Information Retrieval Features Research Articles

Preliminary Perspectives on Information Passing in the Intelligence Community

Analyst sensemaking research typically focuses on individual or small groups conducting intelligence tasks. This has helped understand information retrieval tasks and how people communicate information. As a part of the grand challenge of the Summer Conference on Applied Data Science (SCADS) to build a system that can generate tailored daily reports (TLDR) for intelligence analysts, we conducted a qualitative interview study with analysts to increase understanding of information passing in the intelligence community. While our results are preliminary, we expect that this work will contribute to a better understanding of the information ecosystem of the intelligence community, how institutional dynamics affect information passing, and what implications this has for a TLDR system. This work describes our involvement in and work completed during SCADS. Although preliminary, we identify that information passing is both a formal and informal process and often follows professional networks due especially to the small population and specialization of work. We call attention to the need for future analysis of information ecosystems to better support tailored information retrieval features.

SeaRank: relevance prediction based on click models in a reinforcement learning framework

PurposeUser feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination as pre- and post-estimators of the actual relevance. They also assume that User Satisfaction is a function of the actual relevance. This paper extends the authors' previous work by building a reinforcement learning (RL) model to predict the relevance. The Attractiveness, Examination and User Satisfaction are estimated using a limited number of the features of the utilized benchmark data set and then they are incorporated in the construction of an RL agent. The proposed RL model learns to predict the relevance label of documents with respect to a given query more effectively than the baseline RL models for those data sets.Design/methodology/approachIn this paper, User Satisfaction is used as an indication of the relevance level of a query to a document. User Satisfaction itself is estimated through Attractiveness and Examination, and in turn, Attractiveness and Examination are calculated by the random forest algorithm. In this process, only a small subset of top information retrieval (IR) features are used, which are selected based on their mean average precision and normalized discounted cumulative gain values. Based on the authors' observations, the multiplication of the Attractiveness and Examination values of a given query–document pair closely approximates the User Satisfaction and hence the relevance level. Besides, an RL model is designed in such a way that the current state of the RL agent is determined by discretization of the estimated Attractiveness and Examination values. In this way, each query–document pair would be mapped into a specific state based on its Attractiveness and Examination values. Then, based on the reward function, the RL agent would try to choose an action (relevance label) which maximizes the received reward in its current state. Using temporal difference (TD) learning algorithms, such as Q-learning and SARSA, the learning agent gradually learns to identify an appropriate relevance label in each state. The reward that is used in the RL agent is proportional to the difference between the User Satisfaction and the selected action.FindingsExperimental results on MSLR-WEB10K and WCL2R benchmark data sets demonstrate that the proposed algorithm, named as SeaRank, outperforms baseline algorithms. Improvement is more noticeable in top-ranked results, which usually receive more attention from users.Originality/valueThis research provides a mapping from IR features to the CM features and thereafter utilizes these newly generated features to build an RL model. This RL model is proposed with the definition of the states, actions and reward function. By applying TD learning algorithms, such as the Q-learning and SARSA, within several learning episodes, the RL agent would be able to learn how to choose the most appropriate relevance label for a given pair of query–document.

An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function.

Even without formal training, humans experience a wide range of emotions in response to changes in musical features, such as tonality and rhythm, during music listening. While many studies have investigated how isolated elements of tonal and rhythmic properties are processed in the human brain, it remains unclear whether these findings with such controlled stimuli are generalizable to complex stimuli in the real world. In the current study, we present an analytical framework of a linearized encoding analysis based on a set of music information retrieval features to investigate the rapid cortical encoding of tonal and rhythmic hierarchies in natural music. We applied this framework to a public domain EEG dataset (OpenMIIR) to deconvolve overlapping EEG responses to various musical features in continuous music. In particular, the proposed framework investigated the EEG encoding of the following features: tonal stability, key clarity, beat, and meter. This analysis revealed a differential spatiotemporal neural encoding of beat and meter, but not of tonal stability and key clarity. The results demonstrate that this framework can uncover associations of ongoing brain activity with relevant musical features, which could be further extended to other relevant measures such as time-resolved emotional responses in future studies.

Click models inspired learning to rank

PurposeIncorporating users’ behavior patterns could help in the ranking process. Different click models (CMs) are introduced to model the sophisticated search-time behavior of users among which commonly used the triple of attractiveness, examination and satisfaction. Inspired by this fact and considering the psychological definitions of these concepts, this paper aims to propose a novel learning to rank by redefining these concepts. The attractiveness and examination factors could be calculated using a limited subset of information retrieval (IR) features by the random forest algorithm, and then they are combined with each other to predicate the satisfaction factor which is considered as the relevance level.Design/methodology/approachThe attractiveness and examination factors of a given document are usually considered as its perceived relevance and the fast scan of its snippet, respectively. Here, attractiveness and examination factors are regarded as the click-count and the investigation rate, respectively. Also, the satisfaction of a document is supposed to be the same as its relevance level for a given query. This idea is supported by the strong correlation between attractiveness-satisfaction and the examination-satisfaction. Applying random forest algorithm, the attractiveness and examination factors are calculated using a very limited set of the primitive features of query-document pairs. Then, by using the ordered weighted averaging operator, these factors are aggregated to estimate the satisfaction.FindingsExperimental results on MSLR-WEB10K and WCL2R data sets show the superiority of this algorithm over the state-of-the-art ranking algorithms in terms of P@n and NDCG criteria. The enhancement is more noticeable in top-ranked items which are reviewed more by the users.Originality/valueThis paper proposes a novel learning to rank based on the redefinition of major building blocks of the CMs which are the attractiveness, examination and satisfactory. It proposes a method to use a very limited number of selected IR features to estimate the attractiveness and examination factors and then combines these factors to predicate the satisfactory which is regarded as the relevance level of a document with respect to a given query.

Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.

Free and Low-Cost Twitter Research Software Tools for Social Science

Twitter is an important object of research in social science studies, and the emergence of numerous Twitter software tools has provided researchers with the possibility of gaining insights into Twitter data; however, at the same time, early stage researchers may experience difficulties in selecting the most suitable tool for their own projects. Due to personal or institutional financial constraints, the purchase of commercial software is not a wise investment for all researchers. Hence, this article aims to provide a review of nine different free-of-charge and low-cost software tools for studying Twitter and highlights their advantages and disadvantages, in order to serve as a guide for social science scholars. This review is divided into two parts: background information and data retrieval features of the software tools, and their data analysis features. At the end of the review, several recommendations and suggestions are made for the use of these tools.

A learning to rank approach for cross-language information retrieval exploiting multiple translation resources

AbstractCross-language information retrieval (CLIR), finding information in one language in response to queries expressed in another language, has attracted much attention due to the explosive growth of multilingual information in the World Wide Web. One important issue in CLIR is how to apply monolingual information retrieval (IR) methods in cross-lingual environments. Recently, learning to rank (LTR) approach has been successfully employed in different IR tasks. In this paper, we use LTR for CLIR. In order to adapt monolingual LTR techniques in CLIR and pass the barrier of language difference, we map monolingual IR features to CLIR ones using translation information extracted from different translation resources. The performance of CLIR is highly dependent on the size and quality of available bilingual resources. Effective use of available resources is especially important in low-resource language pairs. In this paper, we further propose an LTR-based method for combining translation resources in CLIR. We have studied the effectiveness of the proposed approach using different translation resources. Our results also show that LTR can be used to successfully combine different translation resources to improve the CLIR performance. In the best scenario, the LTR-based combination method improves the performance of single-resource-based CLIR method by 6% in terms of Mean Average Precision.

СЕРВІСИ ЕЛЕКТРОННИХ КАТАЛОГІВ ЯК ЗАСІБ РОЗВИТКУ ІНФОРМАЦІЙНОЇ КОМПЕТЕНТНОСТІ КОРИСТУВАЧІВ НАУКОВОЇ БІБЛІОТЕКИ

The author of the article raises a question about library users; she is concerned that they are not well aware of modern library information technologies and how to use electronic reference and bibliographic resources, in particular databases of articles and materials taken from periodicals. In order to improve user information competence the author of the article presents functional and information retrieval features of the subject “Periodicals” bibliographic database of the electronic catalogue of V. Sukhomlynskyi State Scientific and Pedagogical Library of Ukraine, a coordinating and scientific methodical centre of the system of education libraries of Ukraine. The forms of propagating the resource to users in particular using Internet information services are described.

Search Engines for User Centric Information Retrieval and Scholarly Communication

Search Engine (SE) is a software system that is designed to search for information on the World Wide Web. Search Engine helps users to find rapidly relevant information. Search Engines were established based on the traditional database and information retrieval methods and also many other algorithms have been added them to improve their search. The Interconnection of world through the use of Internet and Search Engines has changed the profession of Librarianship. The paper describes the Search Engine Architecture, Working Process, Information Retrieval Features, Scholarly Communication and different types of search engines and their usefulness in the field of Science, Arts, Social Sciences, Engineering, Medical, Legal other aspects. Search engines such as, Scirus, Infomine, Refseek, TechXtra, PubMed, Catalaw, Google, Bing, and Yahoo, are presented with their usefulness and the disciplines in which they are used. Search Engines are very important for retrieving information and scholarly communication in achieving academic excellence as per the changing needs of users in Higher Education. Search engines create an environment whereby scholars and researchers can easily share, publish their research findings and be able to locate and also retrieve the required information. Thus, the paper explains the usefulness of SEs to the wider academic community that prepares academicians to face challenges in Higher Education.

Research on Multi-Agent Intelligent Information Retrieval Model

The intelligent information retrieval model based on multi-agent imitates and synthesizes the features of search engine and information retrieval in the website. It realizes high recall-precision and high precision of the retrieval effectively, and has good flexibility and expansibility. It is convenient for users to configure and choose target source of retrieval.

Context-based health information retrieval

The Web has profoundly changed the way we access information. The increase in the amount of information available and the easy access to the Web have contributed to the popularity of search in this medium. While this is true in several domains, it is particularly significant in the health domain. In fact, search for health information is the third most popular online activity and it is performed by almost 3 out of 4 American Internet users. Besides its popularity, health web search is particularly important to inform health consumers, encouraging them to become more participatory in their health management. In the health domain, the context surrounding the search is extremely rich and we believe it can contribute to improve system's performance. Although information retrieval has made significant progresses supporting its retrieval methods solely on the query and document collection, it has been recognized that context should be explored. Context can, for example, be used to disambiguate a query, to retrieve documents adjusted to the expertise of the searcher or to adjust documents to the patient's medical record. In this dissertation we investigate the effects of context features in consumer health information retrieval. In addition, we propose and evaluate strategies to use context features in query formulation support. The popularity of web search on the health domain made us focus on Web retrieval. We opt to concentrate on health consumers due to the lack of research focusing on this specific public. The thesis behind our work is that consumer health information retrieval is affected by context features, which can be used in query formulation support to improve retrieval performance. In three exploratory studies, we analyze how a large number of features affect different outcomes of the retrieval process. Based on findings from these studies and on the importance of query formulation support in a domain where terminology can be a barrier and translations to other languages are not always obvious, we explored the impact of query translations in users with different characteristics. Based on the assumption that a query using a language that is popular on the Web may easily reach high-quality contents, one study analyzes the effects of translating a query to the English language in users with different levels of English proficiency. Findings show that users having, at least, elementary English proficiency benefit from English query suggestions. The other studies focus on query terminology translation, that is, translation between lay and medico-scientific terminology, considering users' health literacy and topic familiarity. Although several strategies have been previously proposed to overcome the terminology gap between health consumers and web documents, none considers users health literacy and topic familiarity. Findings suggest that users with inadequate health literacy and users who are unfamiliar with the topic should be provided with recommendations of lay queries. On the other hand, users with higher health literacy or higher topic familiarity should be given alternative queries using medico-scientific terminology. Based on the above findings, we developed a query suggestion system that, using domain information gathered from an existing consumer health vocabulary, identifies the medical concepts included in the query and returns four types of suggestions combining the Portuguese or English languages with the lay or medico-scientific terminologies. We found that suggestions offered by the system had a good acceptance, with English suggestions being preferred to Portuguese ones in basic and proficient users and medico-scientific suggestions being preferred to lay ones in higher levels of health literacy. We concluded that a retrieval system including the implemented suggestion strategy without any kind of personalization tends to be better than a system without suggestions with respect to precision, correctness of the resulting knowledge and also of its incorrectness. We also concluded that this system tends to be slightly worse in terms of motivational relevance. Of these, only the incorrectness difference is significant. This is extremely relevant in the health domain where incorrect information can have serious consequences. Moreover, we also found that the personalization of this system to users' English proficiency and health literacy, biasing users towards the suggestions more beneficial to them, outperforms the system without personalization, in terms of medical accuracy of the obtained knowledge.

Searching BioModels Database

*Aim*The increasing number of computational models of biological systems demands a better support for search facilities. One possible improvement is to enable ranked retrieval of models based on existing model annotation. Applying existing Information Retrieval (IR) techniques to computational models, we have implemented a new search strategy in BioModels Database. In this search strategy the index of search terms contains not only the model descriptions in the database, but is expanded by including ontological and textual information about a model and its constituents retrieved from external sources. To add more background knowledge, we additionally index the abstracts of the reference publications. The new search system was integrated into BioModels Database and is based on a Lucene implementation. The model index includes 454 models and 154854 terms, the semantic index 2353 constituents and 437244 terms. Different sources are incorporated in the retrieval and ranking process, the most important of them via MIRIAM annotations to models and model constituents.The search results are sorted with regard to different aspects, i.e. model authors, dates, constituents, or SBML elements. Several state-of-the-art information retrieval features are supported, such as fuzzy, proximity or range search. As a result the user is enabled to specify the criteria relevant for a search, and is provided with a ranked result set of relevant models. The search was implemented in the demo version of BioModels Database and is available at http://www.ebi.ac.uk/biomodels-demo/.A detailed description of the technical realisation is given in:[Henkel et al., 2010]: Henkel, R., Endler, L., Peters, A., Novere, N., and Waltemath, D. (2010). Ranked retrieval of computational biology models. BMC Bioinformatics, 11(1):423+..

Searching BioModels Database

Abstract*Aim*The increasing number of computational models of biological systems demands a better support for search facilities. One possible improvement is to enable ranked retrieval of models based on existing model annotation. Applying existing Information Retrieval (IR) techniques to computational models, we have implemented a new search strategy in BioModels Database. In this search strategy the index of search terms contains not only the model descriptions in the database, but is expanded by including ontological and textual information about a model and its constituents retrieved from external sources. To add more background knowledge, we additionally index the abstracts of the reference publications. Results The new search system was integrated into BioModels Database and is based on a Lucene implementation. The model index includes 454 models and 154854 terms, the semantic index 2353 constituents and 437244 terms. Different sources are incorporated in the retrieval and ranking process, the most important of them via MIRIAM annotations to models and model constituents.The search results are sorted with regard to different aspects, i.e. model authors, dates, constituents, or SBML elements. Several state-of-the-art information retrieval features are supported, such as fuzzy, proximity or range search. As a result the user is enabled to specify the criteria relevant for a search, and is provided with a ranked result set of relevant models. The search was implemented in the demo version of BioModels Database and is available at http://www.ebi.ac.uk/biomodels-demo/.A detailed description of the technical realisation is given in:[Henkel et al., 2010]: Henkel, R., Endler, L., Peters, A., Novere, N., and Waltemath, D. (2010). Ranked retrieval of computational biology models. BMC Bioinformatics, 11(1):423+..

Librarians and transportationists: views and usage of US transportation database

PurposeTransportation librarians name the Transportation Information Research Services (TRIS) database as a critical resource to meet research needs of the US transportation community. This paper seeks to question the claim with evidence from a small‐scale pilot study seeking to answer the question, “How is TRIS Online meeting the needs of transportation professionals?” The paper also aims to offer an outline of the information‐seeking behaviours of transportationists who participated in the pilot study.Design/methodology/approachUsing a mixed‐methods pilot study, researchers present observed information‐seeking behaviours of transportation professionals, compared with formal studies about the user group and the consensus of library and information professionals who serve the community.FindingsQuestions remain about the efficacy of TRIS serving its target audience. The results demonstrate that search results received mixed reviews from users and they favoured keyword searching to the near total exclusion of the sophisticated information retrieval features that librarians/information professionals maintain.Research limitations/implicationsThe study focuses on a specific, one‐time interaction between a small pool of professionals and an online database. The results are highly valuable but should not be generalized to describe the information‐seeking behaviour of all transportation professionals.Practical implicationsThe researchers recommend that all elements of this pilot study remain, should the project be reproduced on a larger scale; it provides a feasible framework and yields data necessary for extensive analysis.Originality/valueBaldwin notes, “Although there is a general assumption that a large portion of transportation‐related materials are available on the internet, few, if any, studies have been done on the topic. For most of the transportation community TRIS Online is seen as the most important single means of identifying transportation‐related information resources”.

Tightly-coupled spatial database features in the Odysseus/OpenGIS DBMS for high-performance

Conventional object-relational database management system (ORDBMS) vendors provide extension mechanisms for adding user-defined types and functions to their own DBMSs. Here, the extension mechanisms are implemented using a high-level (typically, SQL-level) interface. We call this mechanism loose-coupling. The advantage of loose-coupling is that it is easy to implement. However, it is not preferable for implementing new data types and operations in large databases when high performance is required. We have earlier proposed the tight-coupling architecture (Whang et al. 2002, 2005) to satisfy this requirement. In tight-coupling, new data types and operations are integrated into the core of the DBMS engine in the extensible type layer. Thus, they are supported in a consistent manner with high performance. This tight-coupling architecture is being used to incorporate information retrieval features and spatial database features into the Odysseus ORDBMS that has been under development at KAIST/AITrc for 19 years. In this paper, we introduce the tightly-coupled spatial database features of Odysseus/OpenGIS. By taking advantage of tight-coupling, Odysseus/OpenGIS provides excellent performance in processing spatial queries as well as flexible concurrency control and recovery on spatial data. We show the performance through extensive experiments. Finally, we present sample applications of a geographical information system (GIS) implemented using Odysseus/OpenGIS.

Metadata-enhanced visual interfaces to digital libraries

Information visualization offers a variety of ways in which digital library collections can be represented on the interface and shown to the user. Metadata, a key component of solid digital libraries, has been utilized to enhance visual user interfaces to digital libraries. This paper reports on a study conducted to investigate and analyze a specific category of digital library visual interface that supports information seeking, exploration and retrieval based on metadata representations, namely metadata-enhanced visual interfaces. This study examined 21 metadata-enhanced digital library visual interfaces from the following perspectives: (a) information access and retrieval features; (b) metadata elements; (c) visualization techniques and metaphors. The results demonstrated that the combined use of visualization techniques and metaphors is becoming increasingly prevalent as a design strategy to support users' information exploration. The results also suggest that visual interfaces enhanced with metadata are becoming more widespread to provide a richer representation of digital collections.

Information retrieval features in Indian digital libraries: a critical appraisal

PurposeThe purpose of this paper is to assess the present situation in the development of indigenous digital libraries in India. The study aims to evaluate different retrieval features of Indian digital libraries, especially those provide access to multilingual and multimedia documents.Design/methodology/approachFor this study, eight digital libraries originated in India, which are available in 24×7 timeframe and accessible worldwide through the internet, have been identified and selected. The retrieval features and other information on these digital libraries have been collected mostly from their respective websites and from other published literature, annual reports, etc.FindingsThe paper finds that Indian digital libraries provide universal access to informative and culturally rich digital content related to South Asian region. The digitisation efforts in Indian institutions make these digital libraries operational.Research limitations/implicationsThe institutional repositories and open access archives of Indian institutions that give access to the current research literature are not included in this study, because their retrieval features are mostly similar to each other as they use either DSpace or GNU EPrints open source software.Practical implicationsIndigenous digital libraries in India provide universal access to information and knowledge related to culture and heritage of people of South Asian counties. This study gives an insight on how diverse contents are integrated and disseminated to the end‐users in a user‐friendly manner. This study also indicates research initiatives on different aspects of multilingual contents in this region.Originality/valueNo such evaluation on Indian digital libraries has been done from the perspective of information retrieval features. This study will generate new knowledge in area of indigenous digital libraries in a developing country like India. This study is also an attempt to popularise Indian indigenous digital libraries among the world community.

A hybrid approach to fuzzy name search incorporating language-based and text-based principles

Name Search is an important search function in various types of information retrieval systems, such as online library catalogs and electronic yellow pages. It is also difficult, because of the high degree of fuzziness required in matching name variants. Previous approaches to name search systems use ad hoc combinations of search heuristics. This paper first discusses two approaches to name modeling - the natural language processing (NLP) and information retrieval (IR) models - and proposes a hybrid approach. The approach demonstrates a critical combination of complementary NLP and IR features that produces more effective fuzzy name matching. Two principles, position-as-attribute and position-transition-likelihood, are introduced as the principles for integrating the advantageous aspects of both approaches. They have been implemented in an NLP- and IR-hybrid model system called Friendly Name Search (FNS) for real world applications in multilingual directory searches on the Singapore Yellow pages website.

Elements of the information technology of cytological specimen analysis: Taxonomy and factor analysis

The automated software system “Black Square,” Version 1.2 is described. The system is intended for the automation of image processing, analysis, and recognition. It is an open system for generating new knowledge: objects, algorithms of image processing, recognition procedures originally not intended for image processing, and methods for solving applied problems. The system combines the features of information retrieval, reference, training, and computing systems.

Development of the open system for the automation of scientific research in image understanding Black Square, Version 1.2

The automated software system Black Square, Version 1.2 is described. The system is intended for the automation of image processing, analysis, and recognition. It is an open system for generating new knowledge: objects, algorithms of image processing, recognition procedures originally not intended for image processing, and methods for solving applied problems. The system combines the features of information retrieval, reference, training, and computing systems.