Abstract. Information integration and retrieval have been important problems for many information systems — it is hard to combine new information with any other piece of related information we already possess, and to make them both available for application queries. Many ontologybased applications are still cautious about integrating and retrieving information from natural language (NL) documents, preferring structured or semi-structured sources. In this paper, we investigate how to use ontologies to facilitate integrating and querying information on parallel leaf shape descriptions from NL documents. Our approach takes advantage of ontologies to precisely represent the semantics in shape description, to integrates parallel descriptions according to their semantic distances, and to answer shape-related species identification queries. From this highly specialised domain, we learn a set of more general methodological rules, which could be useful in other domains. 1

The development and maintenance of domain-specific application ontologies require knowledge input from domain experts who are usually without any formal ontology or AI background. When dealing with large-scale ontologies, for example of the kind with which we are currently familiar in the biomedical spheres, quality assurance becomes important in minimizing modelling mistakes and the application errors which they bring in their wake. In this paper we describe how the upper-level framework BFO (for: Basic Formal Ontology), developed by the Institute for Formal Ontology and Medical Information Science, is being used to provide automatic error detection and run-time modelling support to the development of LinKBase, a large-scale medical domain ontology developed by Language and Computing NV to serve a range of natural language processing applications.

LinKBase ® is a biomedical ontology. Its hierarchical structure, coverage, use of operational, formal and linguistic relationships, combined with its underlying language technology, make it an excellent ontology to support Natural Language Processing and Understanding (NLP/NLU) and data integration applications. In this paper we will describe the structure and coverage of LinKBase®. In addition, we will discuss the editing of LinKBase ® and how domain experts are guided by specific editing rules to ensure modeling quality and consistency. Finally, we compare the structure of LinKBase ® to the structure of third party terminologies and ontologies and discuss the integration of these data sources into

Information integration and retrieval have been important problems for many information systems — it is hard to combine multidimensional and parallel information and make them available for application queries. In our previous work [12], we have shown how to use ontologies to facilitate integrating and querying parallel but single dimensional information. In this paper, we further investigate how to take advantage of ontologies to facilitate integrating parallel information and querying over multiple quantities. 1