Semantic component selection - SemaCS

In component based software development, project success or failure largely depends on correct software component evaluation. All available evaluation methods require time to analyse components. Due to the black box nature of components, preliminary judgments are made based on vendor descriptions. As there is no standard way of describing components, descriptions have to be interpreted using semantics and domain knowledge. This paper presents a semi-automated generic method for component identification and classification based on generic domain taxonomy and user generated semantic input. Every query is semantically tailored to what is being looked for, arriving at better results then it is currently possible using available automated categorisation systems

Semantic component selection

The means of locating information quickly and efficiently is a growing area of research. However the real challenge is not related to locating bits of information, but finding those that are relevant. Relevant information resides within unstructured ‘natural’ text. However, understanding natural text and judging information relevancy is a challenge. The challenge is partially addressed by use of semantic models and reasoning approaches that allow categorisation and (within limited fashion) provide understanding of this information. Nevertheless, many such methods are dependent on expert input and, consequently, are expensive to produce and do not scale. Although automated solutions exist, thus far, these have not been able to approach accuracy levels achievable through use of expert input. This thesis presents SemaCS - a novel nondomain specific automated framework of categorising and searching natural text. SemaCS does not rely on expert input; it is based on actual data being searched and statistical semantic distances between words. These semantic distances are used to perform basic reasoning and semantic query interpretation. The approach was tested through a feasibility study and two case studies. Based on reasoning and analyses of data collected through these studies, it can be concluded that SemaCS provides a domain independent approach of semantic model generation and query interpretation without expert input. Moreover, SemaCS can be further extended to provide a scalable solution applicable to large datasets (i.e. World Wide Web). This thesis contributes to the current body of knowledge by establishing, adapting, and using novel techniques to define a generic selection/categorisation framework. Implementing the framework outlined in the thesis improves an existing algorithm of semantic distance acquisition. Finally, as a novel approach to the extraction of semantic information is proposed, there exists a positive impact on Information Retrieval domain and, specifically, on Natural Language Processing, word disambiguation and Web/Intranet search.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Semantic component selection

The means of locating information quickly and efficiently is a growing area of research. However the real challenge is not related to locating bits of information, but finding those that are relevant. Relevant information resides within unstructured ‘natural’ text. However, understanding natural text and judging information relevancy is a challenge. The challenge is partially addressed by use of semantic models and reasoning approaches that allow categorisation and (within limited fashion) provide understanding of this information. Nevertheless, many such methods are dependent on expert input and, consequently, are expensive to produce and do not scale. Although automated solutions exist, thus far, these have not been able to approach accuracy levels achievable through use of expert input. This thesis presents SemaCS - a novel nondomain specific automated framework of categorising and searching natural text. SemaCS does not rely on expert input; it is based on actual data being searched and statistical semantic distances between words. These semantic distances are used to perform basic reasoning and semantic query interpretation. The approach was tested through a feasibility study and two case studies. Based on reasoning and analyses of data collected through these studies, it can be concluded that SemaCS provides a domain independent approach of semantic model generation and query interpretation without expert input. Moreover, SemaCS can be further extended to provide a scalable solution applicable to large datasets (i.e. World Wide Web). This thesis contributes to the current body of knowledge by establishing, adapting, and using novel techniques to define a generic selection/categorisation framework. Implementing the framework outlined in the thesis improves an existing algorithm of semantic distance acquisition. Finally, as a novel approach to the extraction of semantic information is proposed, there exists a positive impact on Information Retrieval domain and, specifically, on Natural Language Processing, word disambiguation and Web/Intranet search

Semantic distance acquisition in SemaCS

Search functionality and technology is a growing area of research. However, simple search approaches are still frequently used. A simple keyword or thesauri-based search is efficient and can be easily scaled. However, keyword-based search cannot be used to infer what may or may not be relevant to the user and thesauri, or any other expert generated model, is expensive to produce and tends to be of limited applicability. Semantic Component Selection (SemaCS) approach is not tied to any specific domain and does not rely on expert input. SemaCS is based on actual data and statistical semantic distances between words. Information on semantic distances is used for searching and for automated generation of domain model taxonomy. This paper presents SemaCS's means of acquiring these semantic distances - mNGD (2) - and its initial evaluation