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

Earlier this month I asked here on LinkedIn about interest publishing a short piece on Knowledge Graphs. The response was overwhelmingly positive, so today...

Learn more about content hubs and how to build them by leveraging deep learning and data in a knowledge graph and using a specific technique called knowledge graph embeddings (or simply KGE).

Your regular digest of KG research, November edition

Papers With Code highlights trending Machine Learning research and the code to implement it.

The number of users approaching the world of cryptocurrencies exploded in the last years, and consequently the daily interactions on their underlying distributed ledgers have intensified. In this...

My keynote at CIKM 2021 workshop on Knowledge Injection in Neural Networks--about uses/misuses of #LLMs, importance of explicit knowledge, and our ongoing...

Classical event logs have the fundamental shortcoming of describing process behavior only in isolated process executions from the viewpoint of a single case entity. Most real-life processes involve…

Taxonomies are useful for managing and analyzing data, and not just content.

Our work on GNNs for inductive link prediction got the best paper award at International Semantic Web Conference 2021! Wouldn't be possible without the...

We know a lot more about how AlphaFold 2 works, but the mystery of why proteins fold the way they do remains something of a mystery.

Amazon.com: Knowledge Graphs and Big Data Processing (Lecture Notes in Computer Science Book 12072) eBook : Janev, Valentina, Graux, Damien, Jabeen, Hajira, Sallinger, Emanuel: Kindle Store

We present principled approaches to train and deploy dyadic neural embedding models at the billion scale, focusing our investigation on the application of semantic product search. When training a...

This particular article is a discussion about a recommendation to a given standard, that of SPARQL 1.1. None of this has been implemented yet, and as such rep…

User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entities of a graph and...

GraphDB 9.10 features smart updates via SPARQL templates and Kafka as well as graph-path search optimizations