TL;DR
The paper introduces the Semantic Knowledge Graph, a compact, auto-generated model that dynamically materializes and scores relationships between entities within a domain, enabling real-time traversal and analysis.
Contribution
It presents a novel knowledge representation system that automatically constructs and dynamically materializes a semantic graph from data, facilitating real-time relationship discovery.
Findings
Enables dynamic discovery of relationships between entities
Provides a compact and scalable graph representation
Supports real-time traversal and scoring of relationships
Abstract
This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination…
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