OntoConnect: Domain-Agnostic Ontology Alignment using Neural Networks

An ontology is a vocabulary that provides a formal description of entities within a domain and their relationships with other entities. Along with basic schema information, it also captures information in the form of metadata about cardinality, restrictions, hierarchy, and semantic meaning. With the rapid growth of semantic (RDF) data on the web, many organizations like DBpedia, Earth Science Information Partners (ESIP) are publishing more and more data in RDF format. The ontology alignment task aims at linking two or more different ontologies from the same domain or different domains. It is a process of finding the semantic relationship between two or more ontological entities and/or instances. Information/data sharing among different systems is quite limited because of differences in data based on syntax, structures, and semantics. Ontology alignment is used to overcome the limitation of semantic interoperability of current vast distributed systems available on the Web. In spite of the availability of large hierarchical domain-specific datasets, automated ontology mapping is still a complex problem. Over the years, many techniques have been proposed for ontology instance alignment, schema alignment, and link discovery. Most of the available approaches require human intervention or work within a specific domain. The challenge involves representing an entity as a vector that encodes all context information of the entity such as hierarchical information, properties, constraints, etc. The ontological representation is rich in comparison with the regular data schema because of metadata about various properties, constraints, relationship to other entities within the domain, etc. While finding similarities between entities this metadata is often overlooked. The second challenge is that the comparison of two ontologies is an intense operation and highly depends on the domain and the language that the ontologies are expressed in. Most current methods require human intervention that leads to a time-consuming and cumbersome process and the output is prone to human errors. The proposed unsupervised recursive neural network technique achieves an F-measure of 80.3% on the Anatomy dataset and the proposed graph neural network technique achieves an F-measure of 81.0% on the Anatomy dataset.