Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships...

Can Machine Learning Do Symbolic Manipulation?  I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?

How and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. By Leo Meyerovich and Ben Lorica. [A version of this post originally appeared on the Graphistry blog.] In this post, we highlight the current state of Graph Intelligence, a new technology category around new tools and techniques forContinue reading "What is Graph Intelligence?"

Artificial Intelligence has a long history of oscillating between two somewhat contradictory poles. On one side, exemplified by Noam Chomsky, Marvin Minsky, Seymour Papert, and many others, is the idea that cognitive intelligence was algorithmic in nature – that there were a set of fundamental precepts that formed the foundation of language, and by extension,… Read More »Where Semantics and Machine Learning Converge

In 2021, knowledge graphs weren't just blossoming-they were blooming-and it was clear that we are now in the knowledge graph stage of KM development

Enterprise Knowledge Graph Platform ROI: Providing readers with a framework to evaluate the potential financial impact of deploying Stardog on their own organizations.

Maya Natarajan, senior director of product marketing at Neo4j, explores the value of knowledge graphs for CIOs

For this blog, Ahren Lehnert, explores knowledge graphs and their punk rhizomatics. A paradigm shift in knowledge organization and sharing.

Interested in using Wikidata & Wiktionary to extract lexicographical information?@sina_ahm created a simple SPARQL query generator that helps non-experts get familiar w/ #SPARQL & create queries to look up words in @wikidata & #Dbnary:👉 https://t.co/gS6JG8EIFb#NLProc pic.twitter.com/9UOCABh3c9— ELEXIS EU (@elexis_eu) December 1, 2021

Curious about the most used ontologies in the agri-food domain? Our #Ontologies Community of Practice has developed a webpage featuring the most popular ontologies & info on those developed by @CGIAR and partners.Find it here: https://t.co/AMVeSHCpdi pic.twitter.com/LmazTo4V2T— CGIAR Platform for Big Data in Agriculture (@CGIAR_Data) December 9, 2021

"... most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality" > Survey on English Entity Linking on Wikidata https://t.co/JYHJMfVHsO pic.twitter.com/hRxRp3nhnQ— Aaron Bradley (@aaranged) December 10, 2021

Using custom Named Entity Recognition and Relation Extraction models

TempoQR: Temporal Question Reasoning over Knowledge Graphs https://t.co/6SXVnzyG1x pic.twitter.com/LXQW2XYbUb— Aaron Bradley (@aaranged) December 14, 2021

Predictive UX explores UX Design for Enterprise Search Using Knowledge Graphs

Use our SPARQL micro-services to query #PubMedCentral with #SPARQL using a PMID or PMCID, and get article metadata in #RDF.https://t.co/1RKvb9GZkvhttps://t.co/XxjTUy2Gut@pubmed @wimmics @Inria @uca_research @Laboratoire_I3S #ScientificLitterature pic.twitter.com/bm8OdpzoaN— Michel Franck (@franck_michel2) December 7, 2021

Check out how knowledge graphs have transformed the supply chain industry in the continued wake of the pandemic.

Manufacturing Line, Wikimedia Commons, 2014 Fakery certainly isn’t limited to news and social media. The Organisation for Economic Co-operation and Developmen…

Over the last 20 years my life revolved around doing “everything modeling” - building models, discussing modeling, teaching modeling and so on. Unsurprisingly, I have an opinion about models and modeling.

In the previous article on how data modeling is different today, I talked about modularity and expressivity required for defining shared models. Such concepts are often talked about in abstract terms.

To combat antibiotic resistance, Roland Haas uses a knowledge graph to offer patients the right antibiotic for the right diagnosis at the right dosage.

How knowledge graphs model the relationships within scientific data in an open and machine-understandable format for better science.

Designing Web APIs for sustainable interactions within decentralized knowledge graph ecosystems ◆ Web services emerged in the late 1990s as a way to access specific pieces of remote functionality, building on the standards-driven stability brought by the universal protocol that HTTP was readily becoming. Interestingly, the Web itself has drastically changed since…

Knowledge graphs are well-suited to organizations with large data sets and where extracting knowledge often proves burdensome.

Implementation of information extraction pipeline that includes coreference resolution, entity linking, and relationship extraction…

Traversing the Tree of Life, using Neo4j

A blog about my work and fun with Neo4j, the world's leading graph database platform.

How knowledge graph technologies can help clinical natural language processing

Since the launch of its genesis network in January 2019, the OriginTrail project has yielded a crucial advancement for the foundation of…

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same...

Why enterprise ontology management matters to the EKG industry