Improving information retrieval-based concept location using contextual relationships

For software engineers to find all the relevant program elements implementing a business concept, existing techniques based on information retrieval (IR) fall short in providing adequate solutions. Such techniques usually only consider the conceptual relations based on lexical similarities during concept mapping. However, it is also fundamental to consider the contextual relationships existing within an application’s business domain to aid in concept location. As an example, this paper proposes to use domain specific ontological relations during concept mapping and location activities when implementing business requirements

Natural ontology representation based on NP's properties and semantic relations

International audienceIn the context of the valorization of Tunisian patrimony, we propose an approach to represent semantic properties on contents: heterogeneous information (multimedia) concerning patrimony objects. We develop indexing and information retrieval (IR) processes based on noun phrase (NP) and its semantic representation. These processes use natural language processing (NLP) to take into account the NPs structure organization. In view of this study, the ontology has been proposed to capitalize the concept of knowledge as NP and its semantic relations

Specific ontologies for semantic indexing from natural language properties

International audienceIn this paper, we present a specific ontologies based on the extraction of semantic information in text documents. Document here concerns the context of the valorization of Tunisian patrimony. As approach, we propose to represent semantic properties in document contents from heterogeneous information (multimedia) concerning by the patrimony objects. For indexing and information retrieval (IR), we develop processes based on the noun phrase (NP) properties and their semantic representations. These processes use natural language processing (NLP) to take into account the NP syntactic and semantic structures. In view of this study, the specific ontology designed has the encapsulation principle to capitalize the concept and knowledge as NP and its semantic relations

Automatic Ontology Construction for a National Term Bank

In our paper we present a project, the aim of which is to develop innovative and advanced methods for dynamic and automatic extraction of knowledge about concepts from texts and for automatic construction of ontologies. The project builds on and further develops the results of the CAOS project - Computer-Aided Ontology Structuring - which was carried out at Copenhagen Business School in the period 1998-2007. Terminological ontologies differ from other types of ontologies by comprising feature specifications and subdivision criteria. We have formalised subdivision criteria that have been used for many years in terminology work, by introducing dimensions and dimension specifications. In the CAOS prototype, facilities for semiautomatic checking of inconsistencies were developed

Concept graphs: Applications to biomedical text categorization and concept extraction

As science advances, the underlying literature grows rapidly providing valuable knowledge mines for researchers and practitioners. The text content that makes up these knowledge collections is often unstructured and, thus, extracting relevant or novel information could be nontrivial and costly. In addition, human knowledge and expertise are being transformed into structured digital information in the form of vocabulary databases and ontologies. These knowledge bases hold substantial hierarchical and semantic relationships of common domain concepts. Consequently, automating learning tasks could be reinforced with those knowledge bases through constructing human-like representations of knowledge. This allows developing algorithms that simulate the human reasoning tasks of content perception, concept identification, and classification. This study explores the representation of text documents using concept graphs that are constructed with the help of a domain ontology. In particular, the target data sets are collections of biomedical text documents, and the domain ontology is a collection of predefined biomedical concepts and relationships among them. The proposed representation preserves those relationships and allows using the structural features of graphs in text mining and learning algorithms. Those features emphasize the significance of the underlying relationship information that exists in the text content behind the interrelated topics and concepts of a text document. The experiments presented in this study include text categorization and concept extraction applied on biomedical data sets. The experimental results demonstrate how the relationships extracted from text and captured in graph structures can be used to improve the performance of the aforementioned applications. The discussed techniques can be used in creating and maintaining digital libraries through enhancing indexing, retrieval, and management of documents as well as in a broad range of domain-specific applications such as drug discovery, hypothesis generation, and the analysis of molecular structures in chemoinformatics