The Semantic Knowledge Graph: A Compact, Auto-Generated Model for Real-Time Traversal and Ranking of any Relationship within a Domain

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 of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain. The source code for our Semantic Knowledge Graph implementation is being published along with this paper to facilitate further research and extensions of this work.

I. Introduction

Graphs are a well-studied class of data structures used to model relationships (edges) between entities (nodes). Knowledge bases in general, and ontologies specifically, model a domain by defining how different entities within the domain are related. Such knowledge bases are most commonly represented as a graph, and both the nodes and the edge relationships between nodes in that graph must be explicitly modeled either manually by a domain expert or automatically leveraging an ontology learning system. Because building such a knowledge base typically requires explicitly modeling nodes and edges into a graph ahead of time, this unfortunately presents several limitations to the use of such a knowledge graph: ¹

http://github.com/careerbuilderisemantic-knowledge-graph/treeidsaa20

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Entities not modeled explicitly as nodes have no known relationships to any other entities.

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Edges exist between nodes, but not between arbitrary combinations of nodes, and therefore such a graph is not ideal for representing nuanced meanings of an entity when appearing within different contexts, as is common within natural language.

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Substantial meaning is encoded in the linguistic representation of the domain that is lost when the underlying textual representation is not preserved: phrases, interaction of concepts through actions (i.e. verbs), positional ordering of entities and the phrases containing those entities, variations in spelling and other representations of entities, the use of adjectives to modify entities to represent more complex concepts, and aggregate frequencies of occurrence for different representations of entities relative to other representations.

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It can be an arduous process to create robust ontolo-gies, map a domain into a graph representing those ontologies, and ensure the generated graph is compact, accurate, comprehensive, and kept up to date.