Stardog joins the Enterprise Knowledge Graph Foundation - Stardog
We’re proud to announce that Stardog is joining the Enterprise Knowledge Graph Foundation as a founding vendor member. Read on if you want to learn who, what, and why.
TigerGraph Unveils Free TigerGraph Enterprise Edition, Helping Companies Use Graph as the Foundation of Many Modern Data, Analytics and AI Capabilities
2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020
While five new #AI solutions enter the Gartner Hype Cycle for AI, 2020 what trends are dominating this year’s #AI landscape? Read Gartner analyst Svetlana Sicular’s views here. #GartnerSYM #CIO #ML #Chatbot
Converting text documents into knowledge graphs with the Diffbot Natural Language API
Most of the world’s knowledge is encoded in natural language (e.g., news articles, books, emails, academic papers). It is estimated that 80 percent of business-relevant information originates in un…
Happy to announce that my @OReillyMedia book Semantic Modeling for Data is now published https://t.co/4yngwDPMrO and available in electronic and print format https://t.co/VsFc8zf2KY. Get a free sample chapter at https://t.co/DivwADNUGo #datascience #datamodeling #knowledgegraphs pic.twitter.com/9j58IF1lcZ— Panos Alexopoulos (@PAlexop) September 9, 2020
Wikidata and Wikipedia have been proven useful for reason-ing in natural language applications, like question answering or entitylinking. Yet, no existing work has studied the potential of...
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 …
Semantic Knowledge Graphing Market Analysis and Forecast 2020: By Keyplayers Google Inc., metaphacts GmbH, Stardog Union, Grakn Labs, Microsoft Corporation, LinkedIn, Semantic Web Company, Baidu, Yandex, Wolfram Alpha, and Ontotext.
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 ...
More is not Always Better: The Negative Impact of A-box...
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...
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...
Architectural Implications of Graph Neural Networks
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...