Abstract

The rapid growth in digitization of data has led to creation of fragmented but vital knowledge sources. Ontologies are one such crucial source of knowledge and aligning them is a key challenge for creating an Open Knowledge Network. The task of ontology alignment has received significant attention. In this abstract, we building on existing work, and propose a novel probabilistic ontology alignment approach that combines several similarity measures with structural information such as subsumption and mutual exclusion.
Most large-scale ontologies such as product catalogs [Agrawal et. al. 2001] and folksonomies [Plangprasopchok et. al. 2010] do not have a formally defined ontology with well-defined classes, instances and properties. Instead, they loosely define relationships such as subsumption between various entities. For example, a folksonomy for Instagram would contain not only tags corresponding to people, places and activities but also tags such as Selfie, which correspond to a type of image. Product catalogs have very different textual representation for the same entity. For instance, products related to 3D printing are present in a category called 3D Printing & Supplies on Ebay, while the same products are present in a category called Additive Manufacturing Products on Amazon. Moreover, the same textual representation might have different semantics based on the source of the ontology. The category Headphones in an ontology corresponding to a particular company is different from the Headphones category of a large e-commerce retailer such as Amazon. Even aligning tracks in a music catalog is considerably challenging as it is …

Date

January 1, 1970

Authors

Varun Embar, Jay Pujara, Lise Getoor

Journal

AKBC Workshop on Federated Knowledge Bases