Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

Abstract

Efficient resource retrieval is a crucial issue, particularly when semantic resource descriptions are considered which enable the exploitation of reasoning services during the retrieval process. In this context, resources are commonly retrieved by checking if each available resource description satisfies the given query. This approach becomes inefficient with the increase of available resources. We propose a method for improving the retrieval process by constructing a tree index through a new conceptual clustering method for resources expressed as class definitions or as instances of classes in ontology languages. The available resource descriptions are located at the leaf nodes of the index, while inner nodes represent intensional descriptions (generalizations) of their child nodes. The retrieval is performed by following the tree branches whose nodes satisfy the query. Query answering time may be improved as the number of retrieval steps may be O(log n) in the best case.

Ontologies and Similarity

“…But independently from which representation is chosen to define which exact similarity measure, there is a wide range of applications for which this combination has been used. We simply give here an indicative list without much further explanation (and without claiming completeness): Information Retrieval [10]; Machine Learning [1]; Web Service Discovery [11]; Ontology Alignment [7]; Ontology Learning: Evaluation [12,6]; Ontology Learning: Induction [2]; Indexing of description logics ontologies [5] …”