Knowledge-directed intelligent information retrieval for research funding.
| dc.contributor.advisor | Warren, Peter R. | |
| dc.contributor.author | Hansraj, Sanjith. | |
| dc.date.accessioned | 2011-06-27T11:20:53Z | |
| dc.date.available | 2011-06-27T11:20:53Z | |
| dc.date.created | 2001 | |
| dc.date.issued | 2001 | |
| dc.description | Thesis (M.Sc.)- University of Natal, Pietermaritzburg, 2001. | en_US |
| dc.description.abstract | Researchers have always found difficulty in attaining funding from the National Research Foundation (NRF) for new research interests. The field of Artificial Intelligence (AI) holds the promise of improving the matching of research proposals to funding sources in the area of Intelligent Information Retrieval (IIR). IIR is a fairly new AI technique that has evolved from the traditional IR systems to solve real-world problems. Typically, an IIR system contains three main components, namely, a knowledge base, an inference engine and a user-interface. Due to its inferential capabilities. IIR has been found to be applicable to domains for which traditional techniques, such as the use of databases, have not been well suited. This applicability has led it to become a viable AI technique from both, a research and an application perspective. This dissertation concentrates on researching and implementing an IIR system in LPA Prolog, that we call FUND, to assist in the matching of research proposals of prospective researchers to funding sources within the National Research Foundation (NRF). FUND'S reasoning strategy for its inference engine is backward chaining that carries out a depth-first search over its knowledge representation structure, namely, a semantic network. The distance constraint of the Constrained Spreading Activation (CSA) technique is incorporated within the search strategy to help prune non-relevant returns by FUND. The evolution of IIR from IR was covered in detail. Various reasoning strategies and knowledge representation schemes were reviewed to find the combination that best suited the problem domain and programming language chosen. FUND accommodated a depth 4, depth 5 and an exhaustive search algorithm. FUND'S effectiveness was tested, in relation to the different searches with respect to their precision and recall ability and in comparison to other similar systems. FUND'S performance in providing researchers with better funding advice in the South African situation proved to be favourably comparable to other similar systems elsewhere. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10413/3087 | |
| dc.language.iso | en | en_US |
| dc.subject | Artificial intelligence--Data processing. | en_US |
| dc.subject | Prolog (Computer program language) | en_US |
| dc.subject | Information retrieval. | en_US |
| dc.subject | Information storage and retrieval systems--Research. | en_US |
| dc.subject | Theses--Computer science. | en_US |
| dc.title | Knowledge-directed intelligent information retrieval for research funding. | en_US |
| dc.type | Thesis | en_US |