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

2025-08-12

AI Summary

The semantic web lacks systematic monitoring of knowledge graph (KG) technology adoption and knowledge distribution. Method: This study proposes IndeGx, a reflexive knowledge representation (KR) framework that applies KR techniques—including SPARQL rules, OWL RL ontologies, and declarative process modeling—reflexively to KG index construction. Leveraging ontology-driven dynamic modeling and collaborative maintenance via GitHub integration, IndeGx enables agile, scalable, and customizable characterization of open KGs and their SPARQL endpoints across content features, ontology usage, standards compliance, and quality metrics. Contribution/Results: We conducted large-scale indexing and analysis of over 300 public KG endpoints, yielding the first empirical, ecosystem-wide panorama of semantic web technologies. This work provides a reusable methodology and empirical foundation for evaluating KR research impact and guiding infrastructure evolution.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Natural Language Processing: Information Extraction

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web crawling and indexing

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.

Problem

• Index and characterize knowledge graphs using KR methods
• Extend IndeGx with ontology-oriented modeling for KG analysis
• Monitor KR research usage in public Semantic Web endpoints

Innovation

• Extensible rule-based framework for KG indexing
• Ontology-oriented modeling with SPARQL rules
• Declarative representation for agile KR approach