Eat your own KR: a KR-based approach to index Semantic Web Endpoints and Knowledge Graphs

TL;DR

This paper introduces an extended framework called IndeGx that uses knowledge representation and reasoning techniques to index, characterize, and monitor the content and usage of over 300 public knowledge graphs on the Semantic Web.

Contribution

It extends the IndeGx framework with an ontology-oriented, declarative approach for indexing and reasoning over knowledge graphs, enabling better understanding and monitoring of their usage.

Findings

Applied to 300+ knowledge graphs, revealing their language support and ontology usage.

Demonstrated the framework's ability to reason about graph characteristics and quality.

Provided a snapshot of the Semantic Web's current state regarding knowledge graph standards.

Abstract

Over the last decade, knowledge graphs have multiplied, grown, and evolved on the World Wide Web, and the advent of new standards, vocabularies, and application domains has accelerated this trend. IndeGx is a framework leveraging an extensible base of rules to index the content of KGs and the capacities of their SPARQL endpoints. In this article, we show how knowledge representation (KR) and reasoning methods and techniques can be used in a reflexive manner to index and characterize existing knowledge graphs (KG) with respect to their usage of KR methods and techniques. We extended IndeGx with a fully ontology-oriented modeling and processing approach to do so. Using SPARQL rules and an OWL RL ontology of the indexing domain, IndeGx can now build and reason over an index of the contents and characteristics of an open collection of public knowledge graphs. Our extension of the framework relies on a declarative representation of procedural knowledge and collaborative environments (e.g., GitHub) to provide an agile, customizable, and expressive KR approach for building and maintaining such an index of knowledge graphs in the wild. In doing so, we help anyone answer the question of what knowledge is out there in the world wild Semantic Web in general, and we also help our community monitor which KR research results are used in practice. In particular, this article provides a snapshot of the state of the Semantic Web regarding supported standard languages, ontology usage, and diverse quality evaluations by applying this method to a collection of over 300 open knowledge graph endpoints.