Clustering the information space using top-ranking sentences : a study of user interaction

By considering sentences selected by a query-biased sentence extraction model from the top-retrieved documents, we create a personalised information space which is characterised by the presence of search terms. We cluster this information space, and enable searchers to interact with the resulting clusters. In order to examine whether users can recognise, and benefit from, the clustered organisation, we compare user interaction and performance between an actual clustering and a pseudo-clustering of the information space for completing information seeking tasks. The results provide evidence for the utility and meaningfulness of the clustered organisation

Semantics and result disambiguation for keyword search on tree data

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them. Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced

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 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

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

Abstract Deciphering cluster representations

There are several recent studies that propose search output clustering as an alternative representation method to ranked output. Users are provided with cluster representations instead of lists of titles and invited to make decisions on groups of documents. This paper discusses the di culties involved in representing clusters for usersÕ evaluation in a concise but easily interpretable form. The discussion is based on ®ndings and user feedback from a user study investigating the e€ectiveness of search output clustering. The overall impression created by the experiment results and usersÕ feedback is that clusters cannot be relied on to consistently produce meaningful document groups that can easily be recognised by the users. They als