Knowledge Graphs and Bases

Abstract

We see physical and informational networks, connections, relationships, and links grow all around us. This chapter contemplates the universal graph structure at the core of these developments. Relations between nodes, different than those of a hierarchical or subsumptive nature, provide still different structural connections across the knowledge graph. Besides graph theory, the field draws on methods including statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. Graph theory and network science are the suitable disciplines for a variety of information structures and many additional classes of problems. Once we understand graphs as an excellent way to represent logic and data structures, the next step is to extend their applicability to knowledge representations and knowledge bases as well. We see the usefulness of graph theory to linguistics by the various knowledge bases such as WordNet (in multiple languages) and VerbNet. Domain ontologies emphasize conceptual relationships over lexicographic ones for a given knowledge domain. These constructs of semantic Web standards, combined with a properly constructed knowledge graph and the use of synonymous and related vocabularies in semsets, provide potent mechanisms for how to query a knowledge base. Furthermore, if we sufficiently populate a knowledge graph with accurate instance data, often from various knowledge bases, then ontologies can also be the guiding structures for efficient machine learning and artificial intelligence. We want knowledge sources, putatively knowledge bases, to contribute the actual instance data to populate our ontology graph structures.