Automatic Query Research Articles

Arabic Query Expansion Using WordNet and Association Rules

Query expansion is the process of adding additional relevant terms to the original queries to improve the performance of information retrieval systems. However, previous studies showed that automatic query expansion using WordNet do not lead to an improvement in the performance. One of the main challenges of query expansion is the selection of appropriate terms. In this paper, the authors review this problem using Arabic WordNet and Association Rules within the context of Arabic Language. The results obtained confirmed that with an appropriate selection method, the authors are able to exploit Arabic WordNet to improve the retrieval performance. Their empirical results on a sub-corpus from the Xinhua collection showed that their automatic selection method has achieved a significant performance improvement in terms of MAP and recall and a better precision with the first top retrieved documents.

A query term re-weighting approach using document similarity

Pseudo-relevance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedback methods assume the initial retrieved set of documents to be relevant. Then they use these documents to extract more relevant terms for the query or just re-weigh the user's original query. In this paper, we propose a straightforward, yet effective use of pseudo-relevance feedback method in detecting more informative query terms and re-weighting them. The query-by-query analysis of our results indicates that our method is capable of identifying the most important keywords even in short queries. Our main idea is that some of the top documents may contain a closer context to the user's information need than the others. Therefore, re-examining the similarity of those top documents and weighting this set based on their context could help in identifying and re-weighting informative query terms. Our experimental results in standard English and Persian test collections show that our method improves retrieval performance, in terms of MAP criterion, up to 7% over traditional query term re-weighting methods.

A query expansion method for retrieving online BIM resources based on Industry Foundation Classes

With the rapid popularity of Building Information Modeling (BIM) technology, BIM resources such as building product libraries are growing rapidly on the World Wide Web. As a result, this also increases the difficulty for quickly finding useful BIM resources that are sufficiently close to user's specific needs. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy is often not satisfactory because of the semantic ambiguity of terminologies in BIM-specific documents and queries. To address this issue, we develop a prototype semantic search engine, named BIMSeek, for retrieving online BIM resources. The central work consists of two parts as follows. Firstly, based on Industry Foundation Classes (IFC) which is a major standard for BIM, a domain ontology is constructed for encoding BIM-specific knowledge into the search engine. Using the ontology, term`inologies in BIM documents can be disambiguated and indexed. Secondly, by combining the ontology and local context analysis technique, an automatic query expansion method is presented for improving retrieval performance. Compared with traditional keyword-based methods and WordNet-based query expansion methods, the experimental results demonstrate that our method outperforms them. The search engine is available at http://cgcad.thss.tsinghua.edu.cn/liuyushen/ifcqe/ . • Develop a prototype semantic search engine for retrieving online BIM resources • Construct an IFC IR Ontology • By combining the ontology and LCA, present an automatic query expansion method for improving retrieval performance • Test our method on the benchmark dataset of Autodesk Seek

Automatic query rewriting schemes for multitenant SaaS applications

In Software as a Service (SaaS) environments, designing and realizing multitenant schema-mapping that supports a shared database with custom extensions is a non-trivial task. Universal Table is one promising schema-mapping technique that is commonly used. However, there has been little research devoted to the design and realization of a query rewriting scheme for Universal Table. In this paper, we present a collection of general query rewriting schemes for Universal Table that can transparently transform tenant-specific logical queries into corresponding physical queries. Based on the design, we have developed a Java-based schema-mapping and query rewriting middleware for Universal Table and a sample online shopping SaaS application to verify its feasibility. Additionally, analytical results that can be used to predict the overhead of our schemes are also reported. Finally, we conduct a series of experiments and find that the results not only agree well with our analytical predictions but also show that our schemes are scalable to the number of tenants and the number of concurrent database connections.

Learning keyword phrases from query logs of USPTO patent examiners for automatic query scope limitation in patent searching

Professional search in patent repositories poses several unique challenges. One key requirement is to search the entire affected space of concepts, following well-defined procedures to ensure traceability of results obtained. Several techniques have been introduced to enhance query generation, preferably via automated query term expansion, to improve retrieval effectiveness. Currently, these approaches are mostly limited to computing additional query terms from patent documents based on statistical measures. For conceptual search to solve the limitation of traditional keyword search standard dictionaries are used to provide synonyms and keyword phrases for query refinement. Studies show that these are insufficient in such highly specialized domains. In this paper, we present an approach to extract keyword phrases from query logs created during the validation procedure of the patent applications. This creates valuable domain-specific lexical databases for several specific patent classes that can be used to both expand as well as limit the scope of a patent search. This provides a more powerful means to guide a professional searcher through the search process. We evaluate the lexical databases based on real query sessions of patent examiners.

Natural Language to SQL Generation for Semantic Knowledge Extraction in Social Web Sources

Enormous evolution of web data creates a peculiar myth in the field of computer and information technology for extracting the meaningful content from the web. Many organizations and social networks use databases for storing information and the data will be fetched from the specified data store. Data can be retrieved or accessed by SQL queries whereas the query is in the form of natural lingual statement which has to be processed. So, the primary objective of this research article is to find the suitable way to convert natural language query to SQL and make the data apt for semantic extraction. This Research paper also aims to derive an automatic query translator for Natural Language based questions into their associated SQL queries and provides an user friendly interface between end user and the database for easy access of social web data from different web sources such as facebook, twitter and linkedIn etc.,. This paper is implemented using java as the front end, SQL server as the back end and R-tool is used to collect the data from social web sources. This research article provides an optimized SQL query generation for the Natural Language question provided by the end user.

The effects of multiple query evidences on social image retrieval

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality—more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.

An Automatic Query Generation Approach for Arabic Corpus

of the most common problems that encounter retrieval systems is the query environment which is utilized to evaluate the performance or throughput of them. Usually data sets of queries for a specific corpus are generated using the human experiences. Manual queries are more accurate than automatic one's, but they require a huge effort in the huge corpus. This paper proposes an automatic query generation (AQG) system for Arabic language. The system generates a set of queries of different length that were applied on a query expansion system. The results show the feasibility of these queries compared with manual one in term of average recall and average precision.

Incremental blind feedback

Automatic query expansion (AQE) is a useful technique for enhancing the effectiveness of information retrieval systems. In this article, we propose a novel AQE algorithm which first adopts a systematic incremental approach to choose feedback documents from the top retrieved set and then selects the expansion terms aggregating the scores from each feedback set. We also devise a term selection measure and a number of weighting schemes based on easily computable features. A set of experiments with a large number of standard test collections reveals that the proposed incremental blind feedback algorithm outperforms a number of state-of-the-art query expansion methods with remarkable significance and consistency.

Automatic Deep Web Query Results User Satisfaction Evaluation with Click-through Data Analysis

We browse through hundreds of Deep web pages everyday to find information of interest. We feel happy when Deep web browsing operations provide us with necessary information; otherwise, we feel bitter. Now, the measurement of this user satisfaction has become a hot research topic. In this paper, we propose a click-through-data-based and unsupervised user satisfaction evaluation system, CNEITE, to evaluate the user satisfaction of Deep Web query result pages. It applies query type classifying, navigational query evaluating, informational/transactional query evaluating to solve the challenging tasks. We evaluated our CNEITE system on the AOL data sets, experimental results show that CNEITE achieves higher classify precision than a widely used classify method , Dtree, and higher annotate answer accuracy than method proposed in [17].

The Application of Tolerance Database in the Secondary Development of SolidWorks

Visual C++6.0 is selected as development tools while Access 2000 of Microsoft is used to build database, and the technology of ADO is adopted to achieve the connection between them. Though the secondary development of API function provided by SolidWorks, the query module is taken as a plug-in integrated in SolideWorks, which achieves the automatic query.

The Use of Arabic WordNet in Arabic Information Retrieval

Research and experimentation using Arabic WordNet in the field of information retrieval are relatively new. It is limited compared to the research that has been done using Princeton WordNet. This work attempts to study the impact of Arabic WordNet on the performance of Arabic information retrieval. We extend Lucene with Arabic WordNet to expand user's queries. The major contribution of this study is to propose an interactive query expansion (IQE) methodology using the word's part-of-speech, according to the part it plays in a query. First, the user selects the appropriate part of speech for each term in the original query, and then he reselects the appropriate synonyms. Experimental results show that our IQE strategy produces a good Mean Average Precision (MAP), it is able to improve MAP by 12.6%, but no variant of automatic query expansion (AQE) strategies did. Nevertheless, the experiments allow us to conclude that with an appropriate use of Arabic WordNet as a source of linguistic information for AQE can improve effectiveness for Arabic information retrieval.

An Ontology-Based Learning Resources Management Framework for Exploratory E-learning

This paper presents an ontology-based conceptual framework for effectively managing exploratory e-learning resources. The proposed framework has five significant novel features including authentication of retrieved resources, automatic ontology-based query refinement, reuse-oriented management of retrieved resources, adaptive retrieval of learning resources based on the style and preference of individual learners, and synthesisation of retrieval and management activities for creating reusable learning repositories. The applicability of the framework is demonstrated using a sample fragment of an ontology developed in the database domain.

Improving pseudo relevance feedback based query expansion using genetic fuzzy approach and semantic similarity notion

Pseudo relevance feedback-based query expansion is a popular automatic query expansion technique. However, a survey of work done in the area shows that it has a mixed chance of success. This paper captures the limitations of pseudo relevance feedback (PRF)-based query expansion and proposes a method of enhancing its performance by hybridizing corpus-based information, with a genetic fuzzy approach and semantic similarity notion. First the paper suggests use of a genetic fuzzy approach to select an optimal combination of query terms from a pool of terms obtained using PRF-based query expansion. The query terms obtained are further ranked on the basis of semantic similarity with original query terms. The experiments were performed on CISI collection, a benchmark dataset for information retrieval. It was found that the results were better in both terms of recall and precision. The main observation is that the hybridization of various techniques of query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing these techniques.

Improving search over Electronic Health Records using UMLS-based query expansion through random walks

Most of the information in Electronic Health Records (EHRs) is represented in free textual form. Practitioners searching EHRs need to phrase their queries carefully, as the record might use synonyms or other related words. In this paper we show that an automatic query expansion method based on the Unified Medicine Language System (UMLS) Metathesaurus improves the results of a robust baseline when searching EHRs. The method uses a graph representation of the lexical units, concepts and relations in the UMLS Metathesaurus. It is based on random walks over the graph, which start on the query terms. Random walks are a well-studied discipline in both Web and Knowledge Base datasets. Our experiments over the TREC Medical Record track show improvements in both the 2011 and 2012 datasets over a strong baseline. Our analysis shows that the success of our method is due to the automatic expansion of the query with extra terms, even when they are not directly related in the UMLS Metathesaurus. The terms added in the expansion go beyond simple synonyms, and also add other kinds of topically related terms. Expansion of queries using related terms in the UMLS Metathesaurus beyond synonymy is an effective way to overcome the gap between query and document vocabularies when searching for patient cohorts.

Multilingual query expansion in the SveMed+ bibliographic database: A case study

SveMed+ is a bibliographic database covering Scandinavian medical journals. It is produced by the University Library of Karolinska Institutet in Sweden. The bibliographic references are indexed with terms from the Medical Subject Headings (MeSH) thesaurus. The MeSH has been translated into several languages, including Swedish, making it suitable as the basis for multilingual tools in the medical field. The data structure of SveMed+ closely mimics that of PubMed/MEDLINE. Users of PubMed/MEDLINE and similar databases typically expect retrieval features that are not readily available off-the-shelf. The SveMed+ interface is based on a free text search engine (Solr) and a relational database management system (Microsoft SQL Server) containing the bibliographic database and a multilingual thesaurus database. The thesaurus database contains medical terms in three different languages and information about relationships between the terms. A combined approach involving the Solr free text index, the bibliographic database and the thesaurus database allowed the implementation of functionality such as automatic multilingual query expansion, faceting and hierarchical explode searches. The present paper describes how this was done in practice.

Power Distributing Network Client Query Methods Based on Tree Structure Database

On the basis of expounding the necessity of establishing power distributing system fault diagnosis visualization systems, the outage situation automatic notification and equipment failure query plan of distribution network is proposed based on database. Firstly, the information of electrical diagrams of distributing system should be input into database. Secondly, the information in the form of a tree structure will be displayed in WPF by programming. Meanwhile, we can add annotation to electrical diagrams by programming. When the distribution system malfunctions, dispatching operation control personnel enter the point of failure, the cause and outage time and the details of the regional head of a power outage will be shown. Then he responsible person will be informed by SMS. In this way, we can reduce blackout time and achieve the standardized management of distribution network. When needing the working condition of any device, just enter its name and the information will be shown.

Automatic query expansion: A structural linguistic perspective

A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co‐occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency‐based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus‐based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large‐scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.

Using query logs of USPTO patent examiners for automatic query expansion in patent searching

In the patent domain significant efforts are invested to assist researchers in formulating better queries, preferably via automated query expansion. Currently, automatic query expansion in patent search is mostly limited to computing co-occurring terms for the searchable features of the invention. Additional query terms are extracted automatically from patent documents based on entropy measures. Learning synonyms in the patent domain for automatic query expansion has been a difficult task. No dedicated sources providing synonyms for the patent domain, such as patent domain specific lexica or thesauri, are available. In this paper we focus on the highly professional search setting of patent examiners. In particular, we use query logs to learn synonyms for the patent domain. For automatic query expansion, we create term networks based on the query logs specifically for several USPTO patent classes. Experiments show good performance in automatic query expansion using these automatically generated term networks. Specifically, with a larger number of query logs for a specific patent US class available the performance of the learned term networks increases.

Efficient automatic search query formulation using phrase‐level analysis

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase‐level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two‐thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self‐formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2‐second response times at the highest loads tested).