The exponentially grow of Web and data availability, the semantic web area has expanded and each day more data is expressed as knowledge bases. Knowledge bases (KB) used in most projects are represented in an ontology-based fashion, so the data can be better organized and easily accessible. It is common to map these KBs into a graph when trying to induce inference rules from the KB, thus it is possible to apply graph-mining techniques to extract implicit knowledge. One common graph-based task is link prediction, which can be used to predict edges (new facts for the KB) that will appear in a near future. In this paper, we present Graph Rule Learner (GRL), a method designed to extract inference rules from ontological knowledge bases mapped to graphs. GRL is based on graph-mining techniques, and explores the combination of link prediction metrics. Empirical analysis revealed GRL can successfully be applied to NELL(Never-Ending Language Learner) helping the system to infer new KB beliefs from existing beliefs (a crucial task for a never-ending learning system).
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Finding Inference Rules Using Graph Mining in Ontological Knowledge Bases
Semantic Scholar · Computer Science · 2016
Abstract
The exponentially grow of Web and data availability, the semantic web area has expanded and each day more data is expressed as knowledge bases. Knowledge bases (KB) used in most projects are represented in an ontology-based fashion, so the data can be better organized and easily accessible. It is common to map these KBs into a graph when trying to induce inference rules from the KB, thus it is possible to apply graph-mining techniques to extract implicit knowledge. One common graph-based task is link prediction, which can be used to predict edges (new facts for the KB) that will appear in a near future. In this paper, we present Graph Rule Learner (GRL), a method designed to extract inference rules from ontological knowledge bases mapped to graphs. GRL is based on graph-mining techniques, and explores the combination of link prediction metrics. Empirical analysis revealed GRL can successfully be applied to NELL(Never-Ending Language Learner) helping the system to infer new KB beliefs from existing beliefs (a crucial task for a never-ending learning system).