GraphNews

A provoking claim As a software anarchitect, I like to challenge the status quo: I propose to use RDF and SPARQL for the core domain logic of business applications. Many business applications consist of simple workflows to process rich information. My claim is that RDF and SPARQL are ideal to model and process such information while a workflow engine can orchestrate the processing steps. Cheap philosophy Algebraic data types are concrete structures capable of representing information explicitly and are becoming popular for domain modeling. But also a logical framework like RDF shines at rep...

Large amounts of threat intelligence information about mal-ware attacks are available in disparate, typically unstructured, formats. Knowledge graphs can capture this information and its context...

Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources. Comprehending them and utilizing them for threat intelligence extraction requires...

Recommender systems are an essential component of e-commerce marketplaces, helping consumers navigate massive amounts of inventory and find what they need or love. In this paper, we present an...

"OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text" - Bidirectional LSTMs trained on a large #DBpedia and #Wikipedia corpuses to enrich information security ontologies.(Mohan et al, 2021)https://t.co/5ZTYWrDSnu pic.twitter.com/z5XeiaTAf0— WikiResearch (@WikiResearch) February 15, 2021

Demo of COMeT, a knowledge base construction engine that learns to produce new nodes and connections in commonsense knowledge graphs, on ATOMIC and ConceptNet.

"Digital Twins Definition Language is an open modeling language based on JSON-LD and RDF, by which developers can define the schema of the entities they expect to use in their graphs or topologies." This is an "open-source DTDL-based ontology .. for the real estate industry" https://t.co/icnwlMB5qy pic.twitter.com/at2kos9CDK— Aaron Bradley (@aaranged) February 16, 2021

Contextual advertising provides advertisers with the opportunity to target the context which is most relevant to their ads. However, its power cannot be fully utilized unless we can target the...

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback. These systems...

Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link prediction and...

This blog post will give you an overview of what we have developed in customer projects over the years as our game plan to build a Knowledge Graph-driven, FAIR Data platform and drive digital transformation with data. The post will show you how our product metaphactory can support you every step of the way, and will highlight examples from the life sciences and pharma domains.

Introducing Wiki Topic Grapher! 👾🐍🔥Leverage the power of Google #NLP to retrieve entity relationships from Wikipedia URLs or topics! + Get interactive graphs of connected entities+ Export results w/ ent. types+salience to CSV!▶️https://t.co/9M0zaMNIX8h/t @Streamlit 🧵 pic.twitter.com/ok9M3ypQgr— Charly Wargnier (@DataChaz) February 19, 2021

We applaud today’s decision by the American Chemical Society to endorse @DORAssessment and to make citation data for all their journals openly available. One more major publisher supporting the vision of unrestricted access to scholarly citation data 🚀 https://t.co/VNMwefVEbX— I4OC (@i4oc_org) February 18, 2021

Our #Neo4j sandbox infrastructure got a big update. And with this there are all new versions of Neo4j, APOC, Graph Data Science, Bloom and Neosemantics available for you to learn and explore. Enjoy the new sandboxes and let us know what you think.https://t.co/HVLpAtVQ2y— Neo4j (@neo4j) February 19, 2021

@neo4j's @maya_natarajan discusses "Why #KnowledgeGraphs now?" #BBBT pic.twitter.com/DsRSKL97O1— Adrianna Dyczkowsky (@ADyczkowsky) February 19, 2021

Check out Marinella Testori's brand new post featuring Carriero et al.'s preprint: ARCO: The Italian Cultural Heritage Knowledge Graphhttps://t.co/qALUUZgtBe#LinkedOpenData #DigitalHumanities #KnowledgeGraphs pic.twitter.com/UQNSXGAnc2— OpenMethods (@openmethods_dh) March 12, 2021

If you're interested in GNNs for combinatorial tasks (certainly an exciting time!), we've released our 43-page comprehensive survey on the area! + detailed blueprint of algorithmic reasoning in S3.3.https://t.co/F4TG4svKMGwith @chrsmrrs @69alodi @lyeskhalil @qcappart & Didier pic.twitter.com/P6TANTgLvr— Petar Veličković (@PetarV_93) February 19, 2021

Hi everyone, Our video this week is an interview with NASA’s David Meza from Ashleigh Faith’s IsADataThing YouTube channel. Jesús Barrasa analyses the structured data of the White House website, Clair Sullivan imports data from Python, and I show how… Read more →

THREAD - how you can do crazy cool things with @Neo4j and Graph Data Science (GDS). This thread is going to be a walk-through example of what you can do, and how you can even found a startup on the back of this stuff.First, some background on my data: pic.twitter.com/aT6enmV5SC— 𝔻𝕒𝕧𝕚𝕕 𝔸𝕝𝕝𝕖𝕟 (@mdavidallen) February 23, 2021

We’re thrilled to share a proposal to extend the work of the #WikiCite community, to support Wikimedia Projects and make citations: • easier for the editor,• more useful for the reader,• and more efficient for our architecture.#SharedCitationshttps://t.co/pqVwQLApNa 1/ pic.twitter.com/oALIPg7JTD— WikiCite (@Wikicite) February 22, 2021

Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or...

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated...

Graph theory has a long history of problems being solved by amateur mathematicians. Do you want to try yourself to become one of them?

Library for deep learning on graphs

This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In...

TODO

The code property graph (CPG) is a data structure designed to mine large codebases for instances of programming patterns via a domain-specific query language. It was first introduced in the proceedings of the IEEE Security and Privacy conference in 2014 (publication, PDF) in the context of vulnerability discovery in C system code and the Linux kernel in particular.

Selected chapters from my book, A Knowledge Representation Practionary, are now available for free download.