Link Analysis meets Ontologies: Are Embeddings the Answer?
This work addresses the challenge of maintaining accuracy in growing knowledge bases for researchers and practitioners in semantic web and ontology domains, but it is incremental as it evaluates existing methods rather than introducing new ones.
The paper tackled the problem of identifying spurious entries and novel relations in large semantic resources by systematically evaluating thirteen structure-only link analysis methods across eight ontologies, demonstrating that these methods offer scalable anomaly detection for a subset of datasets and that symbolic node embeddings can provide explanations for predictions.
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.