6533b838fe1ef96bd12a4637

RESEARCH PRODUCT

HOWERD: A Hidden Markov Model for Automatic OWL-ERD Alignment

Roberto PirroneFrancesca AnastasioArianna Pipitone

subject

computer.internet_protocolComputer scienceProcess ontology02 engineering and technologyOntology (information science)computer.software_genre01 natural sciencesOWL-S0202 electrical engineering electronic engineering information engineeringUpper ontologyHidden Markov modelcomputer.programming_languageSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryComputer Science::Information RetrievalOntology-based data integration010401 analytical chemistry020207 software engineeringWeb Ontology Language0104 chemical sciencesHidden Markov models Knowledge representation languages Ontologies (artificial intelligence) Semantic Web Databases OWL ERDArtificial intelligencebusinesscomputerOntology alignmentNatural language processing

description

The HOWERD model for estimating the most likely alignment between an OWL ontology and an Entity Relation Diagram (ERD) is presented. Automatic alignment between relational schema and ontology represents a big challenge in Semantic Web research due to the different expressiveness of these representations. A relational schema is less expressive than the ontology; this is a non trivial problem when accessing data via an ontology and for ontology storing by means of a relational schema. Existent alignment methodologies fail in loosing some contents of the involved representations because the ontology captures more semantic information, and several elements are left unaligned. HOWERD relies on a Hidden Markov Model (HMM) to estimate the most likely sequence of ERD symbols in a relational schema that correspond to the constructs of an OWL axiom in the ontology to be aligned. Such constructs are the observable states in the HMM, while hidden states are modeled as the symbols of a context free grammar defined purposely for describing the input ERD lexically. The theoretical background, the model and the implemented system are described in detail. Finally, HOWERD is compared to the most widespread tools in the reference literature.

yearjournalcountryeditionlanguage
2016-02-012016 IEEE Tenth International Conference on Semantic Computing (ICSC)
https://doi.org/10.1109/icsc.2016.76