Aspects Of Information Retrieval Research Articles

Enriching Domain Concepts with Qualitative Attributes (A Text Mining based Approach)

Attributes, whether qualitative or non-qualitative are the formal description of any real-world entity and are crucial in modern knowledge representation models like ontology. Though ample evidence for the amount of research done for mining non-qualitative attributes (like part-of relation) extraction from text as well as the Web is available in the wealth of literature, on the other side limited research can be found relating to qualitative attribute (i.e., size, color, taste etc.,) mining. Herein this research article an analytical framework has been proposed to retrieve qualitative attribute values from unstructured domain text. The research objective covers two aspects of information retrieval (1) acquiring quality values from unstructured text and (2) then assigning attribute to them by comparing the Google derived meaning or context of attributes as well as quality value (adjectives). The goal has been accomplished by using a framework which integrates Vector Space Modelling (VSM) with a probabilistic Multinomial Naive Bayes (MNB) classifier. Performance Evaluation has been carried out on two data sets (1) HeiPLAS Development Data set (106 adjective-noun exemplary phrases) and (2) a text data set in Medicinal Plant Domain (MPD). System is found to perform better with probabilistic approach compared to the existing pattern-based framework in the state of art

Enhancing information retrieval performance by using social analysis

In recent decades, researchers have realized that social networks are important sources for adhering to the evolution of many aspects of Information Retrieval (IR). These social networks have produced vast amounts of important information that are not covered by traditional IR systems. This improvement, which has become one of the main applications of IR, offers several social features such as the conversational exchange and the share of opinions by users, and the association of users with the same interests. This work introduces a model of Social Information Research that takes into account social information on users and exploits them as a second source of information for a given query. In the proposed IR system, the quality of results improved by the analysis of user’s needs and by comparing other users’ social data.

Some Preliminary Remarks on the Cenoscopic and Idioscopic aspects of Information Retrieval

Some Preliminary Remarks on the Cenoscopic and Idioscopic aspects of Information Retrieval

Report on the 2017 ACM SIGIR International Conference Theory of Information Retrieval (ICTIR?17)

This is a report on ICTIR'17, which continued its tradition of being the premier forum for presentation of research on theoretical aspects of Information Retrieval (IR). This includes theory in a broad sense, including conceptual papers that explore key concepts, theoretical papers that model concepts and/or relations between concepts, and papers that study theory in experimental or industrial settings. The importance of information access and information processing in industry and academia is growing in a revolutionary way, making the discussion on fundamental and long term aspects, and their relation to short term success and failure, more urgent than ever before. To highlight the increasingly strong connections between Information Retrieval and its neighboring disciplines, ICTIR'17 explicitly welcomed papers in IR areas that overlap with Human Information Access, Machine Learning, Natural Language Processing and Perception. ICTIR'17 in Amsterdam was a memorable conference. For those who like numbers: we had 142 attendees (biggest ICTIR ever), and 97 submissions with 52 accepted (largest ICTIR ever), 450/250 euro registration for everything (cheapest ICTIR ever), and for the first time in history the organization and keynotes of a leading IR conference where majority female (but selected for being outstanding researchers, of course).

OP71 Evidence-Based Searching For Health Technology Assessment – Keeping Up-to-Date With Summarized Research In Information Retrieval (SuRe Info)

INTRODUCTION:Increasing numbers of research papers about information retrieval for Health Technology Assessments (HTA), systematic reviews and other evidence syntheses are being published. It is time-consuming for information specialists to keep up-to-date with the latest developments in the field. To help searchers with this challenge, the Interest Group on Information Retrieval (IRG) of Health Technology Assessment International (HTAi) has compiled the best available research evidence on information retrieval aspects into an open-access web resource: Summarized Research in Information Retrieval for HTA (SuRe Info). The resource can be accessed at http://www.sure-info.orgMETHODS:The Sure Info team run topic-specific search strategies in selected relevant databases to identify information retrieval methods publications that fulfil the SuRe Info inclusion criteria. Eligible publications receive a structured abstract containing a brief critical appraisal. Key messages for search practice based on the appraisals and accepted best practice are summarized into topic-specific chapters.RESULTS:SuRe Info currently offers fourteen chapters, with more in development. SuRe Info chapters fall into two categories: (i) chapters about general search methods that are used across all types of research, such as how to develop search strategies and the availability and use of search filters, and (ii) chapters summarizing the methods to use when searching for specific aspects of HTA (as defined in the European Network for HTA (EUnetHTA) HTA Core Model®), including searching for evidence on clinical effectiveness and safety, and identifying economic evaluations. References at the end of each chapter are linked to appraisals of publications that have been used to develop each chapter. Links to the full-text of the publications are provided when freely available. The SuRe Info chapters are reviewed every six months and updated if new evidence is identified or if resources change.CONCLUSIONS:SuRe Info is a unique resource, identifying and summarizing current best research evidence on information retrieval aspects for HTA. It supports the timely uptake of potential efficiencies arising from new evidence that may be incorporated into the evidence identification processes of HTA organizations.

When time meets information retrieval: Past proposals, current plans and future trends

With the advent of Web search and the large amount of data published on the Web sphere, a tremendous amount of documents become strongly time-dependent. In this respect, the time dimension has been extensively exploited as a highly important relevance criterion to improve the retrieval effectiveness of document ranking models. Thus, a compelling research interest is going on the temporal information retrieval realm, which gives rise to several temporal search applications. In this article, we intend to provide a scrutinizing overview of time-aware information retrieval models. We specifically put the focus on the use of timeliness and its impact on the global value of relevance as well as on the retrieval effectiveness. First, we attempt to motivate the importance of temporal signals, whenever combined with other relevance features, in accounting for document relevance. Then, we review the relevant studies standing at the crossroads of both information retrieval and time according to three common information retrieval aspects: the query level, the document content level and the document ranking model level. We organize the related temporal-based approaches around specific information retrieval tasks and regarding the task at hand, we emphasize the importance of results presentation and particularly timelines to the end user. We also report a set of relevant research trends and avenues that can be explored in the future.

Report on the CIKM workshop on living labs for information retrieval evaluation

Evaluation is a central aspect of information retrieval (IR) research. In the past few years, a new evaluation methodology known as living labs has been proposed as a way for researchers to be able to perform in-situ evaluation. The first CIKM workshop on Living Labs for IR evaluation (LL'13) was held on 1st November 2013 in San Francisco, USA. The workshop consisted of an industrial keynote, four oral paper presentations, three demo presentations, and a discussion session. This report presents an overview of the scope and contents of the workshop and outlines the major outcomes.

A review on important aspects of Information Retrieval

A review on important aspects of Information Retrieval

Methodological developments in searching for studies for systematic reviews: past, present and future?

The Cochrane Collaboration was established in 1993, following the opening of the UK Cochrane Centre in 1992, at a time when searching for studies for inclusion in systematic reviews was not well-developed. Review authors largely conducted their own searches or depended on medical librarians, who often possessed limited awareness and experience of systematic reviews. Guidance on the conduct and reporting of searches was limited. When work began to identify reports of randomized controlled trials (RCTs) for inclusion in Cochrane Reviews in 1992, there were only approximately 20,000 reports indexed as RCTs in MEDLINE and none indexed as RCTs in Embase. No search filters had been developed with the aim of identifying all RCTs in MEDLINE or other major databases. This presented The Cochrane Collaboration with a considerable challenge in identifying relevant studies.Over time, the number of studies indexed as RCTs in the major databases has grown considerably and the Cochrane Central Register of Controlled Trials (CENTRAL) has become the best single source of published controlled trials, with approximately 700,000 records, including records identified by the Collaboration from Embase and MEDLINE. Search filters for various study types, including systematic reviews and the Cochrane Highly Sensitive Search Strategies for RCTs, have been developed. There have been considerable advances in the evidence base for methodological aspects of information retrieval. The Cochrane Handbook for Systematic Reviews of Interventions now provides detailed guidance on the conduct and reporting of searches. Initiatives across The Cochrane Collaboration to improve the quality inter alia of information retrieval include: the recently introduced Methodological Expectations for Cochrane Intervention Reviews (MECIR) programme, which stipulates 'mandatory’ and 'highly desirable’ standards for various aspects of review conduct and reporting including searching, the development of Standard Training Materials for Cochrane Reviews and work on peer review of electronic search strategies. Almost all Cochrane Review Groups and some Cochrane Centres and Fields now have a Trials Search Co-ordinator responsible for study identification and medical librarians and other information specialists are increasingly experienced in searching for studies for systematic reviews.Prospective registration of clinical trials is increasing and searching trials registers is now mandatory for Cochrane Reviews, where relevant. Portals such as the WHO International Clinical Trials Registry Platform (ICTRP) are likely to become increasingly attractive, given concerns about the number of trials which may not be registered and/or published. The importance of access to information from regulatory and reimbursement agencies is likely to increase. Cross-database searching, gateways or portals and improved access to full-text databases will impact on how searches are conducted and reported, as will services such as Google Scholar, Scopus and Web of Science. Technologies such as textual analysis, semantic analysis, text mining and data linkage will have a major impact on the search process but efficient and effective updating of reviews may remain a challenge.In twenty years’ time, we envisage that the impact of universal social networking, as well as national and international legislation, will mean that all trials involving humans will be registered at inception and detailed trial results will be routinely available to all. Challenges will remain, however, to ensure the discoverability of relevant information in diverse and often complex sources and the availability of metadata to provide the most efficient access to information. We envisage an ongoing role for information professionals as experts in identifying new resources, researching efficient ways to link or mine them for relevant data and managing their content for the efficient production of systematic reviews.

Information Retrieval in the Commentsphere

This article studies information retrieval tasks related to Web comments. Prerequisite of such a study and a main contribution of the article is a unifying survey of the research field. We identify the most important retrieval tasks related to comments, namely filtering, ranking, and summarization. Within these tasks, we distinguish two paradigms according to which comments are utilized and which we designate as comment-targeting and comment-exploiting . Within the first paradigm, the comments themselves form the retrieval targets. Within the second paradigm, the commented items form the retrieval targets (i.e., comments are used as an additional information source to improve the retrieval performance for the commented items). We report on four case studies to demonstrate the exploration of the commentsphere under information retrieval aspects: comment filtering, comment ranking, comment summarization and cross-media retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with comment-exploiting retrieval. Throughout the article, connections to information retrieval research are pointed out.

Applying human computation mechanisms to information retrieval

AbstractCrowdsourcing and Games with a Purpose (GWAP) have each received considerable attention in recent years. These two human computation mechanisms assist with tasks that cannot be solved by computers alone. Despite this increased attention, much of this transformation has been limited to a few aspects of Information Retrieval (IR). In this paper, we examine these two mechanisms' applicability to IR. Using an IR model, we apply criteria to determine the suitability of these crowdsourcing and GWAP mechanisms to each step of the model. Our analysis illustrates that these mechanisms can apply to several of these steps with good returns.

From people to entities

The exponential growth of digital information available in Enterprises and on the Web creates the need for search tools that can respond to the most sophisticated informational needs. Retrieving relevant documents is not enough anymore and finding entities rather than textual resources provides great support to the user both on the Web and in Enterprises. Many user tasks would be simplified if Search Engines would support typed search, and return entities instead of just Web pages. For example, an executive who tries to solve a problem needs to find people in the company who are knowledgeable about a certain topic. Aggregation of information spread over different documents is a key aspect in this process. Finding experts is a problem mostly considered in the Enterprise setting where teams for new projects need to be built and problems need to be solved by the right persons. In the first part of the thesis, we propose a model for expert finding based on the well consolidated vector space model for Information Retrieval and investigate its effectiveness. We can define Entity Retrieval by generalizing the expert finding problem to any entity. In Entity Retrieval the goal is to rank entities according to their relevance to a query (e.g., "Countries where I can pay in Euro"); the set of entities to be ranked is assumed to be loosely defined by a generic category, given in the query itself (e.g., countries), or by some example entities (e.g., Italy, Germany, France). In the second part of the thesis, we investigate different methods based on Semantic Web and Natural Language Processing techniques for solving these tasks both in Wikipedia and, generally, on the Web. Evaluation is a critical aspect of Information Retrieval. We contributed to the field of Information Retrieval evaluation by organizing an evaluation initiative for Entity Retrieval. Opinions and other relevant information about entities can be provided by different sources in different contexts. News articles report about events where entities are involved. In such setting the temporal dimension is critical as news stories develop over time and new entities appear in the story and others are not relevant anymore. In the third part of this thesis, we study the problem of Entity Retrieval for news applications and the importance of the news trail history (i.e., past related articles) to determine the relevant entities in current articles. We also study opinion evolution about entities. In the last years, the blogosphere has become a vital part of the Web, covering a variety of different points of view and opinions on political and event-related topics such as immigration, election campaigns, or economic developments. We propose a method for automatically extracting public opinion about specific entities from the blogosphere. Available online at http://www.gianlucademartini.net/research/phd/.

Introduction to special issue on the second international conference on the theory of information retrieval

This special issue of the Journal of Information Retrieval contains selected and substantially extended works presented and published at the second International Conference on the Theory of Information Retrieval (ICTIR), held at Microsoft Research in Cambridge, UK, on 10–11 September 2009. The ICTIR conference series is a biennial international conference that provides an opportunity for the presentation of the latest work describing advances in the theoretical and formal aspects of Information Retrieval (IR). The conference is run under the auspices of the British Computer Society’s Information Retrieval Specialist Group and the proceedings are now being published in the Lecture Notes in Computer Science (LNCS) series by Springer. The first ICTIR was held in Budapest in October 2007, organized by Sandor

SEMANTIC FIELD: A THEORETICAL PERSPECTIVE OF MODELING INFORMATION RETRIEVAL

Information retrieval has been paid much attention and it is widely studied and applied in real world paradigms. For various aspects of information retrieval, various approaches have been proposed from various perspectives. It is necessary to provide a formally-unified and physically-interpretable model for classical problems in information retrieval (e.g., document classification, authority-page selection, and keyword extraction, etc.). In this paper we propose a theoretical model, called semantic field, inspired by the theories of lexical semantics and electrostatic field. Based on this physical model, information retrieval can be viewed from a theoretical perspective and interpreted by people's physical intuitions and natural heuristics. Centered on the concept of semantic field, we give some relevant properties, including semantic affinity, semantic coacervation degree and radiation of a semantic source. As the representative application of the proposed semantic field model, a novel method for automatic keyword extraction is discussed, and the feasibility is verified by corresponding experiments.

Introduction to special issue on contextual information retrieval systems

Context affects all aspects of Information Retrieval. A searcher's context affects how they interact with a retrieval system, what type of response they expect from a system and how they make decisions about the information objects they retrieve. Information contexts are formed through the creation, use and linkage of these objects within an information resource and the context of use impacts on how we evaluate retrieval systems. Our understanding of user and information context also influences how we design and construct retrieval systems themselves (Crestani and Ruthven, 2005; Ingwersen and Jarvelin, 2005; Ruthven et al., 2006). The ability to respond to context could allow retrieval systems to learn and predict what information a searcher needs, decide how and when this information should be presented and distinguish between different search goals and searcher preferences. How the designers of search systems should respond to context is an important issue for the development of retrieval systems and the appropriate use of context raises many research questions. For example, what aspects of a searcher's context can we recognise, how should context be utilised within a retrieval system to improve search performance, how can we exploit shared contexts and contexts over time, and how should we evaluate contextual IR systems?

ACM SIGIR workshop on mathematical/formal methods in information retrieval MF/IR 2005

The previous five MF/IR workshops showed that the mathematical/formal results achieved in Information Retrieval (IR) could be organized into a coherent theoretical framework, that they brought new knowledge to IR, and that mathematical/formal research in IR can stand as a specialized research area of IR. Therefore the purpose of the MF/IR 2005 was to promote discussion and interaction among those with theoretical and applicative research interests in mathematical/formal aspects of Information Retrieval coming from a large spectrum of different IR fields, and also at being a forum for the presentation of both theoretical and applicative results (e.g., foundational issues; description and/or integration of models; retrieval applications; mathematical/formal techniques, properties and structures in IR; existing and/or new theories and theoretical aspects, interdisciplinary approaches) using formal and mathematical approaches like Sets, Vectors, Similarity Functions, Probability, Algebra, Topology, Metric Spaces, Geometry, Logics, Graph Theory.

The Impact of Specificity on the Retrieval Power of a UDC-Based Multilingual Thesaurus

SUMMARY The article describes the research done over a bibliographic database in order to show the impact the specificity of the knowledge organising tools may have on information retrieval (IR). For this purpose two multilingual Universal Decimal Classification (UDC) based thesauri having different degrees of specificity are considered. Issues of harmonising a classificatory structure with a thesaurus structure are introduced, and significant aspects of information retrieval in a multilingual environment are examined in an extensive manner. Aspects of complementarity are discussed with particular emphasis on the real impact produced on IR by alternative search facilities. Finally, a number of conclusions are formulated as they arise from the study.

Report on ACM SIGIR workshop on mathematical/formal methods in information retrieval

The previous two MF/IR workshops (ACM SIGIR 2000 MF/IR 2000, Athens, Greece, and ACM SIGIR 2001 MF/IR 2001, New Orleans, USA) showed that the mathematical/formal results achieved in Information Retrieval (IR) could be organized into a coherent theoretical framework, that they brought new knowledge to IR, and that mathematical/formal research in IR can stand as a specialized research area of IR. Therefore the purpose of the MF/IR 2002, which was the third in row, workshop was, on the one hand, to continue and enhance the results obtained so far, and on the other hand, to present, discuss, analyze, integrate the newer/newest results. MF/IR 2002 also aimed at promoting discussion and interaction among those with theoretical and applicative research interests in mathematical/formal aspects of Information Retrieval, and also at being a forum for the presentation of both theoretical and applicative results (e.g., foundational issues; description and/or integration of models; retrieval applications; mathematical/formal techniques, properties and structures in IR; existing and/or new theories and theoretical aspects).The following areas were addressed: Information Retrieval, Information Filtering, Information, Mining, Indexing and Retrieval, Hypermedia, World Wide Web Retrieval, Digital Libraries, Evaluation, Human Computer Interaction, User Modeling, Semantic Web and Ontologies, where the different entities involved (e.g., documents, queries, indexing, retrieval, relevance, effectiveness, users, etc.) were modeled using any of, but not necessarily limited to, the following approaches: Classical Sets, Fuzzy Sets, Rough Sets, Vectors, Linear Space, Similarity Functions, Probability, Theory of Uncertainty, Functional Analysis, Algebra, Topology, Metric Spaces, Euclidean Geometry, Non-Euclidean Geometries, Boolean Logic, Non-standard Logics, Fuzzy Logic, Quantum Logic, Matroid Theory, Graph Theory.

Report on ACM sigir workshop on mathematical/formal methods in information retrieval

The previous two MF/IR workshops (ACM SIGIR 2000 MF/IR 2000, Athens, Greece, and ACM SIGIR 2001 MF/IR 2001, New Orleans, USA) showed that the mathematical/formal results achieved in Information Retrieval (IR) could be organized into a coherent theoretical framework, that they brought new knowledge to IR, and that mathematical/formal research in IR can stand as a specialized research area of IR. Therefore the purpose of the MF/IR 2002, which was the third in row, workshop was, on the one hand, to continue and enhance the results obtained so far, and on the other hand, to present, discuss, analyze, integrate the newer/newest results. MF/IR 2002 also aimed at promoting discussion and interaction among those with theoretical and applicative research interests in mathematical/formal aspects of Information Retrieval, and also at being a forum for the presentation of both theoretical and applicative results (e.g., foundational issues; description and/or integration of models; retrieval applications; mathematical/formal techniques, properties and structures in IR; existing and/or new theories and theoretical aspects).The following areas were addressed: Information Retrieval, Information Filtering, Information, Mining, Indexing and Retrieval, Hypermedia, World Wide Web Retrieval, Digital Libraries, Evaluation, Human Computer Interaction, User Modeling, Semantic Web and Ontologies, where the different entities involved (e.g., documents, queries, indexing, retrieval, relevance, effectiveness, users, etc.) were modeled using any of, but not necessarily limited to, the following approaches: Classical Sets, Fuzzy Sets, Rough Sets, Vectors, Linear Space, Similarity Functions, Probability, Theory of Uncertainty, Functional Analysis, Algebra, Topology, Metric Spaces, Euclidean Geometry, Non-Euclidean Geometries, Boolean Logic, Non-standard Logics, Fuzzy Logic, Quantum Logic, Matroid Theory, Graph Theory.

Maxi-Adjustment and Possibilistic Deduction for Adaptive Information Agents

The expressive power of logic is believed to be able to model most of the fundamental aspects of information retrieval (IR). However, it is also understood that classical logic is ineffective for handling partiality and uncertainty in IR. Applying non-classical logics such as the AGM belief revision logic and the possibilistic logic to adaptive information retrieval is appealing since they provide a powerful and rigorous framework to model partiality and uncertainty inherent in any IR processes. The maxi-adjustment method, which is an effective computational apparatus of the AGM paradigm, is applied to develop the learning components of the adaptive information agents. Essentially, maxi-adjustment allows the partial representation K of a user's information needs N to be refined gradually based on the user's relevance feedback t. Generally speaking, learning in adaptive information agents is characterised by the AGM belief revision Kt*. On the other hand, possibilistic logic supports a gradated assessment of the uncertainty arising from matching K with the imperfect characterisation d of an information object D. Information matching in adaptive information agents is underpinned by K ƕ d, where ƕ is the possibilistic inference relation. This paper illustrates how maxi-adjustment and possibilistic deduction can be applied to develop the adaptive information agents. Their impact on the agents learning autonomy and explanatory power is also discussed.