tech solutions for their cities. HackerNoon’s Startups of the Year To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Awards, where cheaters were caught, are under review.) startups.hackernoon.com From all of us at , thank YOU for your excellent campaign perseverance🥺! We look forward to seeing you again in the 2022 campaign (stay tuned 😉)! HackerNoon Without further adieu, let us kick off by sharing the cities with top votes per region for 2021 🎉 . our series of Best Startups of the Year Stay tuned for the rest of the winners per region to be over the course of this week (Feb 14, 2022). announced North and Central America America is the concrete jungle where dreams are made of, the world’s capital of fashion, showbiz, business, major industry, etc., and surely, of Startups! Representing , we have: North and Central America (14,563 votes): Winner: Boulder Bury.com (10,521 votes) - Winner: New York City Hum Capital (7,987 votes) - Winner: San Francisco Zentist (7,713 votes) - Winner: Redwood City TigerGraph (469 votes) - Winner: Los Altos Stobox To see more awards in this region please click here. Europe Charming, sophisticated, cosmopolitan. There are many beautiful words used to describe this region. With its eccentric vibe, is also a place where new Startups thrive. Europe Representing Europe we have: (10, 010 votes) - Winner: Belfast Bob & Berts (5, 066 votes) - Winner: Ghent WeGroup (4, 712 votes) - Winner: Porto TAIKAI (3, 779 votes) - Winner: City of London Crypto Quantique To see more awards in this region please click here. Oceania The heat, the beaches, roaming kangaroos, or tarantulas? No. Startups! Representing the region of are: Oceania (1, 011 votes) - Winner: Sydney shotstack (430 votes) - Winner: Melbourne Doohly (118 votes) - Winner: Gold Coast Easil (76 votes) - Winner: Tauranga LawVu (40 votes) - Winner: Western Junction Today We Learned To see more awards in this region please click here. Asia Diverse in both ethnicity and culture, known for exotic sceneries as well as friendly people. Many Asian countries are on the verge of mass globalization and modernization. These fast-developing economies give way for many potential Startups. We present, for : Asia (12, 686 votes) - Winner: Singapore Cake DeFi (3,355 votes) - Winners: and (It’s a tie, we’re working on it!) Tel Aviv Applitools hyro (2, 402 votes) - Winner: Beirut Hovi Digital Lab (1.006 votes) - Winner: Hyderabad Quixy (756 votes) - Winner: Hanoi DeFi For You To see more city awards in this region please click here. Africa The second biggest continent in the world, home to many big animals, the longest river in the world AND the largest desert in the world. is known for many big things and sure enough, big Startups. Africa Representing Africa, we have: (884 votes) - Winner: Johannesburg MiBRAND (804 votes) - Winner: Lagos Goodtalent (526 votes) - Winner: Cape Town Revix (420 votes) - Winner: Lusaka ComGrow (224 votes) - Winner: Nairobi UTU To see more city awards in this region please click here. South America “South America is the land of the extreme.” - National Geographic Extremely beautiful people? Yes. Extremely worth-noticeable Startups? Hell yeah! We proudly present, Startups in : South America (217 votes ) - Winner: São Carlos Predify (193 votes) - Winner: Belo Horizonte CM Technologia (106 votes) - Winner: Porto Alegre Privacy Tools (82 votes) - Winner: Quito Agrodemanda (55 votes) -Winner: Rio De Janeiro Bybrand To see more city awards in this region please click here. Once again, and to all of the top Startups of each region this year! HUGE CONGRATULATIONS BIG PROPS The whole team at would like to thank you for your excellent campaign perseverance and we look forward to seeing you in the 2022 campaign. HackerNoon As part of honoring your participation, we will continue to host your company in this directory until further notice ( for legal reasons we hesitate to say ‘forever’..but pretty much forever).  As traffic is directed to the site, it will act as a promotional platform for your company. Have a great Valentine’s Day, HackerNoon loves y’all <3 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by Tuan Anh Vu. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

To the Winners of #Startups2021, With Love:

Goal: Migrate the UML-based engineering model of a spacecraft to TypeQL Why Do This Migration in the First Place? The lifecycle of a spacecraft is roughly divided into seven consecutive design phases. Part of the early design phases deals with the feasibility of the intended mission. Feasibility is identified by assessing each design aspect that is needed to accomplish the specific mission. This requires that engineers lay out all possible design options and iteratively go through them in relation to all the other engineering design options. Ultimately ending up with a sound system solution. This requires various experts across domains to be reporting into and checking against a central engineering model. This design process is called concurrent engineering. “A spacecraft is a complex puzzle of subsystems. Concurrent Engineering is a design process enhancing communication between experts and accelerating the convergence towards a sound design solution.” Each expert needs to report their design architecture into a central engineering model. This then becomes the blueprint for the spacecraft. Each design has a project tree comprised of all the components of a particular aspect of the space mission. This quickly becomes a lot of information to organize, maintain, and keep track of. The structure of this blueprint is defined by European space standards — ECSS-E-TM-10–25A — provided in a UML class diagram. Over time, more and more engineering models are created. However, these models are disparate, making it challenging for engineers to make use of them as a collective source of information to be used over time. To enhance the data linkage, reusability, and interpretability of stand-alone models, the need to merge the models into one database becomes clear. This then allows for the mining of information within the models, comparisons between them, and the ability to infer new knowledge from them. Why a Knowledge Graph and not a SQL database? SQL databases aren’t built to represent relationships in your data and, for the most part, don’t handle heterogeneous and unstructured data well. Since Audrey’s team’s goal is to mine and discover information within the models, a database that can model relations naturally and aid in inferring new knowledge is needed. To get the inference they were looking for, a schema that included the ability to define rules was an important requirement. “Without a schema to structure the database, there is no rule-based reasoning, thus no inference.” Once the team noted the requirements they were looking for in a database, they set out to find a suitable one. Researching the landscape, they noted quite a few options: Neo4J, Stardog, AllegroGraph, AnzoGraph, TigerGraph, and TypeDB. The parameters for making their decision were: OS portability: other evaluated databases were only available on Linux systems, and they needed to allow engineers to work with OS or Windows as well as Linux. Learning Curve: a s their team didn’t have a background in RDF, they felt it would be easier and faster to learn one language or tool vs. the multitude of Semantic Web Technologies. Scalability: they didn’t want to be limited by the database in the number of nodes and relations. Inference Engine: does the database allow for inference without requiring the development or integration of another tool? Visualization: is there a visualizer available? Support: what do the community and formal support look like for the database Licensing: a s their team is part of the University of Strathclyde, the availability of an open-source version was preferred. Ultimately, the team chooses Vaticle’s TypeDB for the simplicity of the language — making onboarding easier and faster for engineers — the integrated reasoning engine, the presence of a strong community that is active and supports each other, and the availability of an open-source version. How to Migrate UML to TypeQL Unified Modelling Language (UML): a standardized Object-Oriented (OO) modeling language for specifying, visualizing, constructing, and documenting software systems. A Brief Introduction to UML The building blocks of a class diagram in UML are: Class: describes a set of objects sharing the same features/constraints/semantics (mapped as an entity in TypeDB schema) Interface: a non-instantiable class declaring features to be implemented by another class (mapped as an abstract type in TypeDB schema) Property: a structural feature of a class representing an attribute or the end of a relationship (mapped as an attribute in TypeDB schema) Relationship: links one or more classes — there are multiple relation types as well: association, generalization, dependency, etc. (mapped as relations in TypeDB schema) Migrating Spacecraft Engineering UML to TypeDB There are two main steps to migrating an engineering model in UML to TypeDB: and . defining a schema in TypeQL inserting data into TypeDB TypeDB is a direct implementation of the Entity-Relation diagram and makes Entities, Attributes, and Relations first-class citizens of the database. Defining a Schema As noted above, we map a UML class to a TypeDB entity, a UML property to a TypeDB attribute, and a UML relation to a relation in TypeDB. The schema is also where we can define the rules for the reasoning engine. Starting with the mapping of a UML class to a TypeDB entity, the first thing to note is that TypeDB allows for type inheritance. UML Class to TypeDB Entity In the team’s UML model, they have a class called a Mixin, which is a class containing methods (attributes and roles) for use by other classes. These Mixin classes are not migrated, but instead, the attributes and role types are redefined for each of their “child” classes — . In TypeDB this means they are defined as attributes and role players owned or played by entities (classes). TypeDB’s type inference comes in handy here For the abstract classes, an abstract entity is defined. For concrete classes, a standard entity is defined. Each of these may have Mixin and non-Mixin classes, mapped as noted in the paragraph above. Looking at the example in the image below, we see that a Person is a subclass of four other classes. Three of these: , , and , are Mixin's. While Thing is a concrete class. We redefine the three Mixin's as attributes of the person entity, and using the TypeQL keyword sub; we define it as a subtype of thing. shortnamedthing namedthing deprecatablething UML Property to TypeDB Attribute A UML property can be an attribute (value type) — defined as an attribute in the schema — or the end of a relation (reference type) — defined as a role in the schema. Additionally, the UML property is either directly owned by the class or inherited from another class. In the case that a property is inherited from another class, it can inherit from a Mixin class or a non-Mixin class. When it is inherited from a non-Mixin class, we define the inheritance from the parent entity within the TypeDB schema. The example above shows all the attributes in the UML Diagram User Manual are defined as attributes owned by the person entity type. We also see an example of how we would define these using TypeQL (TypeDB's query language) in the console: define givenName sub attribute, value string; surname sub attribute, value string; organizationalUnit sub attribute, value string; isActive sub attribute, value boolean; The mapping of a UML relationship to a relation in a TypeDB schema is a bit more nuanced. As there are multiple types of relationships in UML, there are some decisions to make in how to map to a TypeDB schema. For Audrey’s team, they were working with just two types of UML relationships: association relationships and composition relationships. For these, their mapping decision was to use different naming conventions in their TypeDB schema: reference and containment relation types, respectively. UML Relationships to TypeDB Relations As we see in the example below, we need to define the relation type and the role type that is being played by the entity. Exporting the engineering models as JSON files, we are able to map this JSON file to the TypeDB schema and build an insert query to get it into TypeDB. This is done via migration pipeline templates that take each element found in the JSON file and look at the class type. Each class type has its own template, which then builds the TypeQL insert queries. We can see an example of one such template below. Inserting Data Where is This Method Currently Being Applied? The migration was applied to three Engineering Models, including two internal CubeSat models. This early implementation already demonstrated the power of using TypeDB as a knowledge graph for this use case. Tasks commonly performed by engineers such as mass budget computation could be automated, and TypeDB provided new insights into the data, enabling the quick recall of similar missions. Thank you and Some Helpful Links We appreciate Audrey and her team for their hard work and for taking the time to put together this presentation. The TypeDB community looks forward to having her back to share the progress and future work with the European Space Agency, the University of Strathclyde, RHEA Systems B.V. and Airbus within the Networking/Partnering Initiative (NPI). Watch the full presentation replay on YouTube Dig into TypeDB further via GitHub Reach out to Audrey and the Vaticle team on the TypeDB community Discord

Spacecraft Engineering Models: How to Migrate UML to TypeQL

Welcome to " ". This week we have an Entrepreneur and tech expert who thinks Monday is the most exciting day of the week. His previous experience includes working for Tech giants like Twitter, Teradata, IBM--before he found fulfillment in graphs at his venture TigerGraph! Mondays with Entrepreneurs 's the Guest? Who : Name Dr. Yu Xu Dr. Yu Xu is CEO of 💪What they do: TigerGraph So before starting your week, pause for a coffee break ;) and enjoy this chit-chat with Dr. Yu Xu. 1. What topic areas are you most passionate about? I’m most passionate about bringing the power of graphs to advanced analytics, Machine Learning (ML), and Artificial Intelligence (AI). With the native and parallel technology breakthroughs in the industry by TigerGraph, utilizing graph analytics on large data sets to provide unique features that improve traditional ML/AI models is critical to many use cases. Such use cases include anti-money laundering, supply-chain optimization, real-time fraud prevention, customer journey analytics, personalized recommendation systems, and more. 2. Through your experience, how do you visualize the evolution of data mining and data science? Could you brief them? Data mining and data science have a long history. Due to the data explosion over the past 10 years and the dramatic cost reduction in data storage and performance improvements in CPU/GPU, deep learning (auto-ML) is fundamentally changing how data mining and sciences are applied across industries today. In addition to all the exciting things happening in data mining and data science right now, an up and coming trend to shed light on is how to leverage graph technology to augment ML/AI, whether it’s new features only available through graphs, or explainability for ML models. Graph neural networks are gaining tons of attention in both academia and industries. TigerGraph has organized an open conference called Graph + AI Summit, which occurs twice a year to facilitate this. The last two virtual conferences have been hugely successful, bringing together over 10,000 data scientists and researchers, Fortune 100 companies, innovative startups, and industry analysts to share best practices for combining graph and AI. 3. How did you get to become an expert in Data Analytics? I gained experience in data analytics through my years of experience training in the field. I’ve always been a database guy, as I see it as an integral part of the data analytics infrastructure. To further my knowledge in this discipline, I got my Ph.D. in Computer Science from the University of California, San Diego. Beyond this, working with customers every day helps me learn what matters to real-world use cases. 4. 2 Sentences that best summarize how you learned ML and data analytics? I learned the basics and theories from my M.S. and Ph.D. studies in Computer Science. I also learned a great deal while at Teradata, working with leading customers and their use cases. And while I was at , I got to work alongside some of the best data scientists and ML engineers in the world. These experiences taught me a lot more than what I learned from my schooling. Twitter Now, I am learning from leading companies such as UnitedHealth Group, JP Morgan Chase, Visa, Jaguar Land Rover, Duke Energy, Nike, Walmart, and many other innovative companies, specifically using graphs to improve their ML and data analytics. I find this, even more, educational and inspiring. 5. What’s the main value proposition of Graph Analytics Technology? The main value propositions for graph analytics technology are highlighted with the phrase, “Connect, Analyze and Learn from Data.” Graph analytics provides these three core capabilities to drive business outcomes: Connect all data sets and pipelines: Organizations of all sizes are connecting all of their data, including Spark pipelines, for machine learning and Kafka pipelines for streaming data with a distributed graph database. Analyze connected data for never-before insights with advanced analytics: After connecting all of the data, the next step is to analyze the data 10-100 times faster with graph-based analytics. Learn from the connected data with in-database ML: After connecting and analyzing data, organizations are learning from the data with graph-based ML. 6. Can you please brief us on How are graphs used for e-commerce/customer intelligence, anti-fraud detection, supply chain intelligence, and other areas? For this question, I think it will be most powerful to explain using customer examples aligning with the core capabilities listed in question 5. These include: You can connect data sets and pipelines: One customer example would be . They are connecting 200+ sources to deliver a real-time customer 360 to improve the quality of care for 50 million members. UnitedHealth Group The graph is also used to analyze connected data, providing never-before insights through advanced analytics: For example, has accelerated supply chain planning from 3 weeks to 45 minutes, reduced supplier risk by 35%, and added 100 million pounds annually in profits. NewDay, a leading specialist financial services provider and one of the largest issuers of credit cards in the UK, uses advanced graph analytics to prevent and preempt financial fraud. Jaguar Land Rover There are many opportunities to learn from the connected data with in-database ML: has built an AI-based customer 360 with in-database ML for entity resolution, personalized recommendations, and fraud detection. Graph-based ML at Intuit has reduced at-risk events by 50% while improving accuracy by 50%, overall reducing false positives. Seven out of the top 10 banks are driving real-time fraud detection and credit risk assessment with in-database ML. Intuit 7. Regarding Tiger Graph’s growth, What is the biggest improvement that you introduced in the last 12 months that has considerably improved your workflow? The #1 challenge for TigerGraph right now is our goal of hiring 300+ talented team members quickly to scale our business. Before the pandemic hit, TigerGraph was mostly hiring in the San Francisco Bay Area, especially within our engineering teams. In the middle of last year, we decided to drop the location requirement, which is the single biggest improvement that has materially changed our business. 8. What are the top challenges you currently face as a professional data scientist and CEO? And how do you go about tackling them? Hiring the right talent is critical to the success of our business. As mentioned earlier, TigerGraph is hiring in many locations now. For example, we’re building an R&D Innovation lab in San Diego and a sales center in Dallas. Another challenge we’ve faced is how to take our successes in the U.S. and UK, and expand our business to more regions. We’re hiring experienced leaders in various regions who can help the company build local teams quickly. 9. Which non-paid activities would you be keen to take part in if the opportunity raised your profile or delivered value to your audience? I would love to volunteer and educational opportunities with student organizations to help students learn how to navigate new tools like graph analytics and UI/BI in data analytics. 10. What are your passions outside of work? I enjoy reading books and listening to music. 11. And the last question, since the series is named “Monday with Entrepreneurs”, What are your Monday Morning rituals? I start my Monday mornings with a cup of coffee! Following that, I get ready for our TigerGraph executive meeting and look forward to sharing what’s been on my mind over the past couple of days with the team and hearing their updates in return. Monday is one of the most exciting days of the week, in my opinion, so I’m pretty psyched in the morning . I'm so glad we had this time together. Thank you for taking a break from your life to share your knowledge with us. I'm sure readers will enjoy and learn. Shahmeer: Thanks, Hackers, for reading :) If you have any suggestions regarding questions to ask in this series or if you want to get featured, I am available on , and you can also reach out to me on ;) slack Linkedin

"Connect, Analyze and Learn from Data" - Dr. Yu Xu

What does graph have to do with machine learning, and data science? A lot, actually, and it goes both ways. Over the last few years, we have seen what was once a niche research topic – graph-based machine learning – snowball. The Year of the Graph was among the first to take stock, point towards this development, and recognize graph-based AI as a key pillar for future development in the field. In this edition of the YotG Newsletter, we highlight resources focused on . Which is not to say there’s lack of news on , , and – on the contrary. graph-based machine learning and data science graph analytics graph databases knowledge graphs 2020 has turned Graph Machine Learning into a celebrity of machine learning, argues Michael Bronstein. Arguably, he’s a celebrity of Graph Machine Learning himself. A pioneering and prolific researcher at Imperial College London, as well as the Head of Graph Learning Research at Twitter. Bronstein sought the opinion of prominent researchers in the field of graph ML and its applications trying to summarise the highlights of the past year and predict what is in store for 2021. For a glimpse of Bronstein’s own work, check his Knowledge Connexions presentation “ ” Deep Learning on Graphs: Past, Present, And Future What 2021 holds for Graph ML? The end of the year is a good time to recap and make predictions. 2020 has turned Graph ML into a celebrity of machine learning. For this post, I sought the opinion of prominent researchers in the field of graph ML and its applications trying to summarise the highlights of the past year and predict what is in store for 2021. Machine learning can help bootstrap and populate knowledge graphs. The information contained in graphs can boost the efficiency of machine learning approaches. A panel featuring some of the world’s top experts in AI, coming from all sides of the spectrum, got together to discuss how . AI + Knowledge are a match made in heaven , , , , , and cover a number of ways graphs, AI and knowledge interact in this 2-hour tour de force. Isabelle Augenstein Nathan Benaich Giuseppe Futia Amy Hodler Katariina Kari Fabio Petroni For the , check our Sergey Ivanov’s quick introduction. Top Applications of Graph Neural Networks 2021 AI + Knowledge – a match made in heaven? What can knowledge-based technologies do for Deep Learning? What is Graph AI, how does it work, what can it do? What’s next? What are the roadblocks and opportunities? Whether you are just getting started with graph-based data science and AI, or you are already advanced, is where you can get both inspiration and hands-on knowledge. Learning with the Machines Aleksa Gordic will share . Bob van Luijt will explain . Paco Nathan will introduce , and then deliver a . How to get started with Graph Machine Learning How businesses apply AI-first solutions in production with the Weaviate Vector Search Engine Graph-Based Data Science hands-on masterclass Tara Safavi will share her work on , and Ashleigh Faith will show how to . CoDEx: A Comprehensive Knowledge Graph Completion Benchmark Add more context to machine learning, using taxonomies and knowledge graphs Learning with the machines Connected Data London Meetup #4 Open source, Graph AI, Search, and Data Science with Python April 15 – 16, 2021 When talking about AI, however, be it graph-based or not, there’s semantics involved. And not just in terms of defining what AI is. AI is not machine learning. Knowledge-based technologies are also AI. Lately, we started seeing more voices advocating for approaches to bridge the worlds of machine learning and knowledge-based technologies. just Gary Marcus, as he shared in a , and in his . Artur d’Avila Garcez and Luis C. Lamb, in their work , Amit Sheth et. al. In . And Frank van Harmelen et. al, who created . series of articles on ZDNet Knowledge Connexions keynote Neurosymbolic AI: The 3rd Wave Semantics of the Black-Box: Can Knowledge Graphs help make Deep Learning systems more interpretable and explainable? Modular Design Patterns for Hybrid Learning and Reasoning Systems Rebooting AI: Deep learning, meet knowledge graphs Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there. The last few months have been good for graph databases. A graph database – Neo4j – made the for the 1st time. Neo4j also announced on GCP, preview on AWS. Stardog too. Top 20 in DB Engines general availability of its Aura managed cloud service announced its own cloud DBaaS We had a series of funding rounds, and an upcoming IPO. TigerGraph scored , Katana Graph , and . In the meantime Bitnine, makers of Agens Graph, is – the first in the market. $105M Series C $28.5M Series A Memgraph $6.7M TerminusDB €3.6M working on its IPO Last but not least, we had a round of new releases. to make working with graph databases easier. , , , , , , , and . AWS open-sourced a Graph Notebook Franz Inc released AllegroGraph v7.1 Grakn Labs released Grakn v2.0 Alpha Nebula Graph released v2.0 Ontotext released GraphDB v9.6 RDFox released v.5.0 Stardog released v7.6 TerminusDB released v4.0 From databases to platforms: the evolution of Graph databases Everything you’ve always wanted to ask about graph databases, but did not have the chance to. It’s also been a very active period in terms of book publishing. Whether you’re interested in Graph Databases, Data Analytics on Graphs, or Knowledge Graphs and Semantic Technologies, there’s a new book out there for you. Dave Bechberger and Josh Perryman published . The book introduces graph database concepts by comparing them with relational database constructs. It promises to include just enough theory to get started, then progress to hands-on development. “Graph Databases in Action” on Manning Ljubiša Stanković et.al. . The authors revisit graph topologies from a modern data analytics point of view, and proceed to establish a taxonomy of graph networks. With this as a basis, they show how the spectral analysis of graphs leads to even the most challenging machine learning tasks, such as clustering, performed in an intuitive and physically meaningful way. published “Data Analytics on Graphs” on Now William L. Hamilton published . It aims to serve as If you need a jumping off point to learn more about Graph analytics and the use of graphs in machine learning, as these fields have exploded in the past few years. “Graph Representation Learning” on Morgan & Claypool A Knowledge Representation Practionary Knowledge representation is shorthand for how to represent human symbolic information and knowledge to computers to solve complex questions. KR applications range from semantic technologies and knowledge management and machine learning to information integration, data interoperability, and natural language understanding. Knowledge representation is an essential foundation for knowledge-based AI. AI capabilities are improving daily, but understanding AI’s contextual data and problem-solving approaches is not easy. Graphs are a typically human way of navigating and accessing data. . Graphs and AI bring out the best in the symbiosis of humans and systems To introduce Knowledge Graphs into Organizations you need to assess the right moment, the organization’s semantic maturity, and to overcome overcome segregation/specialization. It’s important to involve the right stakeholders, and . develop KGs in an agile way Gartner: Top 10 data and analytics technology trends for 2021 By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making A common use case when working with graphs is converting tabular data to graphs, and adding semantics. From research efforts such as “ “, “ ” and , to and implementations of W3C recommendations, progress is being made. Semantic Annotation for Tabular Data Interactively Constructing Knowledge Graphs from Messy User-Generated Spreadsheets DAGOBAH Linked Data Wizard CSV on the Web csvwlib is a Python implementation of the W3C CSV on the Web recommendations This enables converting tabular data, and optionally its associated metadata, to a semantic graph in RDF or JSON-LD format. Tabular data includes CSV files, TSV files, and upstream may be coming from spreadsheets, RDBMS export, etc. Another domain which historically has been prominent for experiments and . Schema.org had 2 new releases recently, and , summarized by Aaron Bradley and Dan Brickley, respectively. applications of graphs is SEO v.11 v.12 Search engines . Structured markup vocabularies include RDFa & SchemaOrg. JSON-LD is more portable, easier to manage, and it has . encourage content creators & developers to implement structured data recently overtaken Microdata , content creation and digital marketing across the web. The include earning your presence in the Knowledge Graph, as it has tremendous impact across multiple platforms. Knowledge Graphs have huge implications for SEO Top 5 trends for SEO in 2021 SEO Turns to Data Graphs to Learn About the Web Τhe web as a data graph is a new direction for SEO. Many of the articles that people writing about SEO are about to involve web pages and links between pages. Τhis post is about entities and relationships between entities and facts that are written about on web pages, and responses to queries from data graphs on the web about facts and attributes related to entities found on web pages. Journalists, and data journalists, can use Crowdsourcing, Open data and Knowledge Graphs as a data source, by . A new interconnected ecosystem for research is shaping up. leveraging Wikidata Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond Science and data are interwoven in many ways. The scientific method has lent a good part of its overall approach and practices to data-driven analytics, software development, and data science. Now data science and software lend some tools to scientific research. More use cases for graphs, from exotic to everyday. Research claims an , using machine learning and graphs. AI tool can distinguish between conspiracy theories and true conspiracies for knowledge contained in the content universe. A self-supervised learning task is crafted. Random edges in the graph are selected to form a test set, and the rest of the graph is conditioned to predict missing edges. Netflix uses Knowledge Graphs MIT Machine Learning Uses ‘Graph Grammar’ to Automate and Optimize Robot Design A series of related objects are transformed into new graphs via algorithms, generating new abstractions that adhere to a set of transformational rules Wrapping up with more resources for graph-based machine learning. (pronounced “giraffe”) is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilites for working with graphs, and a ‘zoo’ of forkable graph neural network models. Jraph : Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured as graphs. Despite being what can be a confusing topic, GNNs can be distilled into just a handful of simple concepts. Graph Neural Networks explained AutoML systems simplify and democratize AI. Tsinghua University developed an AutoML framework & toolkit designed for graph data and tasks. handles all stages of graph learning, may reduce labour & bias in machine learning. AutoGL s Understanding Graph Embedding Graph embeddings have become increasingly important in Enterprise Knowledge Graph strategy. What are they, and how are they related to Mowgli’s Walk? To get the Year of the Graph Newsletter in your inbox, subscribe here Previously published at https://yearofthegraph.xyz/newsletter/2021/04/graph-based-data-science-machine-learning-and-ai-the-year-of-the-graph-newsletter-spring-2021/

Graph Learning News Review: Hot-Topic Facts of Spring 2021 to Take into Account

As 2020 is coming to an end, let's see it off in style. Our journey in the world of Graph Analytics, Graph Databases, Knowledge Graphs and Graph AI culminates. The representation of the relationships among data, information, knowledge and --ultimately-- wisdom, known as the data pyramid, has long been part of the language of information science. Digital transformation has made this relevant beyond the confines of information science. COVID-19 has brought years' worth of digital transformation in just a few short months. In this new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. So how do we go from data to information, and from information to knowledge? This is the key question aims to address. Knowledge Connexions Graphs in all shapes and forms are a key part of this. is a visionary event featuring a rich array of technological building blocks to support the transition to a knowledge-based economy: Connecting data, people and ideas, building a global knowledge ecosystem. Knowledge Connexions The Year of the Graph will be there, in the workshop "From databases to platforms: the evolution of Graph databases". George Anadiotis, Alan Morrison, Steve Sarsfield, Juan Sequeda and Steven Xi bring many years of expertise in the domain, and will analyze Graph Databases from all possible angles. This is the first step in the relaunch of the . Year of the Graph Newsletter subscribers just got a 25% discount code. To be always in the know, , and follow the newly launched ! In addition to getting the famous YotG news stream every day, you will also get a 25% discount code. Year of the Graph Database Report subscribe to the newsletter Year of the Graph account on Twitter From databases to platforms: the evolution of Graph databases This workshop wants to help answer everything you’ve always wanted to ask about graph databases, but did not have the chance to. We have tons of news in the Graph Database world. Fluree is going open source, after securing a new round of funding. , enabling SQL access to Knowledge Graphs and visual mapping of tabular data to RDF. Neo4j released a , and a . Ontotext announced a new version, 9.4 new version of its Graph Data Science Library Connector for Apache Spark TigerGraph announced , and a for faster Graph analytics using FPGA hardware. TerminusDB which enables sharing, building, integrating databases collaboratively. And . free licenses for its Enterprise version for databases up to 50GB partnership with Xilinx released  TerminusHub, MarkLogic got acquired by Vector Capital Fluree, the graph database with blockchain inside, goes open source A hitherto under the radar graph database that uses blockchain to support data lineage and verification wants to take over the world, starting with the US Department of Defense. Speaking of graph databases - we have new book releases. " " by Dave Bechberger and Josh Perryman has just been released by Manning Books. It aspires to be a  comprehensive overview of Graph DBs and how to build applications using them, working with Apache open source tools (all code examples use Gremlin). Graph Databases in Action If you are looking for a simpler, getting started book: , a Neo4j Special Edition, introduces the basics of graph database technology from building a rich graph data model to deploying your 1st graph-powered application. Graph Databases For Dummies Last but not least: Denise Gosnell's " ", and Panos Alexopoulos' " , both from O'Reilly. The former is more focused on analytics, the latter on data models for knowledge graphs. To learn the theory and practice of those skills from Alexopoulos, and have the chance to win a copy his book, ! The Practitioner's Guide to Graph Data Semantic Modeling for Data" join his KnowCon2020 sessions Flawless Semantic Modeling in OWL and SKOS The theory and practice of semantic data modeling, focusing on recognizing and avoiding bad practices when developing ontologies, taxonomies, knowledge graphs and other semantic data artifacts There are 2 approaches used to model graph-shaped data: property graphs and RDF, or semantic graphs. In a series of posts, Irene Polikoff from Top Quadrant outlines their differences. Knowledge Graphs vs Property Graphs – Part III Part 3 completes this article series by discussing some important topics beyond the critical differentiators in the terminology and capabilities of Property Graphs and Knowledge Graphs covered in Parts 1 and 2. In terms of adoption versus hype, we have mixed messages from Gartner. On the one hand, the Hype Cycle for Artificial Intelligence 2020 places Knowledge Graphs on the Peak of Inflated Expectations. On the other hand, the . Hype Cycle for Emerging Technologies 2020 places Ontologies and Graphs on the Trough of Disillusionment In reality, none of this matters too much. What matters is that Knowledge Graphs are seeing increasing adoption in the real world. More and more applications based on Knowledge Graphs are being built and deployed, and Knowledge Graph research and innovation is accelerating. 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020 While five new AI solutions enter this year’s Hype Cycle for AI, the democratization of AI and the industrialization of AI megatrends dominate the AI landscape in 2020. More Gartner. The 2020 Magic Quadrant for Metadata Management Solutions is out, and just like 2019, it includes 2 vendors whose solutions rely on knowledge graphs and semantics. and the - both present in KnowCon2020. Data.world Semantic Web Company with its PoolParty solution Gartner Magic Quadrant for Metadata Management Solutions Demand arising from a variety of data and analytics initiatives drives strategic requirements for metadata management solutions. This Magic Quadrant will help data and analytics leaders find the most appropriate vendor and solution for their organizational needs. Where Deep Learning ends, Knowledge comes to the rescue. Gary Marcus is one of AI's top minds, and has been a vocal critic of the Deep Learning monoculture for years. He has put forward a well-rounded proposal on how to Reboot AI by infusing it with knowledge, including in the form of knowledge graphs. . Marcus will share this vision in KnowCon2020 What's next for AI: Gary Marcus talks about the journey toward robust artificial intelligence Gary Marcus is one of the more prominent, and controversial, figures in AI. Going beyond his critique on Deep Learning, which is what many people know him for, Marcus puts forward a well-rounded proposal for robust AI Deep Learning on graphs sounds great, but are there any real applications? While Graph Neural Networks are used in recommendation systems at Pinterest, Alibaba, Twitter, another success story is the Transformer. Here's how they are connected. Transformers are Graph Neural Networks My engineering friends often ask me: deep learning on graphs sounds great, but are there any real applications? If you want to learn more about Graph AI, there are a few ways to do this. One is to check out these . GNNs is a relatively new field of Deep Learning, which is getting popular as it proves superior to other machine learning models that work with graph data. Here is a collection of educational resources. Learning Resources for Graph Neural Networks Another way is to join #KnowCon2020, as we have a number of sessions dedicated to Graph AI. #KnowCon2020 features some of the world's top experts on this topic, such as Michael Bronstein, Ines Chami, and Giuseppe Futia Graph AI Graph AI, the intersection of Graphs and Machine Learning, is extraordinarily promising. Knowledge Graphs can help Machine Learning systems bootstrap. Machine Learning can help build Knowledge Graphs. Hyper-relational knowledge graphs (e.g. Wikidata) enable associating additional key-value pairs with a main triple to disambiguate/restrict validity of a fact. StarE is message passing based graph encoder capable of modeling such hyper-relational KGs. Michael Galkin elaborates. Message Passing for Hyper-Relational Knowledge Graphs Traditional KGs are based on triples, whereas new KGs like Wikidata use statements and qualifiers to instantiate each edge further making the graph hyper-relational Wrapping up with Wikidata. Wikidata is an RDF Knowledge Base, and SPARQL is the RDF query language. This . SPARQL tutorial addresses people who don't have previous programming background, to enable them to use Wikidata Wikidata is a fantastic open data knowledge graph, but it's difficult to navigate and query. Navigating means clicking from one item to the next. Querying requires typing in SPARQL. provides a drill-down data exploration interface. Wikidata Walkabout Wikidata looks like a new superpower Knowledge Graph. Its structure allows for representation of richer relations, and as it is crowd authored, it enables richer information, but introduces several challenges Knowledge Graph Context in Deep Learning Approaches Encoding KG Aliases in Attentive Neural Network for Entity Linking. To get the Year of the Graph Newsletter in your inbox, . subscribe here Previously published at https://yearofthegraph.xyz/newsletter/2020/11/graphs-and-knowledge-connexions-the-year-of-the-graph-newsletter-autumn-2020/

Year of the Graph: Analytics, Databases, Knowledge Graphs and Graph AI [Autumn Newsletter]

Is there life after COVID-19? Of course there is, even though it may be quite different, and it may be hard to get there. But there’s one thing in common in the “before” and “after” pictures: science and technology as the cornerstones of modern society, for better or worse. We have argued before that Knowledge Graph is a technology that enables other technologies to accelerate their growth, and it also enables humans to take stock of their own knowledge. This is why the future is Knowledge Graph. Today’s most pressing issue seems to be dealing with the COVID-19 pandemic. Science is taking on that task, too, and technology is there to facilitate it. Knowledge Graphs facilitate scientific research and business decision making, as they have been doing, and will continue to do. Producing results to fight against the SARS-CoV-2 (coronavirus) is one of the most pressing issues today, bringing the entire scientific community together. Addressing issues related to scientific research can help produce results under pressure. Here is how prominent researchers Alexander Jarasch and Sören Auer use the state of the art in analytics and AI, graphs analytics and knowledge graphs, to facilitate scientific research for the COVID-19 pandemic. For an in-depth live discussion, join us in this Connected Data London online Meetup on April the 22nd. Graph analytics and knowledge graphs facilitate scientific research for COVID-19 State of the art in analytics and AI can help address some of the most pressing issues in scientific research. Here is how top scientists are using them to facilitate coronavirus research. There is a wide range of datasets and applications released lately centered around the coronavirus. Aaron Bradley has collected a good part of those in a Twitter thread. Add to those a couple of startup initiatives, from and Tellic Kahun Work being undertaken by the linked data/semantic web/knowledge graph world on COVID-19 Without much in way of annotation, here’s a thread enumerating work being undertaken by the knowledge graph world on COVID-19 I’ve spotted over the past couple of days Life goes on, however. Research and development, product releases, and new features never stop. Case in point – we’ve seen a few significant updates from graph database vendors in the last couple of months. Graph databases and analytics are getting ever more accessible and relevant, and vendors seem to be going through a virtuous circle of innovation. DataStax has , TigerGraph 3.0 brings , and and Neo4j for Graph Data Science. unified graph with native Cassandra data model in DSE 6.8 no code data migration and visual querying Neo4j has released a BI connector Announcing Neo4j for Graph Data Science With Neo4j for Graph Data Science, data scientists are empowered to operationalize graph analytics and machine learning models to infer behavior from connected data. Even though the Graph technology landscape is shifting rapidly all the time, it’s good to get a snapshot every once in a while. This is Janos Szendi-Varga’s 2020 update of his Graph technology landscape overview. Graph Technology Landscape 2020 Here it is, the new landscape, and I have a few thoughts about what happened in the last year, and what changes were introduced into the picture. Enjoy! Another hot topic we’ve been keeping an eye on is graph machine learning. A wealth of relevant information is out there. From an , to a roundup of . introduction to deep learning for graphs and where it may be heading knowledge graphs interwoven with AI in AAAI 2020 From , to an outlook of . making graph machine learning scalable AI, Machine Learning and the Future of Graphs Top Trends of Graph Machine Learning in 2020 Top Trends of Graph Machine Learning in 2020: More solid theoretical understanding of Graph Neural Networks; New cool GNN applications; Knowledge Graphs become more popular; New graph embedding frameworks How about some real world knowledge graph use cases? Joshua Fruhlinger built a knowledge graph of knowledge graphs, so to speak. Plus, . Walmart’s knowledge graph This map shows how the most innovative companies are using knowledge graphs What do eBay, Airbnb, Microsoft, Lending Club, and Comcast have in common? They’re all using knowledge graphs to understand their customers, business decisions, and product lines. On a different note: here is how to build your own knowledge graph, using your own data as Facebook stores them. And for some more fun distraction, here’s a . Kardashian knowledge graph Knowledge Graph Metadata: What Facebook really knows about you? Big Tech invested millions in creating knowledge graphs to power search engines, understand context of a query, meaning, user intent. Here’s how to flip the script. As knowledge graphs are attracting attention beyond their historical user base, educational resources are in high demand. Teodora Petkova has written a guide for people getting started in this domain. Crafting a Knowledge Graph: The Semantic Data Modeling Way Knowledge graphs – it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? If you’d rather attend an online class, Stanford has got you covered. A series of lectures is on offer, featuring top knowledge graph experts. It’s online, it’s free, and it’s recorded, too. CS 520 – Knowledge Graphs How should AI explicitly represent knowledge? Knowledge graphs have started to play a central role in machine learning and NLP as a method to incorporate knowledge, a target knowledge representation for extracted knowledge & #XAI If you’re more of a textbook kind of person, then this book by C. Maria Keet may be your thing. Version 1.5 of the textbook on ontology engineering is available now “Extended and Improved!” could some advertisement say of the new v1.5 of “An introduction to ontology engineering” that I made available online today. For the more hands-on group, here is , and automate the process as much as possible. how to use Wikipedia to build a knowledge graph Auto-Generated Knowledge Graphs Utilize an ensemble of web scraping bots, computational linguistics, natural language processing algorithms and graph theory. Wrapping up the knowledge graph educational section for the academically inclined. This publication has as many authors as it has angles. In its 130 pages it covers everything from history to future research directions for knowledge graphs. A propos, there is another deep dive in the history of knowledge graphs. Knowledge Graphs A comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. What about lowering the barrier to entry for software engineers? That seems to be the rationale behind accessing knowledge graphs via GraphQL, and it makes sense. Given GraphQL’s popularity, it’s not surprising that many people have had a go at this. From to , and everything in between. 15 minute hands-on tutorials long reads on the roots of GraphQL and the synergies with graph databases GraphQL and Graph Databases A discussion on the synergies between GraphQL and Graph Databases, data architecture and how ontology models provide the special ingredients. Speaking of software development: a couple of new open source frameworks have been added to a growing list. , and AtomGraph has released what it dubs “the WordPress of graph data”. Zazuko has published a tutorial on getting started with RDF & JavaScript using open source libraries Finally, a Knowledge Graph Management System As the industry is building and researching Knowledge Graphs now more than ever, we are excited to introduce you to LinkedDataHub – AtomGraph’s open-source Knowledge Graph management system. Wrapping up with a roundup of research coming from the likes of and , plus open source graph embeddings: , and as a Web API. Salesforce Stanford RDF2Vec for Python KGvec2go KGvec2go is a semantic resource consisting of RDF2Vec knowledge graph embeddings trained currently on 4 different knowledge graphs. Subscribe here for the Year of the Graph Newsletter

Year of the Graph Newsletter, April 2020: Graphs Power Scientific Research; Business Cases

Year of the Graph Newsletter, September 2019 One of the world’s top AI venues shows that using graphs to enhance machine learning, and vice versa, is what many sophisticated organizations are doing today. New developments in graph algorithms and analytics, and the latest graph database releases, many of which incorporate graph algorithms and machine learning features. Bonus Offer: To celebrate the 15th edition of the Year of the Graph newsletter: 15% discount for Connected Data London! Do you know KDD? SIGKDD’s Conference on Knowledge Discovery and Data Mining, as is its full name, is one of the most prominent venues for AI and data science. Alibaba, Amazon, Apple, Baidu, Snapchat are some of the organizations represented among the 3,5K people who were there. One thing they have in common: using Graph Neural Networks. About 20% of the 300+ papers involve graphs and knowledge-based systems. Apple, Alibaba, Amazon, and the gang promote state of the art in AI and Knowledge Discovery with Graphs As noted in Alibaba’s work, an increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationships among potentially billions of elements. Graph Neural Networks (GNN) become an effective way to address the graph learning problem. Graph-based machine learning, or GraphML, is still a relatively new area of interest. How does it fit into the world of analytics? Expero’s Graham Ganssle Graph notes that there are three paradigms of processing with respect to GraphML, which typify thousands of individual analysis types, and gives some use cases for each of these. The three paradigms of GraphML Graph analytics is a super hot area of research right now because we’re entering a world dominated by machine learning. There are many types of traditional graph analytics which don’t require machine learning. With respect to GraphML, there are three paradigms of processing which typify thousands of individual analysis types: Smart Data Extraction, Data Structure Analysis, Full Throttle GraphML. Wu Huijun from StellarGraph uses graph convolutional networks to look behind the curtain. Saliency mapping is a technique with origins in computer vision literature used to change or simplify an image into something that has meaning for humans, making it easier to analyse. Huijun uses saliency maps in graph machine learning. Saliency maps for graph machine learning Using node classification with graph convolutional networks (GCN) as a case study, we’ll look at how to measure the importance of specific nodes and edges of a graph in the model’s predictions. This will involve exploring the use of saliency maps to look at whether the model’s prediction will change if we remove or add a certain edge, or change node features. As noted when discussing GraphML, there are many types of traditional graph analytics which don’t require machine learning. Here are the 5 graph algorithms Data Scientists should know, because as per Walmart’s Rahul Agarwal, Graph Analytics is the future. Data Scientists, 5 Graph Algorithms that you should know In a connected world, users cannot be considered as independent entities. They have relationships, and we would sometimes like to include such relationships while building our machine learning models. In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python. As graph analytics and algorithms are gaining steam, executing them fast and at scale becomes increasingly important. A new open-source library by NVIDIA could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on NVIDIA GPUs. The effort is led by graph analytics veteran Brad Rees. Nvidia Rapids cuGraph: Making graph analysis ubiquitous With the release of version 0.9, NVIDIA cuGraph is coming one step closer to 1.0. As Rees explained, the goal is not just to keep adding algorithms to cuGraph, but to make them work over multiple GPUs, too. This has now been achieved for PageRank. Even in version 0.6, however, cuGraph was already up to 2000 times faster than NetworkX. Would you like to catch up with Brad Rees, presenting an overview of RAPIDS and cuGraph, discussing and showing examples of how to manipulate and analyze graphs, and how data can be shared with machine learning algorithms? Would you also like to join graph database experts George Anadiotis from ZDNet, Joshua Shinavier from Uber, Steven Sarsfield from Cambridge Semantics, and Geoffrey Horrell from Refinitiv discussing why, and how, graph databases will rule the world in the 2020? For these, and countless others top of the line discussions and presentations, Connected Data London is the place to be. To celebrate the 15th issue of the Year of the Graph newsletter, use code CDL2019YOG15 for 15% discount, to see as many of you as possible there. And remember, with bulk and combo discounts in effect, you could save as much as 30%. Graph Databases Will Rule the World in the 2020s. But Why, and How? If you’re out to shop for a graph database, you will soon realize that there are no universally supported standards, performance evaluation is a dark art, and the vendor space seems to be expanding by the minute. So, what’s all the fuss about? What are some of the things graph databases are being used for, what are they good at, and what are they not so good at? Dan McCreary from Optum goes through the Data Lake and Data Hub patterns, to emphasize what Knowledge Graphs can do for the enterprise. McCreary discusses how Graph Convolutional Neural Networks (GCNs) leverage graph structure to find deep insights even with small training sets. He also touches upon advances in hardware, and how they are related to this. From Lakes to Hubs to Graph We need to build a community of AI researchers educated in what graph algorithms can do at scale & how deep learning algorithms can reinforce graph algorithms to build advanced HTAP solutions. We need to make hardware vendors understand the needs of the scaleable graph algorithm community. We need support for high-level declarative graph languages that perform queries over distributed native graph databases. Denise Gosnell from DataStax spent time talking to dozens of graph users from around the world. Across those conversations, she notes, there has been one common theme: Engineering teams use graph technology because it makes them more productive due to: 1) Easier code maintenance. 2) Ease of human understanding. 3) Graph data helps come up with new features for predictive models. Graph technology makes teams more productive At some point, a researcher isn’t going to be able to improve the accuracy level of their model any further. By augmenting their feature sets with graph features, like path distance, teams were able to unlock new gains in their model’s accuracy. Machine learning teams model and extract graph features to enhance the accuracy of their predictive models. Exploring how the Google Knowledge Graph works can provide some insights into how is growing and improving and may influence what we see on the web, writes Bill Slawski from SEO by the Sea. Slawski writes about a newly granted Google patent from the end of July, which tells us about one way that Google is using to improve the amount of data that the Google Knowledge Graph contains. Google knowledge graph reconciliation Reconciling entities means “providing computers with unambiguous identifications of the entities we talk about.” This patent from Google focuses upon a broader use of the word “Reconciliation” and how it applies to knowledge graphs, to make sure that those take advantage of all of the information from web sources that may be entered into those about entities. We recently saw how Pinterest introduced Knowledge Graphs in its stack to enhance its existing taxonomies. Pinterest engineers elaborate on the Pin2Interest system for content classification and their Adventures in big data wonderland Going down the Pinterest Path. Adventures in big data wonderland going down the Pinterest path Pinterest is a discovery engine that connects ideas across a taste graph, so for every Pin on Pinterest, there are Related Pins (Pins that are visually and semantically similar to that Pin), which we are always working to keep fresh. When thinking about how to visualize and construct these complex Pinterest Paths, it can be useful to think of a Pinterest Path as a graph. Taxonomy is easy, says Bob Kasenchak from Synaptica. That’s a good opener, which lets Kasenchak show some of the issues with building taxonomies (and by extension, hierarchical data models) which emphasize the need for knowledge graphs. Naming the Edges, or: Why Knowledge Graphs? And so we come to the reason we’re hearing about the “rise of knowledge graphs” in recent books and articles and at conferences: it’s solving a problem in vocabulary architecture that’s becoming increasingly important as the foundation of AI and other technologies (not to mention search). Another data modeler, Thomas Frisendal, another look at how graphs can facilitate data modeling. Frisendal goes on a quest for defining (parts of) the future history of temporal data modeling, starting with SQL, including the OWL ontological data model, and property graphs. The future history of time in data models Temporal dependencies quickly explode into a highly connected network, which best can be handled by a graph DBMS. Complexity must be hidden for both data modelers and business users by way of some higher-level concepts. I wrote this post to provoke vendors into considering this architectural sketch for their future product development. SHACL is a W3C standard to validate RDF, which many people find more usable than the rather heavyweight OWL. Here Emidio Stani shows how to design reusable SHACL shapes and implement a linked data validation pipeline. Design reusable SHACL shapes and implement a linked data validation pipeline In July 2017, W3C published SHACL as the standard to validate RDF. Since then, data modellers have the possibility to provide validation services based on SHACL shapes together with their models, however there are considerations to be taken in account when creating them. If you are a Neo4j user, and want to able to import / export RDF data, Jesus Barrasa from Neo4j has just released a new version of his NeoSemantix plugin. Importing RDF into Neo4j, serializing your Neo4j graph as RDF (JSON-LD, Turtle, N-Triples, etc), Quads/Named graphs, Mappings, Microinferences, more new features and a user manual. NEOSEMANTIX NSMNTX is a plugin that enables the use of RDF in Neo4j. RDF is a W3C standard model for data interchange. This effectively means that NSMNTX makes it possible to Store RDF data in Neo4j in a lossless manner, on-demand export property graph data from Neo4j as RDF. Other features in NSMNTX include model mapping and inferencing on Neo4j graphs. There’s been a few graph database releases in the last month. Before we get to those, however, a new release for Apache Tinkerpop – 3.4.1 . TinkerPop is a graph processing framework which includes Gremlin, a widely supported graph query language. Kelvin Lawrence from AWS notes that Amazon Neptune now supports TinkerPop 3.4, and explains how to use the new features in Neptune. Amazon Neptune now supports Tinkerpop 3.4 features Examples of new features in the Gremlin query and traversal language such as text predicates, changes to valueMap, nested repeat steps, named repeat steps, non-numerical comparisons, and changes to the order step. It is worth pointing out that TinkerPop 3.4 has a few important differences from TinkerPop 3.3. Be sure to review the compatibility notes in the engine releases documentation. After raising Series A funding and reaching 10K stars on Github, open source graph database Dgraph releases v.1.1 . New features: new type system, upsert blocks, binary backups, access control lists. Plus performance & bug fixes, and a few breaking changes. Releasing Dgraph v1.1.0 Dgraph’s new release v1.1.0 is here. The new version ships with a plethora of significant changes and new features. In this post, we will cover the most important ones, but you can find all the details in the changelog. Multi-model, open source database with support for graph ArangoDB released version 3.5 released. New features include distributed joins, streaming transactions, extended graph & search capabilities. Multi-model database Arangodb 3.5 released The new k-shortest path feature provides the option to query for all shortest paths between two given vertices, returning sorted results based on path length or path weight. Imagine you transferred a transportation network to a graph dataset and now navigate between two given points. You can query for shortest travel distance, shortest travel time, or any other information you have stored on edges. Wrapping up with graph database updates, a benchmark and its rebuttal. TigerGraph released a benchmark comparing its product against Neo4j. Max DeMarzi from Neo4j had some objections to the methodology and the results published . As we always say, benchmarks are a nuanced topic, on which you can hardly ever expect competing vendors to agree – you have to do your own research. In-Depth Benchmarking of Graph Database Systems with the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB) In this study, we present the first results of a complete implementation of the LDBC SNB benchmark — interactive short, interactive complex, and business intelligence — in two native graph database systems — Neo4j and TigerGraph. Siren is not a a graph database, but its claim to fame is making your existing data look like a knowledge graph, without actually being in a graph database. Siren released v.10.3, with what it calls five new AI superpowers. Siren 10.3 is out with five new AI superpowers Deep learning-based predictive analytics and alerting (Siren ML). Deep learning-based time series anomaly detection. Unstructured data discovery with real-time topic clustering. Associative in-dashboard Relational Technology (“Dashboard 360”). State-of-the-art, self-correcting entity resolution (Siren ER). 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