Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, labels, and relations present in the knowledge graph. Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches. We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods.
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In the last decade, we experienced an urgent need for a flexible, context-sensitive, fine-grained, and machine-actionable representation of scholarly knowledge and corresponding infrastructures for knowledge curation, publishing and processing. Such technical infrastructures are becoming increasingly popular in representing scholarly knowledge as structured, interlinked, and semantically rich Scientific Knowledge Graphs (SKG). Knowledge graphs are large networks of entities and relationships, usually expressed in W3C standards such as OWL and RDF. SKGs focus on the scholarly domain and describe the actors (e.g., authors, organizations), the documents (e.g., publications, patents), and the research knowledge (e.g., research topics, tasks, technologies) in this space as well as their reciprocal relationships. These resources provide substantial benefits to researchers, companies, and policymakers by powering several data-driven services for navigating, analysing, and making sense of research dynamics.
It's been ten years since Google (now a child of holding company Alphabet) coined the term "knowledge graph" and described (in general terms) how their knowledge graph worked. And it's been over 20 years since Tim Berners-Lee, James Hendler and Ora Lassila published their first article to describe the semantic web they envisioned. Many knowledge graphs have been built using the semantic standards the W3C subsequently put in motion a decade or more ago. It's interesting to ponder what's happened since. Over the past decade, Alphabet has grown consistently to become one of the top six companies globally to achieve a market capitalization (total stock value of shares outstanding) of over $1 trillion.