Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for Web ranking algorithms. In fact, there are still many situations in which the users are presented with imprecise or very poor results. One of the key difficulties is the fact that users usually submit very short and ambiguous queries, and they do not fully specify their information needs. That is, it is necessary to improve the query formation process if better answers are to be provided. In this work we propose a novel concept-based query expansion technique, which allows disambiguating queries submitted to search engines. The concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from query relations mined using association rules. The concepts related to the current query are then shown to the user who selects the one concept that he interprets is most related to his query. This concept is used to expand the original query and the expanded query is processed instead. Using a Web test collection, we show that our approach leads to gains in average precision figures of roughly 32%. Further, if the user also provides information on the type of relation between his query and the selected concept, the gains in average precision go up to roughly 52%.

Search engines process queries conjunctively to restrict the size of the answer set. Further, it is not rare to observe a mismatch between the vocabulary used in the text of Web pages and the terms used to compose the Web queries. The combination of these two features might lead to irrelevant query results, particularly in the case of more specific queries composed of three or more terms. To deal with this problem we propose a new technique for automatically structuring Web queries as a set of smaller subqueries. To select representative subqueries we use information on their distributions in the document collection. This can be adequately modeled using the concept of maximal termsets derived from the formalism of association rules theory. Experimentation shows that our technique leads to improved results. For the TREC-8 test collection, for instance, our technique led to gains in average precision of roughly 28% with regard to a BM25 ranking formula.

This work presents a new approach for ranking documents in the vector space model. The novelty lies in two fronts. First, patterns of term co-occurrence are taken into account and are processed efficiently. Second, term weights are generated using a data mining technique called association rules. This leads to a new ranking mechanism called the set-based vector model . The components of our model are no longer index terms but index termsets, where a termset is a set of index terms. Termsets capture the intuition that semantically related terms appear close to each other in a document. They can be efficiently obtained by limiting the computation to small passages of text. Once termsets have been computed, the ranking is calculated as a function of the termset frequency in the document and its scarcity in the document collection. Experimental results show that the set-based vector model improves average precision for all collections and query types evaluated, while keeping computational costs small. For the 2-gigabyte TREC-8 collection, the set-based vector model leads to a gain in average precision figures of 14.7% and 16.4% for disjunctive and conjunctive queries, respectively, with respect to the standard vector space model. These gains increase to 24.9% and 30.0%, respectively, when proximity information is taken into account. Query processing times are larger but, on average, still comparable to those obtained with the standard vector model (increases in processing time varied from 30% to 300%). Our results suggest that the set-based vector model provides a correlation-based ranking formula that is effective with general collections and computationally practical.

1 Introduction The field of knowledge discovery and data mining (KDD), spurred by advances in data collection technology, is concerned with the process of deriving interesting and useful patterns from large datasets. The KDD process is computational and data-intensive and is inherently interactive and iterative in nature. In fact, interactivity is often the key to facilitating effective data understanding and knowledge discovery. In such an environment, response time is crucial because lengthy time delay between responses of consecutive user requests can disturb the flow of human perception and formation of insight. The task of guaranteeing quick response times is more complicated in dynamic datasets, where there is a constant influx of data. Changes to the data can invalidate existing patterns or introduce new. Simply re-executing algorithms from scratch when a database is updated can result in an explosion in the computational and I/O resources required. What is needed is a way to process the data incrementally and update the information that is gleaned while being cognizant of the interactive requirements of the process. In this paper we present such an approach for a key data mining task: association rule mining.

The objective of this paper is to present a new technique for computing term weights for index terms, which leads to a new ranking mechanism, referred to as set-based model. The components in our model are no longer terms, but termsets. The novelty is that we compute term weights using a data mining technique called association rules, which is time efficient and yet yields nice improvements in retrieval effectiveness. The set-based model function for computing the similarity between a document and a query considers the termset frequency in the document and its scarcity in the document collection. Experimental results show that our model improves the average precision of the answer set for all three collections evaluated. For the TReC-3 collection, our set-based model led to a gain, relative to the standard vector space model, of 37% in average precision curves and of 57% in average precision for the top 10 documents. Like the vector space model, the set-based model has time complexity that is linear in the number of documents in the collection.