Link Analysis meets Ontologies: Are Embeddings the Answer?

TL;DR

This study systematically evaluates structure-only link analysis methods for detecting anomalies and novel relations in large semantic knowledge bases, highlighting their scalability and explainability advantages.

Contribution

It provides one of the most extensive evaluations of link analysis techniques across diverse semantic resources, demonstrating their potential for scalable anomaly detection and explanation.

Findings

Structure-only link analysis can detect anomalies in some datasets.

Symbolic node embeddings enable explanation of link predictions.

Methods vary in effectiveness across different semantic resources.

Abstract

The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size. The development of approaches that identify potentially spurious parts of a given knowledge base is thus becoming an increasingly important area of interest. In this work, we present a systematic evaluation of whether structure-only link analysis methods can already offer a scalable means to detecting possible anomalies, as well as potentially interesting novel relation candidates. Evaluating thirteen methods on eight different semantic resources, including Gene Ontology, Food Ontology, Marine Ontology and similar, we demonstrated that structure-only link analysis could offer scalable anomaly detection for a subset of the data sets. Further, we demonstrated that by considering symbolic node embedding, explanations of the predictions (links) could be obtained, making this branch of methods potentially more valuable than the black-box only ones. To our knowledge, this is currently one of the most extensive systematic studies of the applicability of different types of link analysis methods across semantic resources from different domains.