Intelligent personalized approaches for semantic search and query expansion

University of Technology Sydney. Faculty of Engineering and Information Technology.In today’s highly advanced technological world, the Internet has taken over all aspects of human life. Many services are advertised and provided to the users through online channels. The user looks for services and obtains them through different search engines. To obtain the best results that meet the needs and requirements of the users, researchers have extensively studied methods such as different personalization methods by which to improve the performance and efficiency of the retrieval process. A key part of the personalization process is the generation of user models. The most commonly used user models are still rather simplistic, representing the user as a vector of ratings or using a set of keywords. Recently, semantic techniques have had a significant importance in the field of personalized querying and personalized web search engines. This thesis focuses on both processes of personalized web search engines, first the reformulation of queries and second ranking query results. The importance of personalized web search lies in its ability to identify users' interests based on their personal profiles. This work contributes to personalized web search services in three aspects. These contributions can be summarized as follows: First, it creates user profiles based on a user’s browsing behaviour, as well as the semantic knowledge of a domain ontology, aiming to improve the quality of the search results. However, it is not easy to acquire personalized web search results, hence one of the problems that is encountered in this approach is how to get a precise representation of the user interests, as well as how to use it to find search results. The second contribution builds on the first contribution. A personalized web search approach is introduced by integrating user context history into the information retrieval process. This integration process aims to provide search results that meet the user’s needs. It also aims to create contextual profiles for the user based on several basic factors: user temporal behaviour during browsing, semantic knowledge of a specific domain ontology, as well as an algorithm based on re-ranking the search results. The previous solutions were related to the re-ranking of the returned search results to match the user’s requirements. The third contribution includes a comparison of three-term weight methods in personalized query expansion. This model has been built to incorporate both latent semantics and weighting terms. Experiments conducted in the real world to evaluate the proposed personalized web search approach; show promising results in the quality of reformulation and re-ranking processes compared to Google engine techniques

Interpreting and Answering Keyword Queries using Web Knowledge Bases

Many keyword queries issued to Web search engines target information about real world entities, and interpreting these queries over Web knowledge bases can allow a search system to provide exact answers to keyword queries. Such an ability provides a useful service to end users, as their information need can be directly addressed and they need not scour textual results for the desired information. However, not all keyword queries can be addressed by even the most comprehensive knowledge base, and therefore equally important is the problem of recognizing when a reference knowledge base is not capable of modelling the keyword query's intention. This may be due to lack of coverage of the knowledge base or lack of expressiveness in the underlying query representation formalism. This thesis presents an approach to computing structured representations of keyword queries over a reference knowledge base. Keyword queries are annotated with occurrences of semantic constructs by learning a sequential labelling model from an annotated Web query log. Frequent query structures are then mined from the query log and are used along with the annotations to map keyword queries into a structured representation over the vocabulary of a reference knowledge base. The proposed approach exploits coarse linguistic structure in keyword queries, and combines it with rich structured query representations of information needs. As an intermediate representation formalism, a novel query language is proposed that blends keyword search with structured query processing over large Web knowledge bases. The formalism for structured keyword queries combines the flexibility of keyword search with the expressiveness of structures queries. A solution to the resulting disambiguation problem caused by introducing keywords as primitives in a structured query language is presented. Expressions in our proposed language are rewritten using the vocabulary of the knowledge base, and different possible rewritings are ranked based on their syntactic relationship to the keywords in the query as well as their semantic coherence in the underlying knowledge base. The problem of ranking knowledge base entities returned as a query result is also explored from the perspective of personalized result ranking. User interest models based on entity types are learned from a Web search session by cross referencing clicks on URLs with known entity homepages. The user interest model is then used to effectively rerank answer lists for a given user. A methodology for evaluating entity-based search engines is also proposed and empirically evaluated

A relevance-focused search application for personalised ranking model

The assumption that users’ profiles can be exploited by employing their implicit feedback for query expansion through a conceptual search to index documents has been proven in previous research. Several successful approaches leading to an improvement in the accuracy of personalised search results have been proposed. This paper extends existing approaches and combines the keyword-based and semantic-based features in order to provide further evidence of relevance-focused search application for Personalised Ranking Model (PRM). A description of the hybridisation of these approaches is provided and various issues arising in the context of computing the similarity between users’ profiles are discussed. As compared to any traditional search system, the superiority of our approach lies in pushing significantly relevant documents to the top of the ranked lists. The results were empirically confirmed through human subjects who conducted several real-life Web searches

A Relation-Based Page Rank Algorithm for Semantic Web Search Engines

With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing overcoming this limitation. Several search engines have been proposed, which allow increasing information retrieval accuracy by exploiting a key content of Semantic Web resources, that is, relations. However, in order to rank results, most of the existing solutions need to work on the whole annotated knowledge base. In this paper, we propose a relation-based page rank algorithm to be used in conjunction with Semantic Web search engines that simply relies on information that could be extracted from user queries and on annotated resources. Relevance is measured as the probability that a retrieved resource actually contains those relations whose existence was assumed by the user at the time of query definitio

The quest for information retrieval on the semantic web

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research

Using TREC for cross-comparison between classic IR and ontology-based search models at a Web scale

The construction of standard datasets and benchmarks to evaluate ontology-based search approaches and to compare then against baseline IR models is a major open problem in the semantic technologies community. In this paper we propose a novel evaluation benchmark for ontology-based IR models based on an adaptation of the well-known Cranfield paradigm (Cleverdon, 1967) traditionally used by the IR community. The proposed benchmark comprises: 1) a text document collection, 2) a set of queries and their corresponding document relevance judgments and 3) a set of ontologies and Knowledge Bases covering the query topics. The document collection and the set of queries and judgments are taken from one of the most widely used datasets in the IR community, the TREC Web track. As a use case example we apply the proposed benchmark to compare a real ontology-based search model (Fernandez, et al., 2008) against the best IR systems of TREC 9 and TREC 2001 competitions. A deep analysis of the strengths and weaknesses of this benchmark and a discussion of how it can be used to evaluate other ontology-based search systems is also included at the end of the paper