Automatic semantic annotation of Web documents

Ontologies are the most important construct of the Semantic Web. From the first attempt of using simplified RDF syntax to the advanced features of the OWL languages, ontologies have arisen as the most viable technology offering solutions to integrate various Web resources into a more intelligent Web. The work presented in this thesis is a contribution to the new generation of the Web, which should be readable and interpreted not only by humans but also by machines, such as software agents. In order to allow ontologies to achieve their role of "animating" the traditional Web into this next generation Web, it is essential to find an efficient way to map all existent Web resources onto their corresponding ontology classes. In this thesis, we propose an approach for automatic semantic annotation of Web documents which is an effective way to make the Semantic Web a reality. Such an integrated Web would greatly improve the accuracy of search engines, bring a new generation of intelligent Web services, push the limits of multi-agent technologies and improve many other areas of human activity that we cannot even imagine today. Considering the size and the speed of the growing Web, it is clear that this task cannot be achieved manually. Semi-automatic and automatic annotations of Web documents using statistical text classification methods seem to be the most promising solution. This work is focused on an approach based on Naive Bayes text classification adapted to some characteristics that are particular to Web documents. A complete software solution is developed to allow testing feasibility of such an approach. Furthermore, different variations of the text classification algorithms are tested and analysed in order to identify the most optimal approach to semantically annotate Web documents. Notably, the usage of Web documents hierarchy is explored as an option to improve the accuracy of semi-automatic and automatic annotations of Web documents. The results of each tested method are presented and commented. Finally, some aspects that could possibly be improved or approached in a different way are identified for future work

The Effectiveness of Query-Based Hierarchic Clustering of Documents for Information Retrieval

Hierarchic document clustering has been applied to Information Retrieval (IR) for over three decades. Its introduction to IR was based on the grounds of its potential to improve the effectiveness of IR systems. Central to the issue of improved effectiveness is the Cluster Hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other, and therefore tend to appear in the same clusters. However, previous research has been inconclusive as to whether document clustering does bring improvements. The main motivation for this work has been to investigate methods for the improvement of the effectiveness of document clustering, by challenging some assumptions that implicitly characterise its application. Such assumptions relate to the static manner in which document clustering is typically performed, and include the static application of document clustering prior to querying, and the static calculation of interdocument associations. The type of clustering that is investigated in this thesis is query-based, that is, it incorporates information from the query into the process of generating clusters of documents. Two approaches for incorporating query information into the clustering process are examined: clustering documents which are returned from an IR system in response to a user query (post-retrieval clustering), and clustering documents by using query-sensitive similarity measures. For the first approach, post-retrieval clustering, an analytical investigation into a number of issues that relate to its retrieval effectiveness is presented in this thesis. This is in contrast to most of the research which has employed post-retrieval clustering in the past, where it is mainly viewed as a convenient and efficient means of presenting documents to users. In this thesis, post-retrieval clustering is employed based on its potential to introduce effectiveness improvements compared both to static clustering and best-match IR systems. The motivation for the second approach, the use of query-sensitive measures, stems from the role of interdocument similarities for the validity of the cluster hypothesis. In this thesis, an axiomatic view of the hypothesis is proposed, by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other which is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Past research has attributed failure to validate the hypothesis for a document collection to characteristics of the collection. Contrary to this, the view proposed in this thesis suggests that failure of a document set to adhere to the hypothesis is attributed to the assumptions made about interdocument similarity. This thesis argues that the query determines the context and the purpose for which the similarity between documents is judged, and it should therefore be incorporated in the similarity calculations. By taking the query into account when calculating interdocument similarities, co-relevant documents can be "forced" to be more similar to each other. This view challenges the typically static nature of interdocument relationships in IR. Specific formulas for the calculation of query-sensitive similarity are proposed in this thesis. Four hierarchic clustering methods and six document collections are used in the experiments. Three main issues are investigated: the effectiveness of hierarchic post-retrieval clustering which uses static similarity measures, the effectiveness of query-sensitive measures at increasing the similarity of pairs of co-relevant documents, and the effectiveness of hierarchic clustering which uses query-sensitive similarity measures. The results demonstrate the effectiveness improvements that are introduced by the use of both approaches of query-based clustering, compared both to the effectiveness of static clustering and to the effectiveness of best-match IR systems. Query-sensitive similarity measures, in particular, introduce significant improvements over the use of static similarity measures for document clustering, and they also significantly improve the structure of the document space in terms of the similarity of pairs of co-relevant documents. The results provide evidence for the effectiveness of hierarchic query-based clustering of documents, and also challenge findings of previous research which had dismissed the potential of hierarchic document clustering as an effective method for information retrieval