Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results

The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.

Temporal PageRank

The our reality is characterized by a constant progress and, to follow that, people need to stay up to date on the events. In a world with a lot of existing news, search for the ideal ones may be difficult, because the obstacles that make it arduous will be expanded more and more over time, due to the enrichment of data. In response, a great help is given by Information Retrieval, an interdisciplinary branch of computer science that deals with the management and the retrieval of the information. An IR system is developed to search for contents, contained in a reference dataset, considered relevant with respect to the need expressed by an interrogative query. To satisfy these ambitions, we must consider that most of the developed IR systems rely solely on textual similarity to identify relevant information, defining them as such when they include one or more keywords expressed by the query. The idea studied here is that this is not always sufficient, especially when it's necessary to manage large databases, as is the web. The existing solutions may generate low quality responses not allowing, to the users, a valid navigation through them. The intuition, to overcome these limitations, has been to define a new concept of relevance, to differently rank the results. So, the light was given to Temporal PageRank, a new proposal for the Web Information Retrieval that relies on a combination of several factors to increase the quality of research on the web. Temporal PageRank incorporates the advantages of a ranking algorithm, to prefer the information reported by web pages considered important by the context itself in which they reside, and the potential of techniques belonging to the world of the Temporal Information Retrieval, exploiting the temporal aspects of data, describing their chronological contexts. In this thesis, the new proposal is discussed, comparing its results with those achieved by the best known solutions, analyzing its strengths and its weaknesses.