An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such development and evaluation methods are put forward to guide future efforts of ontology development. Our work contributes to the literature on agricultural ontologies, by presenting a method for evaluating agricultural ontologies, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology's purpose.
Syndromic surveillance requires the acquisition and analysis of data that may be "suggestive" of early epidemics in a community, long before there is any categorical evidence of unusual infection. These data are often heterogenous and often quite noisey. The processs of syndromic surveillance poses problems in data integration; in selection of appropriate reusable problem-solving methods, based on task features and on the nature of the data at hand; and in mapping integrated data to appropriate problem solvers. These are all tasks that have been studied carefully in the knowledge-based systems community for many years. We demonstrate how a software architecture that suppports knoweldge-based data integrationa and problem solving facilitates many aspects of the syndromic-surveillance task. In particular, we use reference ontologies for purposes of semantic integration and a parallelizable blackboard architecture for invocation of appropriate problem solving methods and for control of reasoning. We demonstrate our results in the context of a prototype system known as the Biological Spacio-Temporal Outbreak Reasoning Module (BioSTORM), which offers an end-to-end solution to the problem of syndromic surveillance.
Ontology reuse is turning into an important research issue in the ontology field. Ontology reuse can be seen from two points of view: (1) assembling, extending, specializing, adapting other ontologies which are parts of the resulting ontology, or (2) merging different ontologies on the same similar subject into a single one that unifies all of them. The first kind of reuse is named integration and is the central issue of this paper. In this article, we characterize the integration process, describe and discuss the activities that compose this process and propose an integration methodology. This integration methodology has successfully been applied to build two ontologies in different domains by reusing publicly available ontologies. Introduction Ontologies are used in the Knowledge Management (KM) field, for several purposes: as corporate memories, for interoperability of databases through a common ontology, etc. Therefore, results in the ontology field are of use for KM. A considerable amount of ontologies has been built during the last decade. The time has come for sharing and reusing this body of knowledge. One can see ontology reuse according to two different points of view: Building an ontology, by assembling, extending, specializing and adapting, other ontologies which are parts of the resulting ontology. Therefore, they are related to the process of ontology building. However, each kind of reuse is different from the other (Pinto, G6mez-P6rez, & Martins 1999).
Knowledge acquisition is usually the first step in building ontologies. On the one hand, knowledge is typically implicitly contained in large collections of unstructured documents. Therefore it is extremely troublesome to manually identify relevant concepts. On the other hand, users are often not fully satisfied with the results of automated stateof-the-art ontology learning techniques. In this paper we present a technique for large-scale Knowledge Acquisition supported Semi-automated Ontology building (KASO) and a corresponding software system. By applying KASO and using this software, users are able to bootstrap the process of building high quality ontologies by automatically acquiring concepts from large-scale document collections and to make use of traditional knowledge acquisition approaches to refine and organize the machine-generated concepts. Evaluation studies and user experiences indicate the applicability of KASO in bootstrapping ontology construction.