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

Software development organizations are seeking to add quality to their products. Testing processes are strategic elements to manage projects and product quality. However, advances in technology and the emergence of increasingly critical applications make testing a complex task and large volumes of information are generated. In fact, software testing is a knowledge intensive process. Because of this, these organizations have shown a growing interest in Knowledge Management (KM) programs, which in turn support the improvement of testing procedures. KM emerges as a means to manage testing knowledge, and, consequently, to improve software quality. However, there are only a few KM solutions supporting software testing. This paper reports experiences from the development of an approach, called Ontology-based Testing Knowledge Management (OntoT-KM), that aims to assist in launching KM initiatives in the software testing domain with the support of Knowledge Management Systems (KMSs).  OntoT-KM provides a process guiding how to start applying KM in software testing. OntoT-KM is based on the findings of a systematic mapping on KM in software testing and the results of a survey with testing practitioners. Moreover, OntoT-KM considers the conceptualization established by a Reference Ontology on Software Testing (ROoST). As a proof of concept, OntoT-KM was applied to develop a KMS called Testing KM Portal (TKMP), which was evaluated in terms of usefulness, usability, and functional correctness. Results show that the developed KMS from OntoT-KM is a potential system for managing knowledge in software testing, so, the approach can guide KM initiatives in software testing.

A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with edge types. KGs are an important primitive in modern machine...

This article, the first in a series, introduces and defines the concept of a knowledge graph. It highlights its origins and basic characteristics.

An increasing number of cities are creating 3D city models to support visualization and simulations in the urban planning process. The 3D city models...

For some reason I keep hearing the word "upper ontology" more frequently in recent times. I find this problematic, because in many of the contexts I hear about it, it is introduced as a kind of secret sauce that 20 years of Semantic Web research have been hiding from the greater public.

And if you do there's a simple way to prove it.

We believe that the future for cooperation in the construction industry is more based on data and not so much on files.

We are excited to announce that we are running an online panel this November where will explore the foundations of why graphs are the right tool for complexity...

Mike Tung on the problems with search and the future of knowledge representation