Web Query Research Articles

Exploiting location information for Web search

Most Web pages contain location information, which are usually neglected by traditional search engines. Queries combining location and textual terms are called as spatial textual Web queries. Based on the fact that traditional search engines pay little attention in the location information in Web pages, in this paper we study a framework to utilize location information for Web search. The proposed framework consists of an offline stage to extract focused locations for crawled Web pages, as well as an online ranking stage to perform location-aware ranking for search results. The focused locations of a Web page refer to the most appropriate locations associated with the Web page. In the offline stage, we extract the focused locations and keywords from Web pages and map each keyword with specific focused locations, which forms a set of <keyword, location> pairs. In the second online query processing stage, we extract keywords from the query, and computer the ranking scores based on location relevance and the location-constrained scores for each querying keyword. The experiments on various real datasets crawled from nj.gov, BBC and New York Time show that the performance of our algorithm on focused location extraction is superior to previous methods and the proposed ranking algorithm has the best performance w.r.t different spatial textual queries.

Efficient Processing of Semantic Web Queries in HBase and MySQL Cluster

With rapid growth of the Semantic Web, the authors' research focuses on designing a scalable resource description framework (RDF) database system that can efficiently process SPARQL queries over large RDF datasets. In this article, they present their design, architecture, storage schemes, and performance evaluation of two efficient RDF data management approaches that use state-of-the-art cloud and relational database technologies-HBase and MySQL Cluster.

Deep Web query interface schema matching based on matching degree and semantic similarity

Deep Web query interface schema matching based on matching degree and semantic similarity

Syndromic Surveillance for Outbreak Detection and Investigation

For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data: telephone triage of acute gastroenteritis, web queries about symptoms of gastrointestinal illness, and OTC pharmacy sales of anti-diarrhea medication. The sensitivity and specificity were highest for telephone triage data. It provided the most promising source of syndromic data for surveillance of point-source outbreaks. Currently, a project has been initialized to develop and implement a national system in Sweden for daily syndromic surveillance based on 1177 Health Care Direct, supporting regional and local outbreak detection and investigation.

Query Intent Disambiguation of Keyword-Based Semantic Entity Search in Dataspaces

Keyword query has attracted much research attention due to its simplicity and wide applications. The inherent ambiguity of keyword query is prone to unsatisfied query results. Moreover some existing techniques on Web query keyword query in relational databases and XML databases cannot be completely applied to keyword query in dataspaces. So we propose KeymanticES a novel keyword-based semantic entity search mechanism in dataspaces which combines both keyword query and semantic query features. And we focus on query intent disambiguation problem and propose a novel three-step approach to resolve it. Extensive experimental results show the effectiveness and correctness of our proposed approach.

Mining subtopics from text fragments for a web query

Web search queries are often ambiguous or faceted, and the task of identifying the major underlying senses and facets of queries has received much attention in recent years. We refer to this task as query subtopic mining. In this paper, we propose to use surrounding text of query terms in top retrieved documents to mine subtopics and rank them. We first extract text fragments containing query terms from different parts of documents. Then we group similar text fragments into clusters and generate a readable subtopic for each cluster. Based on the cluster and the language model trained from a query log, we calculate three features and combine them into a relevance score for each subtopic. Subtopics are finally ranked by balancing relevance and novelty. Our evaluation experiments with the NTCIR-9 INTENT Chinese Subtopic Mining test collection show that our method significantly outperforms a query log based method proposed by Radlinski et al. (2010) and a search result clustering based method proposed by Zeng et al. (2004) in terms of precision, I-rec, D-nDCG and D#-nDCG, the official evaluation metrics used at the NTCIR-9 INTENT task. Moreover, our generated subtopics are significantly more readable than those generated by the search result clustering method.

Spatio-Temporal Web Sensors Using Web Queries vs. Documents

In the Web world, one user creates and uploads a Web document about a phenomenon or event in the physical world, while another user retrieves and consumes Web documents by submitting a Web query. There have been many researches to mine Web documents in the exploding Web world, especially User Generated Content such as weblogs and microblogs, for knowledge about various phenomena and events in the physical world, and also Web services with the Web-mined knowledge have been made available for the public. However, there are few detailed investigations on how accurately Web-mined data reflect physical-world data. It must be socially-problematic to immoderately utilize the Web-mined data in public Web services without ensuring their accuracy sufficiently. Therefore, the previous papers introduced various Web Sensors to extract spatiotemporal data about a target phenomenon from Web documents searched by keyword(s) representing the target phenomenon, and tried to validate the potential and reliability of the Web-sensed spatiotemporal data. Moreover, this paper compares spatio-temporal Web Sensors analyzing Web queries and/or Web documents. 

Evaluating Question Answering Over Linked Data

The availability of large amounts of open, distributed and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.

Efficient Approach for Knowledge Management Using Deep Web Information Retrieval System

Along with the development of network technology, web information are rapidly growing, and the way of information storage is gradually changed from the html to the database, thus web information can be divided into the surface web and deep web. Deep web is a concept corresponding to the surface web. It means from ordinary search engine that is difficult to discover the information content of a web page. The traditional crawler only crawl the content on the surface of a web, which makes the current traditional search engine, did not retrieve deep web data. Deep web compared with surface web has the advantage of large volume, high quality, theme single-minded, good structured. In view of several advantages, the establishment of deep web data integration system is becoming a research hotspot. The deep web query interface is the only entrance of the background database, so how to determine which web form is the query interface is important to the deep web information access. However, because the page proportion on the internet which contains querying interface is very small, using the traditional breadth-first strategy and keyword filtering method to crawl, it will download a lot of unrelated pages, spend a lot of resources, we need a way to efficiently find and collect the query interfaces through deep web crawling strategy. We proposed novel query planning approach, for executing different types of complex attribute through queries over multiple inter-dependent deep web data sources. increase accelerate query searching based on attribute selection, execution and propose optimization techniques, including query plan merging and grouping optimization. Keywords: Novel query planning approach, Deep web, Semantic Deep Web, Ontologies, attribute.

Advance Frameworks for Hidden Web Retrieval Using Innovative Vision-Based Page Segmentation

The volatile intensification of internet has posed a exigent problem in extracting significant data. Conventional web crawlers focus merely on the surface web while the hidden web remains intensifying following the prospect. Hidden web pages are created dynamically as a result of queries masquerade to precise web databases. The structure of the hidden web pages crafts it unfeasible for conventional web crawlers to access hidden web contents. We proposed innovative Vision-based Page Segmentation (IVBPS) algorithm for hidden web retrieval and develop intelligent crawler and interpretation of hidden web query interfaces. Hidden intelligent crawler and interpretation of hidden web query interfaces split the method into two stages. The primary stage comprises query analysis and query translation and the subsequent wrap vision-based extraction of data from the dynamically created hidden web pages. There are numerous conventional approaches for the extraction of hidden web pages but our proposed technique intends at overcoming the inherent limitations of the former.

A dynamic composition of ontology modules approach: application to web query reformulation

As the semantic web emerges, the problems related to the management of ontologies are gaining relevance. Ontology management is considered as one of the main issues in artificial intelligence and knowledge engineering. Ontology modularisation aims at structuring ontologies to facilitate its reuse and maintenance. Compositional methodologies intend to integrate a set of modules into a larger ontology. This paper presents a dynamic approach for ontology modules composition based on semantic similarity measures between concepts on the web, and aiming to improve the resulting modular ontology’s structure and content. The proposed approach takes place in three main phases, namely: cooccurrence graph construction of ontology modules, cooccurrence graph clustering of modular ontologies and hierarchical modules clusters construction. To evaluate our approach, we compare the obtained results with those of other compositional approaches in the literature, and we also show how these modular ontologies can be used to improve web query reformulation.

Moving towards Positive Security Model for Web Application Firewall

The proliferation of web application and the pervasiveness of mobile technology make web-based attacks even more attractive and even easier to launch. Web Application Firewall (WAF) is an intermediate tool between web server and users that provides comprehensive protection for web application. WAF is a negative security model where the detection and prevention mechanisms are based on predefined or user-defined attack signatures and patterns. However, WAF alone is not adequate to offer best defensive system against web vulnerabilities that are increasing in number and complexity daily. This paper presents a methodology to automatically design a positive security based model which identifies and allows only legitimate web queries. The paper shows a true positive rate of more than 90% can be achieved.

Head Lice Surveillance on a Deregulated OTC-Sales Market: A Study Using Web Query Data

The head louse, Pediculus humanus capitis, is an obligate ectoparasite that causes infestations of humans. Studies have demonstrated a correlation between sales figures for over-the-counter (OTC) treatment products and the number of humans with head lice. The deregulation of the Swedish pharmacy market on July 1, 2009, decreased the possibility to obtain complete sale figures and thereby the possibility to obtain yearly trends of head lice infestations. In the presented study we wanted to investigate whether web queries on head lice can be used as substitute for OTC sales figures. Via Google Insights for Search and Vårdguiden medical web site, the number of queries on “huvudlöss” (head lice) and “hårlöss” (lice in hair) were obtained. The analysis showed that both the Vårdguiden series and the Google series were statistically significant (p<0.001) when added separately, but if the Google series were already included in the model, the Vårdguiden series were not statistically significant (p = 0.5689). In conclusion, web queries can detect if there is an increase or decrease of head lice infested humans in Sweden over a period of years, and be as reliable a proxy as the OTC-sales figures.

Joint Top-K Spatial Keyword Query Processing

Web users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed solution is efficient on real data sets. We also offer analytical studies on synthetic data sets to demonstrate the efficiency of the proposed solution.

BENCHMARKING THE QUALITY OF GEOWEB: INFORMATION AND TACIT KNOWLEDGE ABOUT RESTAURANTS IN THREE ITALIAN CITIES

ABSTRACTIf information and communication technologies are contributing to the attenuation of the force of destiny towards opportunities for individuals and places, then some applications, together with practices of ranking, mapping and delivering the representation of place will be concerned with how codes or practices in cyberspace negatively or positively shape our world. This paper examines the quality of geo‐web information from ‘Google maps’ applied to the empirical case of the Italian cities of Benevento, Bologna and Siena. It interprets data using a three‐level benchmarking of information‐listing generated by web queries of localised services: the Google geocoded Internet, Yellow Pages, and tacit knowledge. The implications of the on‐line/off‐line visibility of local enterprises for local development actions, and the implications of hidden filters and ranking within the framework of the contemporary global information society will be outlined. Furthermore, an assessment model of the ‘geoquality’ of geocoded Internet spatial information is developed.

A Highest Sense Count Based Method for Disambiguation of Web Queries for Hindi Language Web Information Retrieval

The ambiguity in word senses has been recognized as a major challenge for the information retrieval systems. Hindi language web information retrieval, like other languages, faces the problem of sense ambiguity. The sense ambiguity problem deteriorates the performance of every natural language processing (NLP) application. The performance of Hindi language web information retrieval is also affected by it. In this paper, the author formalized an approach for the disambiguation of the senses to improve the performance of Hindi web information retrieval. Our system works in such a way that ambiguity detection has been performed before disambiguation of web queries. Test samples of 100 queries have been selected. When these queries were subjected to ambiguity detection, we found that 43% of them have been detected unambiguous. After ambiguity detection, the disambiguation approach is followed which is based on HSC (Highest Sense Count). Query disambiguation approach further follows query expansion. The expanded query generates the new result set which results into high precision and high similarity score. The 57 expanded queries are tested against 1000 test document instances. The overall improvement is 45% in the average precision, 23% in interpolated average precision and a significant improvement in the average similarity score of the new generated result set. The overall accuracy of our approach has been 61.4% and it improves the performance of the system by 45%.

Web Supported Query Taxonomy Classifier

A new approach called Web Supported Query Taxonomy Classifier is introduced in this paper, which generates better searching results. We combine WSQ, Web Supported Queries approach with Query Classification in which bridging classifier and category selection method is used for classification of queries. In this paper, Query categorization will build bridging classifier in an offline mode at mediator taxonomy integrated with the category selection method for the effectiveness and efficiency of online classification and then classifier is used for mapping input queries to target taxonomy in online mode. After this the queries are settled in virtual tables presented by WSQ tuples for the generation of web search result calling one or more search engines for improved results. Further a technique called asynchronous iteration is used for concurrency among multiple web search requests and query processing.

Entity Synonyms for Structured Web Search

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision.

Automatic discovery of Web Query Interfaces using machine learning techniques

The amount of information contained in databases available on the Web has grown explosively in the last years. This information, known as the Deep Web, is heterogeneous and dynamically generated by querying these back-end (relational) databases through Web Query Interfaces (WQIs) that are a special type of HTML forms. The problem of accessing to the information of Deep Web is a great challenge because the information existing usually is not indexed by general-purpose search engines. Therefore, it is necessary to create efficient mechanisms to access, extract and integrate information contained in the Deep Web. Since WQIs are the only means to access to the Deep Web, the automatic identification of WQIs plays an important role. It facilitates traditional search engines to increase the coverage and the access to interesting information not available on the indexable Web. The accurate identification of Deep Web data sources are key issues in the information retrieval process. In this paper we propose a new strategy for automatic discovery of WQIs. This novel proposal makes an adequate selection of HTML elements extracted from HTML forms, which are used in a set of heuristic rules that help to identify WQIs. The proposed strategy uses machine learning algorithms for classification of searchable (WQIs) and non-searchable (non-WQI) HTML forms using a prototypes selection algorithm that allows to remove irrelevant or redundant data in the training set. The internal content of Web Query Interfaces was analyzed with the objective of identifying only those HTML elements that are frequently appearing provide relevant information for the WQIs identification. For testing, we use three groups of datasets, two available at the UIUC repository and a new dataset that we created using a generic crawler supported by human experts that includes advanced and simple query interfaces. The experimental results show that the proposed strategy outperforms others previously reported works.

Data management on the spatial web

Due in part to the increasing mobile use of the web and the proliferation of geo-positioning, the web is fast acquiring a significant spatial aspect. Content and users are being augmented with locations that are used increasingly by location-based services. Studies suggest that each week, several billion web queries are issued that have local intent and target spatial web objects. These are points of interest with a web presence, and they thus have locations as well as textual descriptions. This development has given prominence to spatial web data management, an area ripe with new and exciting opportunities and challenges. The research community has embarked on inventing and supporting new query functionality for the spatial web. Different kinds of spatial web queries return objects that are near a location argument and are relevant to a text argument. To support such queries, it is important to be able to rank objects according to their relevance to a query. And it is important to be able to process the queries with low latency. The talk offers an overview of key aspects of the spatial web. Based on recent results obtained by the speaker and his colleagues, the talk explores new query functionality enabled by the setting. Further, the talk offers insight into the data management techniques capable of supporting such functionality.