GraphNews

In-database training avoids exporting the data from the DBMS to other machine learning platforms thus better support continuous model…

A frequent source of confusion with ontologies and more generally with any kind of information system is the Open World Assumption. This trips up novice inexperienced users, but as I will argue in …

In Deliberate Internet 27 I focus on the new wave of note-taking apps and explain why they are all sprouting now.

Don t Quarantine Your Research you keep your social distance and we provide you a social DISCOUNT use QUARANTINEDAYS Code in precise requirement and Get FLAT 1000USD OFF on all CMI reports The Knowledge Graph can be defined as the ...

RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by...

Discover the endless possibilities of the NSMNTX plugin on RDF data, linked data and more with Neo4j Engineering Director Jesús Barrasa.

Current action recognition systems require large amounts of training data for recognizing an action. Recent works have explored the paradigm of zero-shot and few-shot learning to learn classifiers...

In graph embedding, the connectivity information of a graph is used to represent each vertex as a point in a d-dimensional space. Unlike the original, irregular structural information, such a...

Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many...

spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data. However, most...

Everything you need to know to start working with GNN

A nice, easy way to enrich your spatial data with features from Graph Theory which capture information that is hard to encode otherwise.

Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes and edges. This...

We present CoDEx, a set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of...

Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new bi...

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts...

Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF...

In a RuleML technical memo; Graph-Relational Data, Ontology, Rules ; Harold Boley points out that ...

Building on recent work that identified human host proteins that interact with SARS-CoV-2 viral proteins in the context of an affinity-purification mass spectrometry screen, we use a machine learning-based approach to connect the viral proteins to relevant biological functions and diseases in a large-scale knowledge graph derived from the biomedical literature. Our aim is to explore how SARS-CoV-2 could interfere with various host cell functions, and also to identify additional drug targets amongst the host genes that could potentially be modulated against COVID-19. Results are presented in...

An earlier article, Fact-Based AI — Improving on a Knowledge Graph, I provided a vision for Fact-Based Modelling’s future in AI while…

Check out this interview with Charlie Beveridge from Accenture on navigating the future of artificial intelligence with knowledge graphs.

Elena Williams https://2020.pycon.org.au/program/A878CA Relational and NoSQL DBs have ruled the roost for a couple of decades now, but in real life there's more to data than just tables or key-pairs. Graph DBMS technology has been coming along for the last decade-or-so and is now quite mature. Everyone wants one, just ask a Fortune 500 company. I mean: why have a table when you can have a knowledge graph? Why not be able to whip up a recommendations engine (or indeed a fraud detector) in a few minutes? Graph databases store data in Graphs -- that is NOT chart-visualisation nor syntax standa...

"What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus" identifying “wiki-worthy” terms in a massive news corpus, with minimal dependency on actual Wikipedia entries.(Bilu et al, 2020)https://t.co/wEts0vt9tl pic.twitter.com/7Zv3AnPZXt— WikiResearch (@WikiResearch) September 18, 2020

AI bots do marvelous things such as facial recognition, document analysis, and creating false videos of world leaders singing pop songs. AI bots, however, are only as smart as they are programmed. …