Information retrieval (IR) has been developed to provide practical solutions to people's need to find the desired information...

Abstract. In classic Information Retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem, known as “term mismatch”, has been recognised for a long time by the Information Retrieval community and a number of possible solutions have been proposed. Here I present a preliminary investigation into a new class of retrieval models that attempt to solve the term mismatch problem by exploiting complete or partial knowledge of term similarity in the term space. The use of term similarity enables to enhance classic retrieval models by taking into account non-matching terms. The theoretical advantages and drawbacks of these models are presented and compared with other models tackling the same problem. A preliminary experimental investigation into the performance gain achieved by exploiting term similarity with the proposed models is presented and discussed.

Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.

this paper: "22 year old with hyperthermia, leukocytosis, increased intracranial pressure, and central herniation. Cerebral edema secondary to infection, diagnosis and treatment." The first part of the query is a brief description of the patient; the second part is the information need

This paper presents a new approach to determine the senses of words in queries by using WordNet. In our approach, noun phrases in a query are determined first. For each word in the query, information associated with it, including its synonyms, hyponyms, hypernyms, definitions of its synonyms and hyponyms, and its domains, can be used for word sense disambiguation. By comparing these pieces of information associated with the words which form a phrase, it may be possible to assign senses to these words. If the above disambiguation fails, then other query words, if exist, are used, by going through exactly the same process. If the sense of a query word cannot be determined in this manner, then a guess of the sense of the word is made, if the guess has at least 50 % chance of being correct. If no sense of the word has 50 % or higher chance of being used, then we apply a Web search to assist in the word sense disambiguation process. Experimental results show that our approach has 100% applicability and 90 % accuracy on the most recent robust track of TREC collection of 250 queries. We combine this disambiguation algorithm to our retrieval system to examine the effect of word sense disambiguation in text retrieval. Experimental results show that the disambiguation algorithm together with other components of our retrieval system yield a result which is 13.7 % above that produced by the same system but without the disambiguation, and 9.2 % above that produced by using Lesk’s algorithm. Our retrieval effectiveness is 7 % better than the best reported result in the literature.

This paper presents new methods for automatically integrating, summarizing and evaluating multimedia knowledge. These are essential for multimedia applications to efficiently and coherently deal with multimedia knowledge at different abstraction levels such as perceptual and semantic knowledge (e.g., image clusters and word senses, respectively). The proposed methods include automatic techniques (1) for interrelating the concepts in the multimedia knowledge using probabilistic Bayesian learning, (2) for reducing the size of multimedia knowledge by clustering the concepts and collapsing the relationships among the clusters, and (3) for evaluating the quality of multimedia knowledge using notions from information and graph theory. Experiments show the potential of knowledge integration techniques for improving the knowledge quality, the importance of good concept distance measures for clustering and summarizing knowledge, and the usefulness of automatic measures for comparing the effects of different processing techniques on multimedia knowledge.

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In this paper we propose an integration of a selforganizing map and semantic networks from WordNet for a text classification task using the new Reuters news corpus. This neural model is based on significance vectors and benefits from the presentation of document clusters. The Hypernym relation in WordNet supplements the neural model in classification. We also analyse the relationships of news headlines and their contents of the new Reuters corpus by a series of experiments. This hybrid approach of neural selforganization and symbolic hypernym relationships is successful to achieve good classification rates on 100,000 full-text news articles. These results demonstrate that this approach can scale up to a large real-world task and show a lot of potential for text classification.

which utilizes a “conceptual interlingua ” representation of documents and queries. The current CINDOR research system uses a conceptual interlingua constructed around the Princeton WordNet, which we are mapping into French and Spanish. The use of an interlingual representation of documents and queries allows us to perform retrieval on any combination of supported languages, rather than having to rely on pairwise translations, while the use of a resource like WordNet allows us to match equivalent terms (including synonyms) across languages. Although the analysis of our TREC-7 results is clouded somewhat by the kinds of system errors which inevitably occur in a first-time evaluation over large TREC corpora, our evaluation of the conceptual interlingua approach suggests that it provides highly effective cross-language retrieval performance. In particular, we notice that the CINDOR system achieves cross-language retrieval results equivalent in many cases to corresponding monolingual queries, without the loss in retrieval precision observed in many other approaches to cross-language retrieval. Future work on the CINDOR system, which was evaluated here in its research prototype form, will focus on improving further the coverage of our conceptual interlingua resources and the efficiency of our document processing modules. We are also investigating the construction of an interlingual resource of

Guarino for their prompt responses to my questions. Third, to all my colleagues and friends in the Department of Spatial Information Science I would like to thank you for sharing the good and bad moments of my study life. I feel fortunate for having being part of a friendly environment that made my Ph.D. program an enjoyable and unforgettable experience. iii Fourth, I thank the support and funding from the University of Concepcin, Chile, and the initial funding from the Fulbright foundation. Further funding from the National Center of Geographic Information and Analysis, the National Imagery and Mapping Agency, and Lockheed Martin are gratefully acknowledged. Most important, I thank the continuous support, love, and patience of Christian and Alicia. This long journey would not have been possible without them. iv Table of Contents Acknowledgments .......................................................................................................ii List of Figu