Graph Theory and #DataScience: A topic intro with the Bridges of Königsberg. Graph Theory can be used to represent and analyze a wide variety of network information, has numerous modern applications within Data Science #data #tech #software #algorithms

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Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem...

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. The...

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete...

This paper presents the KG Usage framework, which allows the introduction of KG features to support Trust, Privacy and Transparency concerns regarding the use of its contents by applications. A...

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...

#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

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

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.

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.

A quick summary of my(and many other) talks at KGC-2019

This page describes our functional and technical approach to constructing schema.org markup.