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Concept-Based Interactive Query Expansion ∗ Concept-Based Interactive Query Expansion ∗ Bruno M. Fonseca12 Paulo Golgher2 Bruno Pôssas12 [email protected] [email protected] [email protected] Berthier Ribeiro-Neto1 2 Nivio Ziviani1 [email protected] [email protected] ABSTRACT back, web searching. Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for 1. INTRODUCTION Web ranking algorithms. In fact, there are still many situations in The Web is an innovation that has modified the way we learn, which the users are presented with imprecise or very poor results. work and live. The novelty lies not only on the freedom to publish, One of the key difficulties is the fact that users usually submit very but also in the almost universal communication facilities. It marks short and ambiguous queries, and they do not fully specify their in- formation needs. That is, it is necessary to improve the query for- the beginning of a new era, of a new society, started by what we mation process if better answers are to be provided. In this work we may call the information revolution. In these new times, the volume of information that can be ac- propose a novel concept-based query expansion technique, which cessed at low cost and high convenience is mind boggling. To illus- allows disambiguating queries submitted to search engines. The trate, Google1 advertised indexing more than 8 billion Web pages concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from in 2004. And this volume of information tends to become even query relations mined using association rules. The concepts related larger. As a result, information of critical value appears frequently to the current query are then shown to the user who selects the one mixed in with other pieces of information that are not of interest. Because the volume of data is now much larger and frequently concept that he interprets is most related to his query. This con- poorly organized, finding useful or relevant information might be cept is used to expand the original query and the expanded query rather difficult. In fact, this is the case even with modern search is processed instead. Using a Web test collection, we show that our approach leads to gains in average precision figures of roughly engines that take advantage of link analysis to identify popular 32%. Further, if the user also provides information on the type of sources of relevant information. relation between his query and the selected concept, the gains in It is general consensus that Web users provide poor specifications of their information needs in general. Either because they are in a average precision go up to roughly 52%. hurry or because they do not understand the search process well, Web users frequently specify short queries with little or no context Categories and Subject Descriptors information associated with them. Further, they are the sole masters H.2.8 [Database Management]: Database Applications—Data Min- in the moment of deciding what is relevant and what is not. The ing; H.3.3 [Information Storage and Retrieval]: Information Search combination of these two factors suggests that interacting with the and Retrieval—Relevance Feedback, Query Formulation. user is a key step if the precision of the results is to be improved. In other words, to improve the user information search experience General Terms we have to ask more information from him. In this work we focus our attention on the problem of improving Theory, algorithms, experimentation, performance. the process of query formation. Our approach is to provide high level suggestions for expanding the original user query with addi- Keywords tional context. This is done using information extracted from a log Association rule mining, interactive query expansion, user feed- of past queries – an important piece of evidence that is generated in abundance by search engines. ∗1 Federal University of Minas Gerais: 30161-970 Belo Horizonte- Our approach is composed of four basic steps. First, compute MG, Brazil past queries related to the current query using the framework of 2 Akwan Information Technologies: Av. Abraao˜ Caram, 430, 40 andar - Pampulha, Belo Horizonte-MG, Brazil association rules. Second, build a query relations graph encom- passing all these query relations. Third, identify subsets of queries that are strongly related among themselves. Each of these subsets is viewed as a concept, an entity of the world that might be related to the current query. Fourth, show these related concepts to the Permission to make digital or hard copies of all or part of this work for user, collect his feedback on the most related concept, expand the personal or classroom use is granted without fee provided that copies are original query with the concept selected, and execute the expanded not made or distributed for profit or commercial advantage and that copies query instead. bear this notice and the full citation on the first page. To copy otherwise, to Our concepts are labelled with subsets of past queries such as, republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. 1 Copyright 200X ACM X-XXXXX-XX-X/XX/XX ...$5.00. http://www.google.com for instance, “jaguar, lion, tiger”. Since these labels were speci- an original query is run using conventional information retrieval fied by the users themselves, they are short in nature. We say that techniques [2]. Then, related terms are extracted from the top doc- the concepts we use are of a high level nature. By this, we mean uments that are returned in response to the original query using sta- that they are simple, intuitive, and can be interpreted by quick in- tistical heuristics. This approach has been shown to be effective on spection. Because of this, we claim that our concept-based feed- some collections, but results on large collections of web data have back approach is intuitive and likely to be engaged by Web users in been mixed. For example, the work in [4], using the Okapi ap- general. proach to ranking, have found that the standard parameters are in- To validate our approach we ran experiments using a test collec- appropriate for web data, and even with the best parameters found tion composed of more than 14 million Web pages (extracted from by tuning to that data and queries, the performance gains are in- the “.br” domain). Our query log was divided into two parts: a first significant. part composed of 182,017 sample queries from January of 2004 to The work in [9] was one of the first that used past user queries December of 2004, and a second part composed of 100 test queries to improve automatic query expansion. They use the results of past from January of 2005 to February of 2005. The first part was used queries to form affinity pools, from which expansion terms are then to compute an all-queries relation graph. The second part was used selected. The process works as follows: for a query that is to be to test our concept-based expansion method. expanded, up to three past queries that are highly similar to the The results were compiled for the 100 test queries and were current query are identified and the top 100 documents that were quantified using standard 11-point recall-precision curves. As base- returned for each of these past queries are then merged, forming the line, we adopted a modern ranking function (provided by the TodoBR2 affinity pool. Individual terms are then selected from the top ranked search engine) that combines information on the text of the docu- documents using a TF-IDF term scoring algorithm. Their method ments, on the anchor texts, and on link analysis. We investigated improves relative average precision for the TREC-5 collection by whether the expansion of the current query with the feedback pro- around 15%. vided by the user, through the selection of a single most related The work in [3] chooses expansion terms from past user queries concept, leads to improved results. Our measures indicate that av- directly, rather than using them to construct sets of full text doc- erage precision improved for all recall levels and that the average uments from which terms are then selected. The method consists gain in precision was of 32.26%. of three phases: ranking the original query against the collection Following, we also asked the user to specify the nature of the re- of documents; extracting additional query terms from the highly lation between the current query and the concept selected. We were ranked items; then ranking the new query against the collection. interested in obtaining extra information from the user on whether The results show relative improvements over unexpanded full text this relation was of a synonym, specialization, generalization, or retrieval of 26%–29%. association type. As before, we claim that this can be done in intu- In [7] they suggest a method for finding relations between queries itive and natural fashion (for instance, it is simple to determine that and phrases of documents based on query logs. They use the hy- “cars” provide a generalization for “ferrari”). Most important, we pothesis that the click through information available on search en- were interested in determining whether this extra information could gine logs represents an evidence of relation between queries and be used to obtain improved results. Indeed this was the case, use of documents chosen to be visited by users. This evidence is called this extra information improved the average gain in precision from cross-reference of documents.
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