Automatic Query Expansion Research Articles

A New Algorithm of Web Queries Clustering Using User Feedback

Web queries clustering attract great concern recently. It is crucial for capturing frequently asked questions in question-answering system, most popular topics in search engine or automatic query expansion in information retrieval. The paper presents a new algorithm of web queries clustering using user click information in the query logs and applies it into query expansion. Different from previous work, in the new algorithm both word form and semantic information have been taken into considered. Experiments show that the new algorithm works effectively.

A New Model for Measuring Similarity of Web Queries and Its Application in Query Expansion

The similarity of web queries plays an important role in capturing frequently asked questions, most popular topics of search engine or automatic query expansion. Accurate measurement of similarity between queries is crucial. The paper presents a new model for similarity metric of web queries using user logs and applied it into information retrieval for query expansion. Different from previous works, in the new model not only word form, but also semantic information has been taken into account. Experiments show that using the new model in query expansion actually improved recall of 8.1 percent and precision of 9.2 percent, which indicates the good performance.

Optimizing an Arabic Query using Comprehensive Query Expansion Techniques

By querying a search engine, a user probably can get what he or she wants. However, the average number of terms specified by a user in a query is generally two or three [23]. This often leads to several problems. To overcome such problems, various query expansion techniques have been developed. However, none of them are asserted to present the optimal solution, especially in Arabic language because its complex morphological structure. Thus, the main goal of this paper is to optimize Arabic queries using comprehensive combination of these expansion techniques that can be used to improve the expansion process and to get most of the relevant documents for the Arabic user's query. The paper concluded that the developed system improved the recall and precision over couples of separated techniques. This approach takes advantages of both automatic and interactive query expansion techniques because query is expanded automatically and users are involved implicitly for query expansion.

CIDER: Concept-based image diversification, exploration, and retrieval

Many of the approaches to image retrieval on the Web have their basis in text retrieval. However, when searchers are asked to describe their image needs, the resulting query is often short and potentially ambiguous. The solution we propose is to perform automatic query expansion using Wikipedia as the source knowledge base, resulting in a diversification of the search results. The outcome is a broad range of images that represent the various possible interpretations of the query. In order to assist the searcher in finding images that match their specific intentions for the query, we have developed an image organization method that uses both the conceptual information associated with each image, and the visual features extracted from the images. This, coupled with a hierarchical organization of the concepts, provides an interactive interface that takes advantage of the searchers’ abilities to recognize relevant concepts, filter and focus the search results based on these concepts, and visually identify relevant images while navigating within the image space. In this paper, we outline the key features of our image retrieval system (CIDER), and present the results of a preliminary user evaluation. The results of this study illustrate the potential benefits that CIDER can provide for searchers conducting image retrieval tasks.

Temporal and Contextual Evaluation of Background Knowledge Discovery for Short Text Classification

Background Knowledge (BK) plays an essential role in machine learning for short-text and non-topical classification. In this paper the authors present and evaluate two Information Retrieval techniques used to assemble four sets of BK in the past seven years. These sets were applied to classify a commercial corpus of search queries by the apparent age of the user. Temporal and contextual evaluations were used to examine results of various classification scenarios providing insight into choice, significance and range of tuning parameters. The evaluations also demonstrated the impact of the dynamic Web collection on classification results, and the advantages of Automatic Query Expansion (AQE) vs. basic search. The authors discuss other results of this research and its implications on the advancement of short text classification.

A Survey of Automatic Query Expansion in Information Retrieval

The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user’s original query is augmented by new features with a similar meaning. AQE has a long history in the information retrieval community but it is only in the last years that it has reached a level of scientific and experimental maturity, especially in laboratory settings such as TREC. This survey presents a unified view of a large number of recent approaches to AQE that leverage various data sources and employ very different principles and techniques. The following questions are addressed. Why is query expansion so important to improve search effectiveness? What are the main steps involved in the design and implementation of an AQE component? What approaches to AQE are available and how do they compare? Which issues must still be resolved before AQE becomes a standard component of large operational information retrieval systems (e.g., search engines)?

Towards an effective automatic query expansion process using an association rule mining approach

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon test.

Adaptive Ontology-Based Web Information Retrieval

Finding relevant information on the Web is difficult for most users. Although Web search applications are improving, they must be more “intelligent” to adapt to the search domains targeted by queries, the evolution of these domains, and users’ characteristics. In this paper, the authors present the TARGET framework for Web Information Retrieval. The proposed approach relies on the use of ontologies of a particular nature, called adaptive ontologies, for representing both the search domain and a user’s profile. Unlike existing approaches on ontologies, the authors make adaptive ontologies adapt semi-automatically to the evolution of the modeled domain. The ontologies and their properties are exploited for domain specific Web search purposes. The authors propose graph-based data structures for enriching Web data in semantics, as well as define an automatic query expansion technique to adapt a query to users’ real needs. The enriched query is evaluated on the previously defined graph-based data structures representing a set of Web pages returned by a usual search engine in order to extract the most relevant information according to user needs. The overall TARGET framework is formalized using first-order logic and fully tool supported.

A query expansion framework in image retrieval domain based on local and global analysis

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall.

Pseudo-Relevance Feedback Driven for XML Query Expansion

Pseudo-relevance feedback has been perceived as an effective solution for automatic query expansion. However, a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval performance. It is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. Compared with traditional query expansion, XML query expansion needs not only content expansion but also considering structural expansion. This paper presents a solution for both identifying related documents and selecting good expansion information with new content and path constrains. Combined with XML semantic feature, a naive document similarity measurement is proposed in this paper. Based on this, kmedian clustering algorithm is firstly implemented and some related documents are found. Secondly, query expansion is only performed by two steps in the set of related documents, which key phrase extraction algorithm is carried out to expand original query in the first step and the second step is structural expansion based on the expanded key phrases. Finally a full-edged content-structure query expression which can represent user’s intention is formalized. Experimental results on IEEE CS collection show that the proposed method can reduce the topic drift effectively and obtain the better retrieval quality.

On the number of terms used in automatic query expansion

This paper investigates the number of expansion terms to use in automatic query expansion by examining the behavior of eight retrieval systems participating in the NRRC Reliable Information Access Workshop. The results demonstrate that current systems are able to obtain nearly all of the benefit of using a fixed number of expansion terms per topic, but significant additional improvement is possible if systems were able to accurately select the best number of expansion terms on a per topic basis. When optimizing average effectiveness as measured by mean average precision, using a fixed number of terms increases the score a large amount for a small number of topics but has little effect for most topics. The analysis further suggests that when a topic is helped by automatic feedback, the increase is from a set of terms that reinforce each other rather than from the system finding a single excellent term.

Using Windmill Expansion for Document Retrieval

SEMIOTIKS aims to utilise online information to support the crucial decision–making of those military and civilian agencies involved in the humanitarian removal of landmines in areas of conflict throughout the world. An analysis of the type of information required for such a task has given rise to four main areas of research: information retrieval, document annotation, summarisation and visualisation. The first stage of the research has focused on information retrieval, and a new algorithm, “Windmill Expansion” (WE) has been proposed to do this. The algorithm uses retrieval feedback techniques for automated query expansion in order to improve the effectiveness of information retrieval. WE is based on the extraction of human–generated written phases for automated query expansion. Top and Second Level expansion terms have been generated and their usefulness evaluated. The evaluation has concentrated on measuring the degree of overlap between the retrieved URLs. The less the overlap, the more useful the information provided. The Top Level expansion terms were found to provide 90% of useful URLs, and the Second Level 83% of useful URLs. Although there was a decline of useful URLs from the Top Level to the Second Level, the quantity of relevant information retrieved has increased. The originality of SEMIOTIKS lies in its use of the WE algorithm to help non–domain specific experts automatically explore domain words for relevant and precise information retrieval.

Query expansion using UMLS Tools for health information retrieval

AbstractFour new automatic query expansion strategies based on UMLS Metathesaurus are proposed to improve the effectiveness of health information retrieval: String index with Concept expansion (SC), String index with Term expansion (ST), Word index with Concept expansion (WC), and Word index with Term expansion (WT). Results from a comparison evaluation study using Medline plus dataset indicated that 1) the Mean Average Precisions (MAPs) with term‐level expansion are higher than those with concept level by 5.6% for 30 queries and 10.9% for short queries; 2) the MAPs based on the string index strategy are better than those based on the word index by 15.5% for 30 queries and 9.6% for short queries; and 3) the String index with Term expansion (ST) has the highest MAPs for both 30 queries and short queries. These results will help us better understand the effectiveness of different automatic query expansion strategies using UMLS Metathesaurus and further inform the design of future Healthcare IR system.

Interactive and Automatic Query Expansion: A Comparative Study with an Application on Arabic

Much attention has been paid to the relative effectiveness of Interactive Query Expansion (IQE) versus Automatic Query Expansion (AQE). This research has been shown that automatic query expansion (collection dependent) strategy gives better performance than no query expansion. The percentage of queries that are improved by AQE strategy is 57% with average precision equal to 43.2. Compared against AQE (collection dependent) strategy, IQE gives better average precision than AQE strategy. The percentage of queries that are improved by best IQE decision is 86% with average precision equal to 44.1. Evaluation process reveals that the value of n in AQE strategy that gave the optimal value of average precision for the whole query set is equal to one.

Evaluation of Query Expansion Using MeSH in PubMed.

This paper investigates the effectiveness of using MeSH(®) in PubMed through its automatic query expansion process: Automatic Term Mapping (ATM). We run Boolean searches based on a collection of 64 topics and about 160,000 MEDLINE(®) citations used in the 2006 and 2007 TREC Genomics Tracks. For each topic, we first automatically construct a query by selecting keywords from the question. Next, each query is expanded by ATM, which assigns different search tags to terms in the query. Three search tags: [MeSH Terms], [Text Words], and [All Fields] are chosen to be studied after expansion because they all make use of the MeSH field of indexed MEDLINE citations. Furthermore, we characterize the two different mechanisms by which the MeSH field is used. Retrieval results using MeSH after expansion are compared to those solely based on the words in MEDLINE title and abstracts. The aggregate retrieval performance is assessed using both F-measure and mean rank precision. Experimental results suggest that query expansion using MeSH in PubMed can generally improve retrieval performance, but the improvement may not affect end PubMed users in realistic situations.

Joining automatic query expansion based on thesaurus and word sense disambiguation using WordNet

The selection of the most appropriate sense of an ambiguous word in a certain context is one of the main problems in Information Retrieval (IR). For this task, it is usually necessary to count on a semantic source, that is, linguistic resources like dictionaries, thesaurus, etc. Using a methodology based on simulation under a vector space model, we show that the use of automatic query expansion and disambiguation of the sense of the words permits to improve retrieval effectiveness. As shown in our experiments, query expansion is not able by itself to improve retrieval. However, when it is combined with Word Sense Disambiguation (WSD), that is, when the correct meaning of a word is chosen from among all its possible variations, it leads to effectiveness improvements.

A new query expansion method for document retrieval based on the inference of fuzzy rules

Automatic query expansion based on user relevance feedback techniques can improve the performance of document retrieval systems. In this paper, we present a new query expansion method based on the inference of fuzzy rules and user relevance feedback techniques to deal with document retrieval. The proposed method uses membership functions and fuzzy rules to infer relevant degrees of expansion terms and puts the expansion terms with larger relevant degrees into the original user's query. Then, the system calculates the degree of similarity of each document with respect to the expanded user's query. The proposed method gets a higher average precision rate and a higher average recall rate than the existing methods for document retrieval.

A practical ontology query expansion algorithm for semantic-aware learning objects retrieval

Following the rapid development of Internet, particularly web page interaction technology, distant e-learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have been proposed by several different international organizations. SCORM LOM, namely learning object metadata, facilitates the indexing and searching of learning objects in a learning object repository through extended sharing and searching features. However, LOM suffers a weakness in terms of semantic-awareness capability. Most information retrieval systems assume that users have cognitive ability regarding their needs. However, in e-learning systems, users may have no idea of what they are looking for and the learning object metadata. This study presents an ontological approach for semantic-aware learning object retrieval. This approach has two significant novel features: a fully automatic ontology-based query expansion algorithm for inferring and aggregating user intention based on their original short query, and another “ambiguity removal” procedure for correcting inappropriate user query terms. This approach is sufficiently generic to be embedded to other LOM-based search mechanisms for semantic-aware learning object retrieval. Focused on digital learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has experimentally demonstrated significantly improved retrieval precision and recall rate.

SEARCHING FOR EXPLANATORY WEB PAGES USING AUTOMATIC QUERY EXPANSION

When one tries to use the Web as a dictionary or encyclopedia, entering some single term into a search engine, the highly ranked pages in the result can include irrelevant or useless sites. The problem is that single-term queries, if taken literally, underspecify the type of page the user wants. For such problems automatic query expansion, also known as pseudo-feedback, is often effective. In this method the top n documents returned by an initial retrieval are used to provide terms for a second retrieval. This paper contributes, first, new normalization techniques for query expansion, and second, a new way of computing the similarity between an expanded query and a document, the “local relevance density” metric, which complements the standard vector product metric. Both of these techniques are shown to be useful for single-term queries, in Japanese, in experiments done over the World Wide Web in early 2001.

Enhancing Search Performance on Gnutella-Like P2P Systems

The big challenges facing the search techniques on Gnutella-like peer-to-peer networks are search efficiency and quality of search results. In this paper, leveraging information retrieval (IR) algorithms such as Vector Space Model (VSM) and relevance ranking algorithms, we present GES (Gnutella with Efficient Search) to improve search performance. The key idea is that GES uses a distributed topology adaptation algorithm to organize semantically relevant nodes into same semantic groups by using the notion of node vector. Given a query, GES employs an efficient search protocol to direct the query to the most relevant semantic groups for answers, thereby achieving high recall with probing only a small fraction of nodes. To the best of our knowledge, GES is the first to identify node vector size as an important role in impacting search performance and to show that the node vector size offers a good trade-off between search performance and bandwidth cost. Moreover, GES adopts automatic query expansion and local data clustering to improve search performance. We show that GES is efficient and even outperforms the centralized node clustering system SETS. For example, in the scenario where node capacity is heterogeneous, GES can achieve 73 percent recall when probing only 20 percent nodes, outperforming SETS by about 18 percent.