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Journal Cover Journal of Information Science
  [SJR: 1.008]   [H-I: 40]   [780 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0165-5515 - ISSN (Online) 1741-6485
   Published by Sage Publications Homepage  [842 journals]
  • Generating query suggestions by exploiting latent semantics in query logs
    • Authors: Momtazi, S; Lindenberg, F.
      Pages: 437 - 448
      Abstract: Search engines assist users in expressing their information needs more accurately by reformulating the issued queries automatically and suggesting the generated formulations to the users. Many approaches to query suggestion draw on the information stored in query logs, recommending recorded queries that are textually similar to the current user’s query or that frequently co-occurred with it in the past. In this paper, we propose an approach that concentrates on deducing the actual information need from the user’s query. The challenge therein lies not only in processing keyword queries, which are often short and possibly ambiguous, but especially in handling the complexity of natural language that allows users to express the same or similar information needs in various differing ways. We expect a higher-level semantic representation of a user’s query to more accurately reflect the information need than the explicit query terms alone can. To this aim, we employ latent Dirichlet allocation as a probabilistic topic model to reveal latent semantics in the query log. Our evaluations show that, whereas purely topic-based query suggestion performs the worst, the interpolation of our proposed topic-based model with the baseline word-based model that generates suggestions based on matching query terms achieves significant improvements in suggestion quality over the already well performing purely word-based approach.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515594723
      Issue No: Vol. 42, No. 4 (2016)
  • Incorporating social information to perform diverse replier recommendation
           in question and answer communities
    • Authors: Liu, Y; Lin, Z, Zheng, X, Chen, D.
      Pages: 449 - 464
      Abstract: Social information is contextual information that has made significant contributions to intelligent information systems. However, social information has not been fully used, especially in question and answer (Q&A) systems. This study describes a contextual recommendation method in which diverse repliers are recommended for new questions using incorporated social information in Q&A communities. We have mined multiple kinds of social information by analysing social behaviours and relations found in a Q&A community and proposed an algorithm to incorporate different social information in various social contexts to perform diverse repliers’ recommendations. Recommendation diversity and social contexts have been considered and the properly used social information has been emphasized in this study. We conducted experiments using a dataset collected from the Stack Overflow website. The results demonstrate that different social information makes different contributions in promoting question answering, and incorporating social information properly could improve recommendation diversity and performance, which would then result in the promotion of satisfactory question solving.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515592093
      Issue No: Vol. 42, No. 4 (2016)
  • Summary generation approaches based on semantic analysis for news
    • Authors: Kogilavani, S. V; Kanimozhiselvi, C. S, Malliga, S.
      Pages: 465 - 476
      Abstract: With the exponential growth of the internet, a lot of online news reports are produced on the web every day. The news stream flows so rapidly that no one has the time to look at each and every item of information. In this situation, a person would naturally prefer to read updated information at certain time intervals. Document updating technique is very helpful for individuals to acquire new information or knowledge by eliminating out-of-date or redundant information. Existing summarization systems involve identifying the most relevant sentences from the text and putting them together to create a concise initial summary. In the process of identifying the important sentences, features influencing the relevance of sentences are determined. Based on these features the salience of the sentence is calculated and an initial summary is generated from highly important sentences at different compression rates. These types of initial summaries work on a batch of documents and do not consider the documents that may arrive at later time, so that corresponding summaries need to get updated. The update summarization system addresses this issue by taking into account the documents read by the user in the past and seeks to present only fresh or different information. The first step is to create an initial summary based on basic and additional features. The next step is to create an update summary based on the basic, additional and update features. In this paper, two approaches are proposed for generating initial and update summary from multiple documents about given news. The first approach performs semantic analysis by modifying the vector space model with dependency parse relations and applying latent semantic analysis on it to create a summary. The second approach applies sentence annotation based on aspects, prepositions and named entities to generate summary. Experimental results show that the proposed approaches generate better initial and update summaries compared with the existing systems.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515594726
      Issue No: Vol. 42, No. 4 (2016)
  • An exploration of search session patterns in an image-based digital
    • Authors: Han, H; Wolfram, D.
      Pages: 477 - 491
      Abstract: Three months of server transaction logs containing complete clickstream data for an image collection digital library were analysed for usage patterns to better understand user searching and browsing behaviour in this environment. Eleven types of user actions were identified from the log content. The study is novel in its combined analytical techniques and use of clickstream data from an image-based digital library. Three analytical techniques were used to analyse the data: (a) network analysis to better understand the relationship between sequential actions; (b) sequential pattern mining to identify frequent action sequences; and (c) k-means cluster analysis to identify groups of session patterns. The analysis revealed strong ties between several pairs of actions, relatively short pattern sequences that frequently duplicate previous actions and largely uniform session behaviour with little individual item browsing within sessions, indicating users are primarily engaged in purposeful and directed searching. Developers of image-based digital libraries should consider design features that support rapid browsing.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515598952
      Issue No: Vol. 42, No. 4 (2016)
  • Discovering aspects of online consumer reviews
    • Authors: Suleman, K; Vechtomova, O.
      Pages: 492 - 506
      Abstract: In this paper we propose a fully unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a two-step hierarchical clustering process in which we first cluster words representing aspects based on the semantic similarity of their contexts and then on the similarity of the hypernyms of the cluster members. Our approach also includes a method for assigning class labels to each of the clusters. We evaluated our methods on large datasets of restaurant and camera reviews and found that the two-step clustering process performed better than a single-step clustering process at identifying aspects and words refering to aspects. Finally, we compare our method to a state-of-the-art topic modelling approach by Titov and McDonald, and demonstrate better results on both datasets.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515595742
      Issue No: Vol. 42, No. 4 (2016)
  • Improving the geospatial consistency of digital libraries metadata
    • Authors: Renteria-Agualimpia, W; Lopez-Pellicer, F. J, Lacasta, J, Zarazaga-Soria, F. J, Muro-Medrano, P. R.
      Pages: 507 - 523
      Abstract: Consistency is an essential aspect of the quality of metadata. Inconsistent metadata records are harmful: given a themed query, the set of retrieved metadata records would contain descriptions of unrelated or irrelevant resources, and may even not contain some resources considered obvious. This is even worse when the description of the location is inconsistent. Inconsistent spatial descriptions may yield invisible or hidden geographical resources that cannot be retrieved by means of spatially themed queries. Therefore, ensuring spatial consistency should be a primary goal when reusing, sharing and developing georeferenced digital collections. We present a methodology able to detect geospatial inconsistencies in metadata collections based on the combination of spatial ranking, reverse geocoding, geographic knowledge organization systems and information-retrieval techniques. This methodology has been applied to a collection of metadata records describing maps and atlases belonging to the Library of Congress. The proposed approach was able to automatically identify inconsistent metadata records (870 out of 10,575) and propose fixes to most of them (91.5%) These results support the ability of the proposed methodology to assess the impact of spatial inconsistency in the retrievability and visibility of metadata records and improve their spatial consistency.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515597364
      Issue No: Vol. 42, No. 4 (2016)
  • Incorporating social media comments in affective video retrieval
    • Authors: Nemati, S; Naghsh-Nilchi, A. R.
      Pages: 524 - 538
      Abstract: Affective video retrieval systems aim at finding video contents matching the desires and needs of users. Existing systems typically use the information contained in the video itself to specify its affect category. These systems either extract low-level features or build up higher-level attributes to train classification algorithms. However, using low-level features ignores global relations in data and constructing high-level features is time consuming and problem dependent. To overcome these drawbacks, an external source of information may be helpful. With the explosive growth and availability of social media, users’ comments could be such a valuable source of information. In this study, a new method for incorporating social media comments with the audio-visual contents of videos is proposed. Furthermore, for the combination stage a decision-level fusion method based on the Dempster–Shafer theory of evidence is presented. Experiments are carried out on the video clips of the DEAP (Database for Emotion Analysis using Physiological signals) dataset and their associated users’ comments on YouTube. Results show that the proposed system significantly outperforms the baseline method of using only the audio-visual contents for affective video retrieval.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515593689
      Issue No: Vol. 42, No. 4 (2016)
  • Information encountering on social media and tacit knowledge sharing
    • Authors: Panahi, S; Watson, J, Partridge, H.
      Pages: 539 - 550
      Abstract: The purpose of this paper is to investigate how social media may support information encountering (i.e. where individuals encounter useful and interesting information while seeking or browsing for some other information) and how this may lead to the facilitation of tacit knowledge creation and sharing. The study employed a qualitative survey design that interviewed 24 physicians who were active users of social media to better understand the phenomenon of information encountering on social media. The data was analysed using the thematic analysis approach. The study found six main ways through which social media supports information encountering. Furthermore, drawing upon knowledge creation theories, the study concluded that information encountering on social media facilitates tacit knowledge creation and sharing among individuals. The study provides new directions for further empirical investigations to examine whether information encountering on social media actually leads to tacit knowledge creation and sharing. The findings of the study may also provide opportunities for users to adopt social media effectively or gain greater value from social media use.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515598883
      Issue No: Vol. 42, No. 4 (2016)
  • The information environment and information behaviour of the Offshore
           Installation Manager (OIM) in the context of safety and emergency
           response: An exploratory study
    • Authors: Marcella, R; Lockerbie, H.
      Pages: 551 - 567
      Abstract: The offshore installation manager (OIM) is a unique role in the oil and gas industry with the legal responsibility for the health and safety of individuals on an offshore installation, as well as holding commercial responsibilities. Using exploratory, qualitative data based on 10 interviews conducted with OIMs, the information environment and behaviour of the OIM are described and areas for further research are explored. The OIM’s information environment is one that is complex and relies heavily on both formal and informal sources of information. Two modes of OIM information behaviour are identified: everyday information need, in which the OIM seeks, uses and shares information to maintain safe operations; and emergency information need, in which there is both reliance on information that must be known in order to react to an emergency situation and a need for accessible information about the status of a rapidly changing environment. The OIM is both the user of information and a source of information for others and as such must be trusted, reliable and automotive.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515600118
      Issue No: Vol. 42, No. 4 (2016)
  • A refined twig-join swift query algorithm for diversification issues of
    • Authors: Kung, Y.-W; Chang, H.-K, Lee, C.-N.
      Pages: 568 - 578
      Abstract: Compiling documents in extensible markup language (XML) plays an important role in accessing data services. An efficient query service should be based on a skillful representation that can support query diversification and solve ambiguity in order to improve high-precision search capabilities. However, to the best of our knowledge, research on query diversification, target hierarchical level and the problem of ambiguity is insufficient. In this study we aimed to solve these problems so that the results are able not only to satisfy query diversification, but also to offer better precision compared with the existing twig join algorithms. An extended twig join Swift (TJSwift) associated with adjacent linked lists for the provision of efficient XML query services is also proposed, whereby queries can be versatile in terms of predicates. It can completely preserve hierarchical information; in addition, the new index generated from XML is used to save semantic information.
      PubDate: 2016-07-06T02:47:19-07:00
      DOI: 10.1177/0165551515601004
      Issue No: Vol. 42, No. 4 (2016)
  • Erratum
    • Pages: 579 - 579
      Abstract: ‘A language-model-based approach for subjectivity detection’, by Samaneh Karimi and Azadeh Shakery, published in Journal of Information Science, first published Online First on 26 April 2016 as
      DOI : 10.1177/0165551516641818. Owing to an error made by SAGE, the corresponding author’s affiliation is incorrect as an institution was missed. The author should appear as: Azadeh Shakery School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Iran SAGE would like to apologise to the authors for this error.
      PubDate: 2016-07-06T02:47:19-07:00
      Issue No: Vol. 42, No. 4 (2016)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
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