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In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated summaries of scholarly papers. [3] The Semantic Scholar team is actively researching the use of artificial intelligence in natural language processing , machine learning , human–computer ...
Provides many innovative ways to explore scientific papers, conferences, journals, and authors [104] Free Microsoft: Microsoft Academic Knowledge Graph: Multidisciplinary Provides an RDF data set about scientific publications and related entities, such as authors, institutions, journals, and fields of study.
In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.
A knowledge graph is a knowledge base that uses a graph-structured data model. Common applications are for gathering lightly-structured associations between topic-specific knowledge in a range of disciplines, which each have their own more detailed data shapes and schemas .
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
The Knowledge Graph was powered in part by Freebase. [7] In August 2014, New Scientist reported that Google had launched a Knowledge Vault project. [14] After publication, Google reached out to Search Engine Land to explain that Knowledge Vault was a research report, not an active Google service.
The research was funded by the Chinese National High-tech R&D Program and the National Science Foundation of China. AMiner is commonly used in academia to identify relationships between and draw statistical correlations about research and researchers. It has attracted more than 10 million independent IP accesses from 220 countries and regions.