• Ontology Mapping with OWL and Rules

This is one of the possible Use Cases.

This is an anchor link to "Breakdown"

1. Abstract

This use case covers scenarios where rules are used for data integration in a web-based setting in combination with OWL-DL ontologies. OWL-DL knowledge bases and alignments between them are to be collected and evaluated in an automated fashion.

2. Status

• Originally proposed by Markus Krötzsch (FZI Karlsruhe), introduced as use case FZI-1 at F2F1.

• Based on research activities at FZI and AIFB Karlsruhe, using the KAON2 reasoner for OWL-DL and DL-safe rules. For details, see:

Peter Haase and Boris Motik. A mapping system for the integration of OWL-DL ontologies. In Proceedings of the ACM-Workshop: Interoperability of Heterogeneous Information Systems (IHIS05). November 2005.

• Implementation in the KAON2 system is available and the system is under further active development.

3. Links to Related Use Cases

• Internet search: combining query language, rule languages and scoped negation. This use case targets a similar scenario and therefore partially imposes similar requirements. However, both use cases still have a very different focus, and consider complementary aspects and requirements. In particular, the present use case considers a light-weight rule language, featuring seamless integration with OWL-DL and the possibility of fully automatic processing and integration. In contrast, the use case "Internet search" focusses on advanced features of increased computational complexity to be added "on top of RDFS and OWL" but for which decidability is not considered crucial (at least it is not discussed in the current description of the use case). Thus the two use cases basically reflect the typical complementary aspects of tractability and expressivity that have to be accounted for in any rule language, even when conceived for similar usage scenarios.

Also feel free to add a pointer to your own use case, if appropriate. It's a wiki! (Email notification is appreciated)

4. Relationship to OWL/RDF Compatibility

This use case requires intimate and well-understood compatibility with OWL-DL, since OWL-DL ontologies are to be combined with (in contrast to, e.g., "processed by") online rule-bases. Ontology engineers, service providers, and users should have the freedom to combine OWL and rules for achieving sufficient expressivity.

5. Examples of Rule Platforms Supporting this Use Case

• A type of rules that could be used in this setting are DL-safe rules, which consitute a fragment of SWRL that is decidable even in combination with OWL-DL ontologies. The reasoning system KAON2 widely supports this combination of rules with OWL knowledge bases. KAON2 also is capable of pre-processing of schematic knowledge (TBox) as suggested below for improving efficiency of query answering.

6. Benefits of Interchange

• Online exchange of rules.
• Integration of external rule bases into local ontology.
• Possibility to employ multiple distributed rule bases that are specified in different languages.

7. Requirements on the RIF

• Intimate and well-understood compatibility with OWL-DL; possibility to integrate rules with other ontological knowledge bases.
• Expressiveness that is superiour to OWL-DL.
• Decidability or possibility to automatically restrict to an expressive decidable fragment.
• Support for scalable reasoning on large amounts of instances (ABox).

8. Breakdown

8.1. Actors and their Goals

• End users: want to search for information in distributed knowledge bases
• Service providers: want to attract users by offering web-based search services
• Data providers: want their data to be found by interested users
• Mapping providers: want to enable integration of multiple online knowledge bases

8.2. Main Sequence

1. The user employs an online form to compose a search query and to select online knowledge bases to search in.
2. The search engine collects specified knowledge bases from the data providers.
3. The search engine queries mapping providers for rule-based ontology mappings to align the retrieved knowledge bases. Mapping providers can be the data providers themselves, other service providers (e.g. hosting alignment descriptions), or local repositories of the search engine.
4. The search composes the retrieved rules and ontologies into a unified knowledge base. This step involves consistency checks and the selection of a consistent subset.
5. The search engine evaluates the user query on the unified knowledge base.
6. The user views the query results in an online form.

8.3. Alternate Sequences

8.3.1. Preprocessed query answering

For efficiency reasons, the main sequence might be modified to allow for offline processing:

1. The search engine collects a pre-specified set of knowledge bases from the data providers.
2. The search engine queries mapping providers for rule-based ontology mappings to align the retrieved knowledge bases.
3. The search composes the retrieved rules and ontologies into a unified knowledge base. This step involves consistency checks and the selection of a consistent subset.
4. The search engine performs additional pre-processing steps to simplify reasoning with the (static) unified knowledge base.
5. Users employ an online form to compose a search query against the pre-selected knowledge base.
6. The search engine evaluates the user query on the unified knowledge base.
7. Users view the query results in an online form.

9. Narratives

9.1. Job search engine

A search engine allows users a unified search for job offers that are published on distributed websites of corporations and universities (data providers). Some standard domain-specific ontologies are available for this task, but many data providers require additional concepts to describe their specific requirements. In order to facilitate searching over their offerings, data providers often supply rule-based descriptions of the relationships between their ontologies and the more general domain ontologies. On the oter hand, rule-based alignments between the most common standard ontologies in this domain are maintained in a local repositor of the search provider.

The search engine collects the knowledge bases and alignments as described above, and performs a consistency check. The unified knowledge base turns out to be consistent (also with respect to the user query), and the query results can be calculated and returned to the user.

10. Commentary

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