Automatic Query Expansion Research Articles

Rank fusion and semantic genetic notion based automatic query expansion model

Abstract Query expansion term selection methods are really very important for improving the accuracy and efficiency of pseudo-relevance feedback based automatic query expansion for information retrieval system by removing irrelevant and redundant terms from the top retrieved feedback documents corpus with respect to user query. Individual query expansion term selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual query expansion term selection method that would outperform other individual query expansion term selection methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual query expansion term selection methods. Second, we propose a model for combining multiple query expansion term selection methods by using rank combination approach, called multiple ranks combination based query expansion. Third, semantic filtering is used to filter semantically irrelevant term obtained after combining multiple query expansion term selection methods, called ranks combination and semantic filtering based query expansion. Fourth, the genetic algorithm is used to make an optimal combination of query terms and candidate term obtained after rank combination and semantic filtering approach, called semantic genetic filtering and rank combination based query expansion. Our experimental results demonstrated that our proposed approaches achieved significant improvement over each individual query expansion term selection method and related state-of-the-art approaches.

A novel Fuzzy-PSO term weighting automatic query expansion approach using combined semantic filtering

Information Retrieval system retrieves relevant documents from large datasets. Automatic Query Expansion (AQE) is one of the approaches to enhance IR performance by adding additional terms to original query. The selection of suitable additional terms for AQE is a crucial task. Term weighting method is one of the ways to deal with such a problem. This paper presents a new term weighting based AQE approach to retrieve more relevant documents from data corpus. The proposed approach comprises of three major steps. First step determines the optimal weights of different IR evidences for different terms using Particle Swarm Optimization (PSO). Fuzzy logic technique is used to improve performance of PSO by controlling inertia and acceleration coefficients during the optimization. Co-occurrence score is introduced as new IR evidence in the proposed approach. Second step is focused on removal of noisy terms by using new combined semantic filtering method. Third step reweights the terms using Rocchio method. The proposed approach is compared with recently developed automatic query expansion approaches in terms of performance measures such as precision, recall, F-measure and MAP (Mean Average Precision). Three benchmark datasets CACM, CISI and TREC-3 are used to verify the results. The proposed approach is found better than other approaches according to results obtained for these benchmark datasets.

Quary Expansion Using Local and Global Document Analysis

Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention has focused on techniques that analyze the corpus to discover word relationship (global techniques) and those that analyze documents retrieved by the initial query ( local feedback). In this paper, we compare the effectiveness of these approaches and show that, although global analysis haa some advantages, local analysia is generally more effective. We also show that using global analysis techniques.

Web-Scale Discovery Services Retrieve Relevant Results in Health Sciences Topics Including MEDLINE Content

A Review of:
 Hanneke, R., & O’Brien, K. K. (2016). Comparison of three web-scale discovery services for health sciences research. Journal of the Medical Library Association, 104(2), 109-117. http://dx.doi.org/10.3163/1536-5050.104.2.004
 
 Abstract
 
 Objective – To compare the results of health sciences search queries in three web-scale discovery (WSD) services for relevance, duplicate detection, and retrieval of MEDLINE content.
 
 Design – Comparative evaluation and bibliometric study.
 
 Setting – Six university libraries in the United States of America.
 
 Subjects – Three commercial WSD services: Primo, Summon, and EBSCO Discovery Service (EDS).
 
 Methods – The authors collected data at six universities, including their own. They tested each of the three WSDs at two data collection sites. However, since one of the sites was using a legacy version of Summon that was due to be upgraded, data collected for Summon at this site were considered obsolete and excluded from the analysis. 
 
 The authors generated three questions for each of six major health disciplines, then designed simple keyword searches to mimic typical student search behaviours. They captured the first 20 results from each query run at each test site, to represent the first “page” of results, giving a total of 2,086 total search results. These were independently assessed for relevance to the topic. Authors resolved disagreements by discussion, and calculated a kappa inter-observer score. They retained duplicate records within the results so that the duplicate detection by the WSDs could be compared.
 
 They assessed MEDLINE coverage by the WSDs in several ways. Using precise strategies to generate a relevant set of articles, they conducted one search from each of the six disciplines in PubMed so that they could compare retrieval of MEDLINE content. These results were cross-checked against the first 20 results from the corresponding query in the WSDs. To aid investigation of overall coverage of MEDLINE, they recorded the first 50 results from each of the 6 PubMed searches in a spreadsheet. During data collection at the WSD sites, they searched for these references to discover if the WSD tool at each site indexed these known items.
 
 Authors adopted measures to control for any customisation of the product setup at each data collection site. In particular, they excluded local holdings from the results by limiting the searches to scholarly, peer-reviewed articles.
 
 Main results – Authors reported results for 5 of the 6 sites. All of the WSD tools retrieved between 50-60% relevant results. EDS retrieved the highest number of relevant records (195/360 and 216/360), while Primo retrieved the lowest (167/328 and 169/325). There was good observer agreement (k=0.725) for the relevance assessment. The duplicate detection rate was similar in EDS and Summon (between 96-97% unique articles), while the Primo searches returned 82.9-84.9% unique articles.
 
 All three tools retrieved relevant results that were not indexed in MEDLINE, and retrieved relevant material indexed in MEDLINE that was not retrieved in the PubMed searches. EDS and Summon retrieved more non-MEDLINE material than Primo. EDS performed best in the known-item searches, with 300/300 and 299/300 items retrieved, while Primo performed worst with 230/300 and 267/300 items retrieved.
 
 The Summon platform features an “automated query expansion” search function, where user-entered keywords are matched to related search terms and these are automatically searched along with the original keyword. The authors observed that this function resulted in a wholly relevant first page of results for one of the search questions tested in Summon.
 
 Conclusion – While EDS performed slightly better overall, the difference was not great enough in this small sample of test sites to recommend EDS over the other tools being tested. The automated query expansion found in Summon is a useful function that is worthy of further investigation by the WSD vendors. The ability of the WSDs to retrieve MEDLINE content through simple keyword searches demonstrates the potential value of using a WSD tool in health sciences research, particularly for inexpert searchers.

Query Expansion for Content-Based Similarity Search Using Local and Global Features

This article presents an efficient and totally unsupervised content-based similarity search method for multimedia data objects represented by high-dimensional feature vectors. The assumption is that the similarity measure is applicable to feature vectors of arbitrary length. During the offline process, different sets of features are selected by a generalized version of the Laplacian Score in an unsupervised way for individual data objects in the database. Online retrieval is performed by ranking the query object in the feature spaces of candidate objects. Those candidates for which the query object is ranked highly are selected as the query results. The ranking scheme is incorporated into an automated query expansion framework to further improve the semantic quality of the search result. Extensive experiments were conducted on several datasets to show the capability of the proposed method in boosting effectiveness without losing efficiency.

Constructing a biodiversity terminological inventory.

The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications.

Ranks Aggregation and Semantic Genetic Approach based Hybrid Model for Query Expansion

Effective query expansion terms selection methods are really very important for improving the accuracy and efficiency of Pseudo-Relevance Feedback (PRF) based automatic query expansion techniques in information retrieval system. These methods remove irrelevant and redundant terms from the top retrieved feedback documents with respect to a user query. Individual terms selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual expansion terms selection method that would outperform other individual methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual terms selection methods. Second, we propose a model for combining multiple expansion terms selection methods by using a variety of ranks combining approaches. Third, semantic filtering used to filter out semantically irrelevant terms obtained after combining multiple terms selection methods. Fourth, the Genetic Algorithm used to make an optimal combination of query terms and candidate expansion terms obtained by applying ranks combination and semantic filtering approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over each individual terms selection methods and related state-of-the-arts approaches.

Local and global approaches for unsupervised image annotation

Image annotation is the task of assigning keywords to images with the goal of facilitating their organization and accessibility options (e.g., searching by keywords). Traditional annotation methods are based on supervised learning. Although being very effective, these methods require of large amounts of manually labeled images, and are limited in the sense that images can only be labeled with concepts seen during the training phase. Unsupervised automatic image annotation (UAIA) methods, on the other hand, neglect strongly-labeled images and instead rely on huge collections of unstructured text containing images for the annotation. In addition to not requiring labeled images, unsupervised techniques are advantageous because they can assign (virtually) any concept to an image. Despite these benefits, unsupervised methods have not been widely studied in image annotation, a reason for this is the lack of a reference framework for UAIA. In this line, this paper introduces two effective methods for UAIA in the context of a common framework inspired in the way a query is expanded throughout Automatic Query Expansion (AQE) in information retrieval. On the one hand, we describe a local method that processes text information associated to images retrieved when using the image to annotate as query, several methods from the state of the art can be described under this formulation. On the other hand, we propose a global method that pre-process offline the reference collection to identify visual-textual associations that are later used for annotation. Both methods are extensively evaluated in benchmarks for large-scale UAIA. Experimental results show the competitiveness of both strategies when compared to the state of the art. We foresee the AQE-based framework will pave the way for the development of alternative and effective methods for UAIA.

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.

Methods of Intellectual Data Processing and Presentation in Multi-Subject Information Systems of Industrial Enterprises

In this paper, the problem of improving the technology of formation and functioning of multi-subject intellectualized information systems of industry is considered. As a solution to this problem, we propose an architecture of multi-subject IS information system of an industrial enterprise; an automated method of formation of a semantic domain model based on the principle of as an expert.; a method of formation of cognitive user interfaces adapted for different categories of users; and search method , providing automated query expansion and evaluation of the relevance of search results based on a joint of analysis of the formal mental models and semantic models of a domain with the subtractive relationship

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

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.

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.

Automated mapping of clinical terms into SNOMED-CT. An application to codify procedures in pathology.

Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-to-concept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0% precision and 51.4% recall, providing a substantial improvement of recall (28% and 60%) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.

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.

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.

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.