Paea LePendu

Ontology-based integration for relational databases

In this paper, we show that representation and reasoning techniques used in traditional knowledge engineering and the emerging Semantic Web can play an important role for heterogeneous database integration. Our OntoGrate architecture combines ontology-based schema representation, first order logic inference, and some SQL wrappers to integrate two sample relational databases. We define inferential data integration as the theoretical framework for our approach. The performance evaluation for query answering shows that OntoGrate reformulates conjunctive queries and retrieves over 100,000 answers from a target database in under 30 seconds. In addition to query answering, the system translates 40,000 database facts from source to target in under 30 seconds.

Integrating Databases into the Semantic Web through an Ontology-Based Framework

To realize the Semantic Web, it will be necessary to make existing database content available for emerging Semantic Web applications, such as web agents and services, which use ontologies to formally define the semantics of their data. Our research in the design and implementation of an ontology-based system, OntoGrate, addresses the critical and challenging problem of supporting human experts in multiple domains to interactively integrate information that is heterogenous in both structure and semantics. Databases, knowledge bases, the World Wide Web, and the emerging Semantic Web are some of the resources for which scalable integration remains a challenge. To integrate databases into the Semantic Web, we use Semantic Web ontologies to incorporate database schemas. An expressive first order ontology language, Web-PDDL, is used to define the structure, semantics, and mappings of data resources. A powerful inference engine, OntoEngine, can be used for query answering and data translation. In this paper, besides introducing new ideas in the OntoGrate system, we will elaborate on two case studies for which our system works well.

Discovering executable semantic mappings between ontologies

Creating executable semantic mappings is an important task for ontology-based information integration. Although it is argued that mapping tools may require interaction from humans (domain experts) for best accuracy, in general, automatic ontology mapping is an AI-Complete problem. Finding matchings (correspondences) between the concepts of two ontologies is the first step towards solving this problem but matchings are normally not directly executable for data exchange or query translation. This paper presents an systematic approach to combining ontology matching, object reconciliation and multi-relational data mining to find the executable mapping rules in a highly automatic manner. Our approach starts from an iterative process to search the matchings and do object reconciliation for the ontologies with data instances. Then the result of this iterative process is used for mining frequent queries. Finally the semantic mapping rules can be generated from the frequent queries. The results show our approach is highly automatic without losing much accuracy compared with human-specified mappings.

Ontology Database: A New Method for Semantic Modeling and an Application to Brainwave Data

We propose an automatic method for modeling a relational database that uses SQL triggers and foreign-keys to efficiently answer positive semantic queries about ground instances for a Semantic Web ontology. In contrast with existing knowledge-based approaches, we expend additional space in the database to reduce reasoning at query time. This implementation significantly improves query response time by allowing the system to disregard integrity constraints and other kinds of inferences at run-time. The surprising result of our approach is that load-time appears unaffected, even for medium-sized ontologies. We applied our methodology to the study of brain electroencephalographic (EEG and ERP) data. This case study demonstrates how our methodology can be used to proactively drive the design, storage and exchange of knowledge based on EEG/ERP ontologies.

Using ontology databases for scalable query answering, inconsistency detection, and data integration

An ontology database is a basic relational database management system that models an ontology plus its instances. To reason over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate our method—using triggers—and we demonstrate that by forward computing inferences, we not only improve query time, but the improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first, ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based inconsistencies—something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration across multiple, distributed ontology databases.

ONTOGRATE: TOWARDS AUTOMATIC INTEGRATION FOR RELATIONAL DATABASES AND THE SEMANTIC WEB THROUGH AN ONTOLOGY-BASED FRAMEWORK

Integrating existing relational databases with ontology-based systems is among the important research problems for the Semantic Web. We have designed a comprehensive framework called OntoGrate which combines a highly automatic mapping system, a logic inference engine, and several syntax wrappers that inter-operate with consistent semantics to answer ontology-based queries using the data from heterogeneous databases. There are several major contributions of our OntoGrate research: (i) we designed an ontology-based framework that provides a unified semantics for mapping discovery and query translation by transforming database schemas to Semantic Web ontologies; (ii) we developed a highly automatic ontology mapping system which leverages object reconciliation and multi-relational data mining techniques; (iii) we developed an inference-based query translation algorithm and several syntax wrappers which can translate queries and answers between relational databases and the Semantic Web. The testing results of our implemented OntoGrate system in different domains show that the large amount of data in relational databases can be directly utilized for answering Semantic Web queries rather than first converting all relational data into RDF or OWL.

Detecting Inconsistencies in the Gene Ontology Using Ontology Databases with Not-gadgets

We present ontology databases with not-gadgets, a method for detecting inconsistencies in an ontology with large numbers of annotated instances by using triggers and exclusion dependencies in a unique way. What makes this work relevant is the use of the database itself, rather than an external reasoner, to detect logical inconsistencies given large numbers of annotated instances. What distinguishes this work is the use of event-driven triggers together with the introduction of explicit negations. We applied this approach toward the serotonin example, an open problem in biomedical informatics which aims to use annotations to help identify inconsistencies in the Gene Ontology. We discovered 75 inconsistencies that have important implications in biology, which include: (1) methods for refining transfer rules used for inferring electronic annotations, and (2) highlighting possible biological differences across species worth investigating.

Ontology-Based integration for relational data

Motivation. Recent years gave witness to significant progress in database integration including several commercial implementations. However, existing works make strong assumptions about mapping representations but are weak on formal semantics and reasoning. Current research and practical application calls for more formal approaches in managing semantic heterogeneity [3].