Efficient Selection and Integration of Data Sources for Answering Semantic Web Queries

Abstract

In this work we adapt an efficient information integration algorithm to identify the minimal set of potentially relevant Semantic Web data sources for a given query. The vast majority of these sources are files written in RDF or OWL format, and must be processed in their entirety. Our adaptation includes enhancing the algorithm with taxonomic reasoning, defining and using a mapping language for the purpose of aligning heterogeneous Semantic Web ontologies, and introducing a concept of source relevance to reduce the number of sources that we need to consider for a given query. After the source selection process, we load the selected sources into a Semantic Web reasoner to get a sound and complete answer to the query. We have conducted an experiment using synthetic ontologies and data sources which demonstrates that our system performs well over a wide range of queries. A typical response time for a substantial work load of 50 domain ontologies, 80 map ontologies and 500 data sources is less than 2 seconds. Furthermore, our system returned correct answers to 200 randomly generated queries in several workload configurations. We have also compared our adaptation with a basic implementation of the original information integration algorithm that does not do any taxonomic reasoning. In the most complex configuration with 50 domain ontologies, 100 map ontologies and 1000 data sources our system returns complete answers to all the queries whereas the basic implementation returns complete answers to only 28% of the queries.

ISENS: A System for Information Integration, Exploration, and Querying of Multi-Ontology Data Sources

“…Since we have described the ISENS back-end components previously [4], [6], we mention here only essential information about them and cover in more detail the capabilities of the ISENS user interface.…”

“…We refer interested readers to Qasem et al [4] for a formal description of the source selection framework and Qasem et al [6] for an extended exposition of our algorithms.…”

“…We explain in detail ontology maps and REL statements in Subsection III-B. In OBII, we have implemented two source selection algorithmsthe goal node search [6] (GNS) algorithm and our original source selection algorithm [4] (we adapted the peer data management system (PDMS) reformulation algorithm [8] to the Semantic Web) in this module. PDMS extends the standard Local-as-View (LAV) and Global-as-View (GAV) information integration approaches [9].…”

“…In our solution, we consider OWL axioms (a subset of OWL to be precise) to map terms in domain ontologies. We have developed a mapping language for OWL information integration (OWLII) [4], [7] to specify the map ontologies.…”

“…It is specifically designed for the Semantic Web and enables constructing sophisticated queries to search for different types of data. For information integration over data described by multiple OWL ontologies, we implemented a solution based on map ontologies, source descriptions, and source selection algorithms that we developed for efficient identification of relevant Semantic Web data sources during query answering [4], [5], [6], [7].…”