Knowledge Graphs: Construction, Querying and Management

A Knowledge Graph (KG) is a graph-theoretic knowledge representation that (at its simplest) models entities and attribute values as nodes, and relationships and attributes as labeled, directed edges. Knowledge Graphs have emerged as a unifying technology in several areas of AI, including Natural Language Processing and Semantic Web, and for this reason, the scope of what constitutes a KG has continued to broaden. In industry, widespread adoption of schema.org, as well as the Google Knowledge Graph, is changing the way information is being produced and consumed by both humans and machine agents on the Web. Even before the term ‘Knowledge Graph’ was coined and was in use, the Semantic Web community was a strong advocate of many of the core elements that make KGs so powerful, including graph-theoretic data models (and more generally, semi-structured representations of both data and schema), powerful pattern-matching querying languages, graph data management and the emergence and utilization of large publicly available KGs like DBpedia, GeoNames and Wikidata for such varied tasks as knowledge acquisition, information retrieval and knowledge alignment. With the renaissance of, and deep interest in, such technologies in the broader computer science community, we believe that the time is ripe for the Semantic Web to revisit Knowledge Graphs from the lens of construction, management and querying.