A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)(About) from the abstract: Previous work in information retrieval have shown that using evidence, such as concepts and relations, from external knowledge sources could enhance the retrieval performance... This paper presents a new tri-partite neural document language framework that leverages explicit knowledge to jointly constrain word, concept, and document learning representations to tackle a number of issues including polysemy and granularity mismatch.
Combining word and entity embeddings for entity linking (ESWC 2017)(About) The general approach for the entity linking task is to generate, for a given mention, a set of candidate entities from the base and, in a second step, determine which is the best
one. This paper proposes a novel method for the second step which is
based on the **joint learning of embeddings for the words in the text and
the entities in the knowledge base**.
ESWC2008 Conference Data(About) This page provides an overview about different access mechanisms to the RDF dataset about the 5th European Semantic Web Conference (ESWC2008) and explains how the dataset can be used within different Semantic Web applications.