Keywords: Ontology Alignment, Semantic Embedding, Distant Supervision, Siamese Neural Network
Abstract: Ontology alignment plays a critical role in knowledge integration and has been widely investigated in the past decades. State of the art systems, however, still have considerable room for performance improvement especially in dealing with new industrial tasks. In this paper we present a machine learning based general extension to traditional ontology alignment systems, using distant supervision for training, ontology embedding and Siamese Neural Networks for incorporating richer semantics. We applied the extension together with traditional systems such as LogMap and AML to align two food ontologies HeLiS and FoodOn, and found that the extension recalls many true mappings that are originally missed and avoids some false positive mappings. This is also verified by the evaluation on the alignments of the OAEI conference track.
First Author Is Student: No
Subtrack: Machine Learning