  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.
