Description Logics for Interoperability

Abstract

Conceptual Modelling and Ontology Design

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For the purpose of this short survey, an Ontology will be considered as a Conceptual Schema expressed in a suitable conceptual data model (i.e., an Ontology Language). Good conceptual data models put their emphasis on the correct and semantically rich representation of complex properties and relations that may exist between documents. They should allow for an abstract representation of data which resembles the way they are actually perceived and used in the real world, thus shortening (with respect to the more traditional data models) the semantic gap between the domain and its representation.

Conceptual (or Ontology) modelling deals with the question on how to describe in a declarative and reusable way the domain information of an application, its relevant vocabulary, and how to constrain the use the data, by understanding what can be drawn from it. Not surprisingly, conceptual modelling tasks have always been in the mainstream of KR research – see for example the research on Ontology representation and design – and can be considered now one of the main applications of KR languages and reasoning technique. In addition, given the high complexity of the modelling task when complex data is involved, in the semantic web field there is the demand of more sophisticated and expressive languages than for normal information systems. Again, DL research is very active in providing expressive ontology languages to capture various aspects of the information (see, e.g., [ 1, 3, 2, 11, 15, 12, 6 ]).

A big part of the DL community likes to see a generic ontology language as the generalisation of both the object-oriented data model based on UML class diagrams and the extended Entity-Relationship (EER) semantic data model, strictly related to the ontology web languages such as DAML+OIL and OWL. Our work in this direction includes the i.com tool [ 14, 22 ] – which fully implements an extended conceptual data model generalising the UML class diagrams and the EER schemas – and which is available online for the evaluation of the principles just exposed at the public web address http://www.inf.unibz.it/ ~franconi/icom/. i.com allows for the specification of multiple EER (or UML) diagrams and inter- and intra-schema constraints. Complete logical reasoning is employed by the tool using an underlying DL inference engine to verify the specification, infer implicit facts and stricter constraints, and manifest any inconsistencies during the conceptual modelling phase.

Information Access

Only recently has KR research started to have an interest in query processing and information access. Recent work has come up with advanced reasoning techniques for query evaluation and rewriting using views under the constraints given by the ontology – also called view-based query processing. This means that the notion of accessing information through the navigation of an Ontology modelling the document’s domain – which can be seen as a conceptual schema – has its formal foundations.

I will thus consider DL for formalising not only the ontology but also the query processing as well. The (DL-based) conceptual schema as defined in the previous section can be seen as a set of constraints over a vocabulary which is usually richer that the logical schema of the information system it is modelling. In some sense, quite often the conceptual schema plays the role of an general ontology of the domain, very close to the user’s rich vocabulary, rather than of a set of constraints over the poor logical vocabulary structuring the data. With this perspective in mind, the user would prefer to query the information system using the richer vocabulary of the ontology [ 9 ]. The vocabulary of the basic data (i.e., the logical schema) could be seen in turn either as a subset of the conceptual vocabulary – this is the simplistic view – or more generally as a set of (materialised) views over the vocabulary of the ontology. However, in this case we have to solve the problem of view-based query processing. The problem requires to answer a query posed to a database – the one defined by the ontology – only on the basis of the information in a set of (materialised) views, which are again queries over the same database. In the process, the information contained in the conceptual schema of the database should be of course taken into account.

Two approaches to view-based query processing exist, namely query rewriting and query answering (see, e.g., [ 25 ]). This framework can be used to characterise several aspects of an information system to support interoperability. In query optimisation, view-based query processing is relevant because using the views may speed up query processing. In data integration, the views represent the only information sources accessible to answer a query. A data warehouse can be seen as a set of materialised views, and, therefore, query processing reduces to view-based query answering. Finally, since the views provide partial knowledge on the database, view-based query processing can be seen as a special case query answering with incomplete information.

Information Integration and Interoperability

In this last part I will hint how the technologies introduced in the first two parts, namely a very expressive ontology language and view-based query processing over it, can be used in the framework of Information Integration [ 16, 21, 22, 24 ].

Let us suppose to have multiple databases to be integrated. Each database will have its own conceptual schema and logical schema, where, as seen in the previous part, the logical schema is just a set of views over the conceptual schema (local-as-view approach). We assume that each symbol of each schema is identified by a unique global symbol, i.e., the various databases have disjoint signatures. Interdependencies between entities and relationships in different schemas are represented by means of integrity constraints involving symbols of the schemas. Such interdependencies are called coordination formulas, and they are of the form of inclusion dependencies expressed in a suitable view language (e.g., a DL itself, or a SPJ query language. The union of the various schemas with the coordination formulas and the local views forms the global integrated schema, or the mediator. It is worth noting that the integration process is incremental – since the integrated schema can be monotonically refined as soon as there is new understanding of the different component schemas – and that the resulting unified schema is strongly dependent from (actually, it includes) the schemas of the single information sources.

This approach gives both a clear semantics to the integration process of ontologies, and a calculus for deriving inconsistencies and checking the validity of integrity constraints in the integrated schema. Most importantly, in this framework global queries can be defined as views over single ontologies, or they can be generalised to span over multiple ontologies. The view-based query processing mechanism will guarantee the correct answer to the global query from the local sources.

In [ 23 ] a comparison is given between the above local-as-view approach to processing global queries and the global-as-view approach, which is more common in current information integration architectures.

More recent research work is dealing with the application of the above ideas in a peer-to-peer interoperability framework [ 17, 19, 18 ]. Here, the difference with the classical framework are the following: (a) the role of the coordination formulas between nodes is for data migration (as opposed to the role of logical constraints in classical data integration systems); (b) computation is delegated to single nodes (distributed local computation); (c) the topology of the network may dynamically change; (d) local inconsistency does not propagate; (e) computational complexity can be low.

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