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About 826 papers for “Web query classification”

Query enrichment for web-query classification

Dou Shen, Rong Pan, Jian-Tao Sun et al. · ACM Transactions on Information Systems · 2006

Web-search queries are typically short and ambiguous. To classify these queries into certain target categories is a difficult but important problem. In this article, we present a new technique called query enrichment, which takes a short query and maps it to intermediate objects. Based on the collected intermediate objects, the query is then mapped to target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to the ACM Knowledge Discovery and Data Mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this article, we show that, despite the difficulty of an abundance of ambiguous queries and lack of training data, our query-enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best result for F1 and precision is 42.4% and 44.4%, respectively, which is 9.6% and 24.3% higher than those from the runner-ups, respectively.

Cited by 145

Normalized Web Distance Based Web Query Classification

Lovelyn Rose, K. R. Ch · 2012

Abstract: Problem statement: The problem is to classify a given web query to a set of 67 target categories. The target categories are ranked based on the degree of similarity to a given query. Approach: The feature set is the set of intermediate categories retrieved from a directory search engine for a given query. Using direct mapping and Normalized Web Distance (NWD) the intermediate categories are mapped to the required target categories. The categories are then ranked based on three parameters of the intermediate categories namely, position, frequency and a combination of frequency and position. Results: The results proved that the third parameter gave a better result and a maximum of 40 search result pages ensure better results. Conclusion: With NWD as the similarity measure, the precision and recall is found to increase by 10 % over the previous methods.

Cited by 0

Functional Faceted Web Query Classification By

Nguyen Viet Bang, Nguyen Viet Bang · 2015

Work on web query analysis has attempted to infer the information goals of search users behind the queries. In this thesis, we propose a faceted classification scheme for web queries. Unlike previous work, our functional scheme ties its classification to actionable strategies for search engines to take. The scheme consists of four facets of ambiguity, authority sensitivity, temporal sensitivity and spatial sensitivity. We hypothesize that the classification of queries into such facets yields insight on user intent and information needs. We demonstrate how to bridge our taxonomy to existing work on web query classification. To validate the classification scheme, we asked users to annotate queries with respect to our facets and obtained high agreement. We also assess the coverage of our faceted classification on a random sample of queries from logs. Finally, we discuss the algorithmic approaches we take in our current work to automate such faceted classification. In particular, we present our approach to characterize the authority sensitivity facet of web queries, which to the best of our knowledge, has not been studied before. We implemented and evaluated a prototype system to automatically classify this facet of queries and result shows that automatic classification is possible. Subject Descriptors:

Cited by 0

Building bridges for web query classification

Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2006

Elsevier’s Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

Cited by 0

Building bridges for web query classification

Dou Shen, Jian-Tao Sun, Qiang Yang et al. · 2006

Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories. QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization. In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries. In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode. This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy. A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once. In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries. Category selection can improve both efficiency and effectiveness of the online classification. By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 9.7% and 3.8% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005.

Cited by 277

Query Topic Classification and Sociology of Web Query Logs

Nikolai Buzikashvili · Computación y Sistemas · 2015

received the M.Sc. in AppliedMathematics from the Moscow Institute ofEconomics and Statistics. He is a SeniorResearcher at the Institute for System Analysis ofthe Russian Academy of Sciences. Areas of hisinterest are Web information search and retrieval.He is author of more than 50 publications in thearea of computer science.

Cited by 2

Varying approaches to topical web query classification

Steven M. Beitzel, Eric C. Jensen, Abdur Chowdhury et al. · 2007

Topical classification of web queries has drawn recent interest because of the promise it offers in improving retrieval effectiveness and efficiency. However, much of this promise depends on whether classification is performed before or after the query is used to retrieve documents. We examine two previously unaddressed issues in query classification: pre versus post-retrieval classification effectiveness and the effect of training explicitly from classified queries versus bridging a classifier trained using a document taxonomy. Bridging classifiers map the categories of a document taxonomy onto those of a query classification problem to provide sufficient training data. We find that training classifiers explicitly from manually classified queries outperforms the bridged classifier by 48% in F1 score. Also, a pre-retrieval classifier using only the query terms performs merely 11% worse than the bridged classifier which requires snippets from retrieved documents.

Cited by 60

Query Type Classification for Web Document Retrieval

In-ho Kang, et al. · 2003

The heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web documents are not enough to find good answer documents. Link information and URL information compensates for the insu #ciencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need di#erent strategies to find target documents according to a query type. We can classify user queries as three categories, the topic relevance task, the homepage finding task, and the service finding task. In this paper, a user query classification scheme is proposed. This scheme uses the di#erence of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification. After we classified a user query, we apply di#erent algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.

Cited by 0

Automatic Query Reformulation Using Classification in Web Searching

R. M. Suresh, Kurmachalam Ajay Kumar · International Journal of Computer Science and Informatics · 2012

The search query is a set of words or phrases a user enters when looking for information on a specific topic or subject. Formulating a search query is a challenging task for most of users because they are required to express their anomalous states of knowledge. In the query reformulation stage, users modify their initial queries and submit new ones that more accurately reflect their information needs. Classification and Prediction are two forms of query analysis that can be used to extract models describing frequently used query classes or to predict the reformulation for the query.. An ideal solution might be that the system automatically generates a concise and informative summary for each perspective of the query. In our approach a model is constructed by analyzing queries described by the frequency of search, query is assumed to belong to a predefined class.

Cited by 0Open access

Research on Automatic Classification for Deep Web Query Interfaces

Peiguang Lin, Yibing Du, Xiaohua Tan et al. · 2008

In recent years, the Web is "deepened" rapidly and users have to browse quantities of Web sites to access Web databases in a specific domain. So, to build an unified query interface which integrates query interfaces of a domain to access various Web databases at the same time becomes a very important issue. In this paper, the schema characteristics of query interfaces and common attributes in a same domain are firstly analyzed, and it also gives a new representation of query interface, then the definition of "Form term" and "Function term" are proposed ,and a new similarity computing algorithm, literal and semantic based similarity computing (LSSC) is proposed, which is based on the two definitions. Secondly, a clustering algorithm for Deep Web query interfaces is given by combining LSSC and NQ algorithm: LSSC-NQ. Finally, experiments show that this algorithm can give accurate similarity computing, and cluster query interfaces efficiently, reliably and quickly.

Cited by 13

Automatic classification of deep web databases with simple query interface

Xuefeng Xian, Pengpeng Zhao, Wei Fang et al. · 2009

Deep Web database classify is a key operation in organizing Deep Web resources. We address the problem of identifying the domain of Web databases with simple query interface. The existing methods can not effectively classify this type of Web databases, to solve this problem, we propose an new framework that can automatically and accurately classify Web databases with simple query interface based on probing query. The core of this framework is a domain specific classifier(DSC). DSC is constructed by using the features that can be easily extracted from advanced query interfaces(forms) in domain. According to the similar relation among result schemas, interface schemas and global schemas of Web database, Based on its result schemas, a new Web database with simple query interface can be classified by DSC. Experiments running on real structured Web databases collected from the Internet show that our provides an effective and scalable solution for classifying Web databases with simple query interface.

Cited by 6

Deep Web Data Source Classification Based on Query Interface Context

Zilu Cui, Yuchen Fu · 2012

As the volume of information in the Deep Web grows, a Deep Web data source classification algorithm based on query interface context is presented. Two methods are combined to get the search interface similarity. One is based on the vector space. The classical TF-IDF statistics are used to gain the similarity between search interfaces. The other is to compute the two pages semantic similarity by the use of HowNet. Based on the K-NN algorithm, a WDB classification algorithm is presented. Experimental results show this algorithm generates high-quality clusters, measured both in terms of entropy and F-measure. It indicates the practical value of application.

Cited by 4

SIGIR 2007 Proceedings Poster Varying Approaches to Topical Web Query Classification

Steven M. Beitzel · 2009

Topical classification of web queries has drawn recent interest because of the promise it offers in improving retrieval effectiveness and efficiency. However, much of this promise depends on whether classification is performed before or after the query is used to retrieve documents. We examine two previously unaddressed issues in query classification: pre versus post-retrieval classification effectiveness and the effect of training explicitly from classified queries versus bridging a classifier trained using a document taxonomy. Bridging classifiers map the categories of a document taxonomy onto those of a query classification problem to provide sufficient training data. We find that training classifiers explicitly from manually classified queries outperforms the bridged classifier by 48 % in F1 score. Also, a pre-retrieval classifier using only the query terms performs merely 11 % worse than the bridged classifier which requires snippets from retrieved documents.

Cited by 0

Automatic classification of Web queries using very large unlabeled query logs

Steven M. Beitzel, Eric C. Jensen, David D. Lewis et al. · ACM Transactions on Information Systems · 2007

Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose Web search systems. Such classification becomes critical if the system must route queries to a subset of topic-specific and resource-constrained back-end databases. Successful query classification poses a challenging problem, as Web queries are short, thus providing few features. This feature sparseness, coupled with the constantly changing distribution and vocabulary of queries, hinders traditional text classification. We attack this problem by combining multiple classifiers, including exact lookup and partial matching in databases of manually classified frequent queries, linear models trained by supervised learning, and a novel approach based on mining selectional preferences from a large unlabeled query log. Our approach classifies queries without using external sources of information, such as online Web directories or the contents of retrieved pages, making it viable for use in demanding operational environments, such as large-scale Web search services. We evaluate our approach using a large sample of queries from an operational Web search engine and show that our combined method increases recall by nearly 40% over the best single method while maintaining adequate precision. Additionally, we compare our results to those from the 2005 KDD Cup and find that we perform competitively despite our operational restrictions. This suggests it is possible to topically classify a significant portion of the query stream without requiring external sources of information, allowing for deployment in operationally restricted environments.

Cited by 106

Inducing and Refining Topics for Web Query Classification Using a Semantic Network

Rituparna Kumar, M. Chandrasekaran · Journal of Computational and Theoretical Nanoscience · 2018

Web query classification, the task of inferring topical categories from a web search query is a non-trivial problem in Information Retrieval domain. The topic categories inferred by a Web query classification system may provide a rich set of features for improving query expansion and web advertising. Conventional methods for Web query classification derive corpus statistics from the web and employ machine-learning techniques to infer Open Directory Project categories. But they suffer from two major drawbacks, the computational overhead to derive corpus statistics and inferring topic categories that are too abstract for semantic discrimination due to polysemy. Concepts too shallow or too deep in the semantic gradient are produced due to the wrong senses of the query terms coalescing with the correct senses. This paper proposes and demonstrates a succinct solution to these problems through a method based on the Tree cut model and Wordnet Thesarus to infer fine-grained topic categories for Web query classification, and also suggests an enhancement to the Tree Cut Model to resolve sense ambiguities.

Cited by 0

Detecting the Abnormal SQL Query Using Hybrid SVM Classification Technique in Web Application

R Shobana, M Suriakala · International Journal of Data Mining Techniques and Applications · 2020

Detecting SQL injection attacks (SQLIAs) is ending up progressively significant in database-driven sites. A large portion of the investigations on SQLIA detection have concentrated on the structured query language (SQL) structure at the application level. Yet, those methodologies unavoidably neglects to identify those attacks that utilization previously put away methodology and information inside the database framework. While most existing techniques tended to towards diminishing the quantity of support vectors, the proposed philosophy concentrated on decreasing the quantity of test datapoints that need SVMs assistance in getting grouped. The focal thought is to inexact the choice limit of SVM utilizing paired trees. The subsequent tree is a half and half tree as in it has both univariate and multivariate (SVM) nodes. The cross breed tree takes SVMs assistance just in ordering significant information focuses lying close choice limit staying less urgent datapoints are grouped by quick univariate nodes.

Cited by 1