Ontology

Currently the information integration issue attracts researchers from all around the world. Numerous information integration systems are already available and the number is growing fast. Ontologies play an important role for integration as a way of formally defined terms for communication. They aim at capturing domain knowledge in a generic way and provide a commonly agreed understanding of a domain, which may be reused, shared, and operationalized across applications and groups.

A good ontology should represent the domain specific knowledge explicitly. The question is how do we know an ontology is good? The answer is the ontology benchmark.

There are plenty of benchmark studies in other fields like database or compilers. However, there are no specific benchmarks studies or tools for evaluating ontology-based applications. In fact, there is still no guideline to evaluate ontologies and related technologies.

In this section, we introduce the role of ontologies in information integration first.

And then we discuss a major inference task, which is the main operation of an ontology benchmark. Finally, the ontology related benchmark works are reviewed and discussed.

2.4.1 Ontology and Information Integration

Traditional integration approaches use inexpressive models of database schemas or XML trees to integrate heterogeneous data sources. This would cause many semantic heterogeneity problems. Ontologies provide much richer modeling means with classes and properties organized into is-a hierarchy and enriched with axioms and relations processable with inference. Main benefits for an ontology-based approach are illustrated

as follows (Maier, Aguado, Bernaras, Laresgoiti, Pedinaci, Pena, & Smithers, 2003):

 The ability to picture all occurring data structures, for ontologies can be seen as nowadays most advanced knowledge representation model.

 The combination of deduction and relational database systems, which extends the mapping and business logic capabilities.

 A higher degree of abstraction, as the model is separated from the data storage.

 Its extendibility and reusability.

Almost all ontology-based integration approaches ontologies are used for the explicit description of the information source semantics. With respect to the integration of data sources, they can be used for the identification and association of semantically corresponding information concepts. Some approaches use ontologies not only for content explication, but also either as a global query model or for the verification of the (user-defined or system-generated) integration description (Wache, Vögele, Visser, Stuckenschmidt, Schuster, Neumann, & Hübner, 2001). Ontologies are usually expressed in a logic-based language, so that fine, accurate, consistent, sound, and meaningful distinctions can be made among the classes, properties, and relations. Therefore, ontologies not only have the expressiveness needed in order to model the data in the sources, but their reasoning ability can help in the selection of the sources that are relevant for a query of interest, as well as to specify the extraction process. Ontologies let domain experts, system developers, and applications perform reasoning about information content in an application domain.

2.4.2 Ontology and Reasoning

Ontologies intend to provide a machine-understanding syntax for information integration. Understanding is closely related to reasoning. Reasoning is important to ensure the quality of an ontology. During ontology design, it can be used to test whether concepts are non-contradictory and to derive implied relations. It may also be used when the ontology is deployed, one can determine the consistency of facts stated in the annotation with the ontology or infer instance relationships (Baader, Horrocks, & Sattler, 2003). Therefore, reasoning is the major operation in the ontology-based application. The workload model of the ontology benchmark should identify key reasoning tasks in the operation model.

Tempich and Volz (2003) mention that a reasoner supporting ontology languages usually offers several different query services with respect to an ontology. These query services primarily target queries about classes. They fall into four categories, class-instance membership queries, class subsumption queries, class hierarchy queries, and class satisfiability queries. There are similar queries about properties, i.e.

property-instance membership, property subsumption, property hierarchy, and property

satisfiability, and also the possibility to check the consistency of the whole ontology.

Simov and Jordanov (2002) cite that ontologies within their ontology-based project have two types of reasoning tasks, terminological reasoning and instance reasoning.

Terminological reasoning checks the classes are defined and the relations between them are explicitly represented. Instance reasoning involves first an already developed ontology (after some terminological reasoning) and next large amounts of instances.

We find that terminological reasoning is similar to class subsumption queries, class hierarchy queries, and class satisfiability queries. Instance reasoning is similar to class-instance membership queries. This would provide this research with the basis of major reasoning tasks in the operation model of the ontology benchmark workload model.

2.4.3 Ontology and Benchmark

To the best of our knowledge, the benchmark presented here is the first one for ontology-based information integration. The ontology benchmark model in this research differs from database benchmarks, such as Wisconsin benchmark, OO7 benchmark, and BUCKY benchmark. They are all DBMS-oriented and storage benchmarks, and there is no inference ability included. In this research, the ontology workload model is applied to an information integration system, and we focus on the inference ability of the ontology.

Ontology and XML are often found together and are often confused. XML is a standard for marking up - adding additional information, called metadata - to documents.

The purpose of XML is to tag textual information with additional structure that enables it to be“understood”and exchanged by programs.However,XML tagsstillrequirehumans to interpret their meanings. Therefore, XML benchmarks only focus on structural and syntactic evaluation of systems, and they have no semantics. On the other hand, ontology benchmark is devoted to capture the semantic expressions in the system. Thus, ontology and XML are complementary technologies: ontology provides the meaning for XML standards; XML provides a valuable medium for information exchange between programs that share the same ontology (Andersen, 2001).

As mentioned above, there is still no guideline for evaluation of ontology-based application. Horrocks and Patel-Schneider (1998) benchmark description logic systems, or so-called knowledge bases. Description logics (DLs) are a family of knowledge representation languages that can be used to represent the knowledge of an application domain in a structured and formally well-understood way. Description logic systems provide their users with various inference capabilities that deduce implicit knowledge from the explicitly represented knowledge. Horrocks and Patel-Schneider try to evaluate the reasoning algorithms in description logics.

The knowledge base is composed of a Tbox and an Abox. Terminological part (Tbox)

is a set of axioms describing the structure of domain. Assertional part (Abox) is a set of axioms describing concrete situation (Horrocks, 2002). They are related to this research.

In an information integration system, the ontology can be viewed as the Tbox, and the heterogeneous data can be viewed as Abox. However, the logic described is only a subset of the ontology languages, such as DAML+OIL and OWL. DAML+OIL and OWL can be seen to be equivalent to a very expressive description logic. They provide more constructors and allow more axioms than description logic. Therefore, the inference services of ontology are more complex than traditional description logic systems.