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To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like...

Background. Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as...

.@opencitations & its datasets, tools, services, activities: #DataModel; @sparontologies (Semantic Publishing & Referencing) #ontologies; open software for searching, browsing and providing REST APIs over RDF #graphDBs; and more #OpenScience #OA https://arxiv.org/abs/1906.11964

Pinterest is a popular Web application that has over 250 million active users. It is a visual discovery engine for finding ideas for recipes, fashion, weddings, home decoration, and much more. In...

Roles are sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities are sets of nodes with more connections inside the set than outside...

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the...

Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase)....

#Knowledgegraphs have proven to be effective for high-quality recommendation. This #research designs a novel attentive knowledge graph embedding (AKGE) framework for recommendation, which exploits both semantics & topology in interaction-specific manner

Reasoning over paths in large scale #knowledgegraphs has many applications. @healx #research on approach to automatically build & rank paths between source & target entity pair with learned embeddings using a knowledge base completion model #EmergingTech

Same Stats, Different Graphs: Exploring the Space of Graphs in Terms of Graph Properties. #Data analysts use statistics to summarize large datasets, but it can be misleading. #Research considers similar problem in graph mining #knowledgegraph #analytics http://arxiv.org/abs/1911.01527

Literature review is complex, requires expert analysis of unstructured #data. #Automation of process presents opportunity for high throughput knowledge extraction & meta analysis. Gextext extracts #knowledgegraph of relationships from text #research #NLP

#Semantic Scholar Graph of References in Context: a large contextual citation graph of 81.1M academic publications, including parsed full text for 8.1M open access papers, across broad #science domains #research #knowledgegraph #openscience #AI @allen_ai

Commonsense #knowledgegraphs are rich repositories that encode how the world is structured. Scene graphs are powerful representations that encode images into their abstract #semantic elements. #Research presents a unified formulation #AI #EmergingTech

An accurate representation of knowledge is essential for teaching systems. This #research gives insight into different knowledge representation techniques and their categorization into various problem domains in #AI #taxonomy #applications h/t @aaranged https://arxiv.org/abs/2001.04835

mannheim.de/41307/1/Ristoski_RDF2Vec.pdf ​ If you have any questions, problems running the code, or an interesting use case. Get in touch with us! submitted by

This Stardog Training teaches you the basics of the RDF data model and best practices for ontology development. To learn more, visit stardog.com

A "definitive feature of knowledge graphs is the ability to connect concepts to external resources using Linked Data". ✔️ @taxobob bit.ly/2SDis4h

This article, based on a comprehensive history and definitions of the concept, provides a common-sense view of how to understand knowledge graphs.

A story about graph databases and how you can leverage the tech to turn companies and products around.

a general list of terms and phrases used frequently in the Semantics and Property Graph space. It is far from comprehensive, but if you've ever read my or other writers' articles, you may have run across terms that were unfamiliar. I'm publishing this here, with the intent of periodically updating it as time and resources permit.

Lots of researchers studied how can GNN works. TransE (Border et al., 2013), RESCAL (Nickle et al., 2011), DistMult (Yang et al., 2015) and ComplEx (Trouillon et al., 2016) will be introduced in this section.TransEIf you are familiar with word2vec (Mikolov et al., 2013), you can assume that TransE (Border et al., 2013) is similar to word2vec. Giving subject entity (aka head), relation and object entity (aka tail), object entity embeddings should be close to the subject entity embeddings plus relation embeddings if the subject entity is similar to the object entity. Otherwise, the subject entity should be far away from the object entity.Word2vec Sample: King + Woman ~= Queen (source)RESCALRESCAL (Nickle et al., 2011) uses multiple matrics to represent the relations among entities. Assume that the total number of entity is n while the total number of a relation is m, the total number of parameters is n x n x m. If there is no relation between an entity i and entity j, the value is

Presentation for Data Day Texas / Global Graph Summit 2019

SciBite CSO and Founder Lee Harland shares his views on the use of BERT (or more specifically BioBERT) for deep learning approaches.

Read this interview with Ivan Zoratti – the former Chief Technology Officer of MariaDB – and learn why he joined Neo4j as the Director of Product Management.

The United States is in the midst of an opioid epidemic with recent estimates indicating that more than 130 people die every day due to drug overdose. The over-prescription and addiction to opioid...

#data stored in graphs mimic the way humans understand information. @ontotext built a Star Wars #Knowledgegraph using RDF. But…what do #software developers want? Many APIs are built using #GraphQL #JSON. “A query language for your API” #GraphDB #database

Today we want to make this available in a first release under an Apache License for you to try out and test. It works with Neo4j from 3.4.x

A Practical Guide to Build an Enterprise #KnowledgeGraph for Investment Analysis. 1. Schema Design 2. D2R Transformation 3. Information Extraction 4. Data Fusion with Instance Matching 5. Storage Design and Query Optimization

A refresher on the Google Knowledge Graph and how you can find facts about people, places and things in knowledge panels on Search.