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Software-intensive information systems have a major impact on our lives, both privately and professionally. Development of these systems is a complex activity that requires the involvement of people with different competences and skills. Even though software-intensive systems have been developed since the 1960s, the success rate is still low. A major hindrance to successful system development projects is the lack of consistent terminology. Since systems development is a collaborative activity, involving not only systems developers but also domain experts and user representatives, the understanding of each other is a prerequisite for an effective collaboration. The aim of this paper is to explore and present definitions, dependencies, and relationships of the most fundamental concepts in systems development in the form of an ontology. The ontology consists of four categories of concepts: General concepts, Description concepts, Realization concepts, and Appearance concepts. The two core concepts in the ontology are Systems and Systems development.

Information Retrieval (IR) and NLP-driven Information Extraction (IE) are complementary activities. IR helps in locating specific documents within a huge search space (localization) while IE supports the localization of specific information within a document (extraction or explanation). In application scenarios both capabilities are usually needed. IE is important here, as it can enrich the IR inferences with motivating information. Works on Web-based IR suggest that embedding linguistic information (e.g. sense distinctions) at a suitable level within traditional quantitative approaches (e.g. query expansion as in [26]) is a promising approach. "Which linguistic level is best suited to which IR mechanism" is the interesting representational problem posed by the current research stage. This is also the central concern of this paper. A traditional method for efficient text categorization is here presented. Original features of the proposed model are a self-adapting parameterized weighting model and the use of linguistic information. The key idea is the integration of NLP methods within a robust and efficient TC framework. This allows to combine benefits of large scale and efficient IR with the richer expressivity closer to IE. In this paper we capitalize the systematic benchmarking resources available in TC to extensively derive empirical evidence about the above representational problem. The positive experimental results confirm that the proposed TC framework characterizes as a viable approach to intelligent text categorization on a large scale.

Recently, a number of collaborative large-scale mouse mutagenesis programs have been launched. These programs aim for a better understanding of the roles of all individual coding genes and the biological systems in which these genes participate. In international efforts to share phenotypic data among facilities/institutes, it is desirable to integrate information obtained from different phenotypic platforms reliably. Since the definitions of specific phenotypes often depend on a tacit understanding of concepts that tends to vary among different facilities, it is necessary to define phenotypes based on the explicit evidence of assay results. We have developed a website termed PhenoSITE (Phenome Semantics Information with Terminology of Experiments: ), in which we are trying to integrate phenotype-related information using an experimental-evidence–based approach. The site's features include (1) a baseline database for our phenotyping platform; (2) an ontology associating international phenotypic definitions with experimental terminologies used in our phenotyping platform; (3) a database for standardized operation procedures of the phenotyping platform; and (4) a database for mouse mutants using data produced from the large-scale mutagenesis program at RIKEN GSC. We have developed two types of integrated viewers to enhance the accessibility to mutant resource information. One viewer depicts a matrix view of the ontology-based classification and chromosomal location of each gene; the other depicts ontology-mediated integration of experimental protocols, baseline data, and mutant information. These approaches rely entirely upon experiment-based evidence, ensuring the reliability of the integrated data from different phenotyping platforms.