When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.

With the increasing amount of data and the need to integrate data from multiple data sources, one of the challenging issues is to identify near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pair of records such that their similarities are no less than a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the token ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. We have also studied the implementation of our proposed algorithm in stand-alone and RDBMSbased settings. Experimental results show our proposed algorithms can outperforms previous algorithms on several real datasets.

Databases (DB) and information retrieval (IR) have evolved as separate fields. However, modern applications such as customer support, health care, and digital libraries require capabilities for both data and text management. In such settings, traditional DB queries, in SQL or XQuery, are not flexible enough to handle applicationspecific scoring and ranking. IR systems, on the other hand, lack efficient support for handling structured parts of the data and metadata, and do not give the application developer adequate control over the ranking function. This paper analyzes the requirements of advanced text- and data-rich applications for an integrated platform. The core functionality must be manageable, and the API should be easy to program against. A particularly important issue that we highlight is how to reconcile flexibility in scoring and ranking models with optimizability, in order to accommodate a wide variety of target applications efficiently. We discuss whether such a system needs to be designed from scratch, or can be incrementally built on top of existing architectures. The results of our analyses are cast into a series of challenges to the DB and IR communities.

Data Cleaning is an important process that has been at the center of research interest in recent years. An important end goal of effective data cleaning is to identify the relational tuple or tuples that are “most related ” to a given query tuple. Various techniques have been proposed in the literature for efficiently identifying approximate matches to a query string against a single attribute of a relation. In addition to constructing a ranking (i.e., ordering) of these matches, the techniques often associate, with each match, scores that quantify the extent of the match. Since multiple attributes could exist in the query tuple, issuing approximate match operations for each of them separately will effectively create a number of ranked lists of the relation tuples. Merging these lists to identify a final ranking and scoring, and returning the top-K tuples, is a challenging task. In this paper, we adapt the well-known footrule distance (for merging ranked lists) to effectively deal with scores. We study efficient algorithms to merge rankings, and produce the top-K tuples, in a declarative way. Since techniques for approximately matching a query string against a single attribute in a relation are typically best deployed in a database, we introduce and describe two novel algorithms for this problem and we provide SQL specifications for them. Our experimental case study, using real application data along with a realization of our proposed techniques on a commercial data base system, highlights the benefits of the proposed algorithms and attests to the overall effectiveness and practicality of our approach. 1

We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-commerce, learning from experts, analysis of population preference sampling, committee decision making and more. We improve recent constant factor approximation algorithms for aggregation of full rankings and generalize them to partial rankings. Our algorithms improved constant factor approximation with respect to all metrics discussed in Fagin et al’s recent important work on comparing partial rankings. We pay special attention to two important types of partial rankings: the well-known top-m lists and the more general p-ratings which we define. We provide first evidence for hardness of aggregating them for constant m, p.

To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, because of their far more complex search space, most existing techniques focus on simple 1:1 matchings. To tackle this challenge, this article takes a conceptually novel approach by viewing schema matching as correlation mining, for our task of matching Web query interfaces to integrate the myriad databases on the Internet. On this “deep Web, ” query interfaces generally form complex matchings between attribute groups (e.g., {author} corresponds to {first name, last name} in the Books domain). We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships: grouping attributes (e.g., {first name, last name}) tend to be co-present in query interfaces and thus positively correlated. In contrast, synonym attributes are negatively correlated because they rarely co-occur. This insight enables us to discover complex matchings by a correlation mining approach. In particular, we develop the DCM framework, which consists of data preprocessing, dual mining of positive and negative correlations, and finally matching construction. We evaluate the DCM framework on manually extracted interfaces and the results show good accuracy for discovering complex matchings. Further, to automate the

In recent years, the multimedia retrieval community is gradually shifting its emphasis from analyzing one media source at a time to exploring the opportunities of combining diverse knowledge sources from correlated media types and context. This thesis presents a conditional probabilistic retrieval model as a principled framework to combine diverse knowledge sources. An efficient rank-based learning approach has been developed to explicitly model the ranking relations in the learning process. Under this retrieval framework, we overview and develop a number of state-of-the-art approaches for extracting ranking features from multimedia knowledge sources. To incorporate query information in the combination model, this thesis develops a number of query analysis models that can automatically discover mixing structure of the query space based on previous retrieval results. To adapt the combination function on a per query basis, this thesis also presents a probabilistic local context analysis(pLCA) model to automatically leverage additional retrieval sources to improve initial retrieval outputs. All the proposed approaches are evaluated on multimedia retrieval tasks with large-scale video collections as well as meta-search tasks with large-scale text collections. 1

The central problem for many applications in Information Retrieval is ranking and learning to rank is considered as a promising approach for addressing the issue. Ranking SVM, for example, is a state-of-the-art method for learning to rank and has been empirically demonstrated to be effective. In this paper, we study the issue of learning to rank, particularly the approach of using SVM techniques to perform the task. We point out that although Ranking SVM is advantageous, it still has shortcomings. Ranking SVM employs a single hyperplane in the feature space as the model for ranking, which is too simple to tackle complex ranking problems. Furthermore, the training of Ranking SVM is also computationally costly. In this paper, we look at an alternative approach to Ranking SVM, which we call “Multiple Hyperplane Ranker ” (MHR), and make comparisons between the two approaches. MHR takes the divide-and-conquer strategy. It employs multiple hyperplanes to rank instances and finally aggregates the ranking results given by the hyperplanes. MHR contains Ranking SVM as a special case, and MHR can overcome the shortcomings which Ranking SVM suffers from. Experimental results on two information retrieval datasets show that MHR can outperform Ranking SVM in ranking.

A top-k query combines different rankings of the same set of objects and returns the k objects with the highest combined score according to an aggregate function. We bring to light some key observations, which impose two phases that any top-k algorithm, based on sorted accesses, should go through. Based on them, we propose a new algorithm, which is designed to minimize the number of object accesses, the computational cost, and the memory requirements of top-k search. Adaptations of our algorithm for search variants (exact scores, on-line and incremental search, top-k joins, other aggregate functions, etc.) are also provided. Extensive experiments with synthetic and real data show that, compared to previous techniques, our method accesses fewer objects, while being orders of magnitude faster. 1

Tasks of information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be formulated in a context-dependent (also application-, data-, and user-dependent) way. In this paper, we present a novel method, which learns a distance function by capturing the nonlinear relationships among contextual information provided by the application, data, or user. We show that through a process called the “kernel trick, ” such nonlinear relationships can be learned efficiently in a projected space. In addition to using the kernel trick, we propose two algorithms to further enhance efficiency and effectiveness of function learning. For efficiency, we propose a SMO-like solver to achieve O(N 2) learning performance. For effectiveness, we propose using unsupervised learning in an innovative way to address the challenge of lack of labeled data (contextual information). Theoretically, we substantiate that our method is both sound and optimal. Empirically, we demonstrate that our method is effective and useful.