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Journal Cover   Journal of Information Science
  [SJR: 1.008]   [H-I: 40]   [764 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  [814 journals]
  • Polarity classification for Spanish tweets using the COST corpus
    • Authors: Martinez-Camara, E; Martin-Valdivia, M. T, Urena-Lopez, L. A, Mitkov, R.
      Pages: 263 - 272
      Abstract: It was not until 2010 when businesses, politicians and people in general began to realize the potential of Twitter in Spain. This fact has awoken research interest in the extraction of knowledge from Twitter. This paper aims to fill the gap of the lack of resources for Twitter sentiment analysis in Spanish by performing a study of different features and machine learning algorithms for classifying the polarity of Twitter posts. The result is a new corpus of Spanish tweets called COST, and we have carried out a wide-ranging experiment in which different machine learning algorithms have been used. Furthermore, we have tested the influence of using different weighting schemes for unigrams, the influence of eliminating stop-words and the application of a stemmer process.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551514566564
      Issue No: Vol. 41, No. 3 (2015)
  • An enriched social behavioural information diffusion model in social
    • Authors: Mozafari, N; Hamzeh, A.
      Pages: 273 - 283
      Abstract: Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur over the edges of social networks. Many prior works have carried out empirical studies and diffusion models to understand how information propagates in online social networks; however they suffer from problems. In this paper, we propose an information diffusion model inspired by information propagation among people. Our proposed Social Behavioural Information Diffusion Model, abbreviated as SBIDM, considers the effect of mainstream media like TV and radio, as well as interaction with the neighbours. The advantages of our approach are four-fold. First, it models information diffusion in social networks inspired by social life, which considers the effect of aggregate social behaviour to diffuse information; second, it allows partial knowledge to be held in each individual; third, it considers the effects of social media in propagating information as well as the effects of interacting with neighbours; and last but not least, it is applicable to different types of data including synthetic and well-known real social networks like Facebook, Amazon, Epinions and DBLP. To explore the advantages of our approach, many experiments with different settings and specifications were conducted. The obtained results are very promising.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551514565318
      Issue No: Vol. 41, No. 3 (2015)
  • A user-profile-based friendship recommendation solution in social networks
    • Authors: Mazhari, S; Fakhrahmad, S. M, Sadeghbeygi, H.
      Pages: 284 - 295
      Abstract: Today, OSNs (online social networks) have become a popular communication platform, and a wide range of applications are considered for them. One fundamental relationship among members of a social network is friendship. Friend matching is a reasonable way to suggest people to each other. In this paper, we propose a model for measuring the similarities of two members based on information contained in their profiles. Unlike other profile-matching models, we do not assign equal weights to different items in users’ profiles to measure their similarity. In the first phase of the proposed scheme, we develop a mining model to discover the actual degree of influence of different factors that affect the formation of friendships. We extract this information from a real online social network. Then, based on the analysis results of this large dataset, matching and recommender systems are designed. The experimental results are encouraging and show that the proposed model significantly outperforms its counterparts.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551515569651
      Issue No: Vol. 41, No. 3 (2015)
  • Object-oriented and ontology-alignment patterns-based expressive Mediation
           Bridge Ontology (MBO)
    • Authors: Khan, W. A; Amin, M. B, Khattak, A. M, Hussain, M, Afzal, M, Lee, S, Kim, E. S.
      Pages: 296 - 314
      Abstract: The Semantic Web is dependent on extensive knowledge management by interlinking resources on the web using matching techniques. This role is played by the progressing domain of ontology matching, by introducing ontology-matching tools. The focus of these matching tools is limited to matching techniques and automation, rather than expressive formal representation of alignments. We propose Mediation Bridge Ontology (MBO), an expressive alignment representation ontology used to store correspondences between matching ontologies matched by our ontology-matching tool, System for Parallel Heterogeneity Resolution (SPHeRe). The MBO utilizes object-oriented design patterns and the proposed ontology-alignment design patterns to provide extendibility and reusability factors to SPHeRe system. We compared our proposed system with existing systems using Coupling Factor, Number of Polymorphic methods and Rate of Change metrics to support extendibility and reusability. These factors contribute to the overall objective of interoperability for knowledge management in the Semantic Web.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551514560952
      Issue No: Vol. 41, No. 3 (2015)
  • Supervised learning for building stemmers
    • Authors: Karanikolas; N. N.
      Pages: 315 - 328
      Abstract: This work is part of a project aiming to define a methodology for building simple but robust stemmers, having primitive knowledge of the stemmer’s target language. The methodology starts with a very simple primary stemmer that simply removes the longest suffix (using the primitive knowledge – the list of available suffixes) that matches the ending of the examined word. Information retrieval (IR) experts express their arguments against the results of the primary stemmer. These (the experts’ arguments) are valuable knowledge that offer us the ability to apply supervised learning in order to automatically produce better stemmers (that conform to the arguments expressed by the IR experts). We also conduct an evaluation of our supervised learning-based methodology that builds stemmers for languages that the experts do not have knowledge on.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551515572528
      Issue No: Vol. 41, No. 3 (2015)
  • Discovering duplicate and related resources using an interlinking
           approach: The case of educational datasets
    • Authors: Rajabi, E; Sicilia, M.-A, Sanchez-Alonso, S.
      Pages: 329 - 341
      Abstract: Linking a learning dataset to useful information on the Web of Data enriches its learning resources, as it enhances learners’ knowledge. This enrichment is usually achieved by creating links between datasets using the interlinking tools, which facilitate connecting any kind of data in a semi-automatic manner. This paper evaluates the interlinking results between an e-learning repository and several educational datasets on the Web of Data, which leads to enrichment of the contents. Many related resources were discovered during this experimentation already matched to the GLOBE learning objects. Furthermore, this research presents a data model to find similarity between two datasets and a workflow to identify the duplicate resources by performing a semi-automatic evaluation process. A case study was also assessed by human experts.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551515575922
      Issue No: Vol. 41, No. 3 (2015)
  • Lexicon-based context-sensitive reference comments crawler
    • Authors: Jeon; H.
      Pages: 342 - 353
      Abstract: This paper proposes a novel system that aids in the writing of research papers by gathering and analysing other researchers’ comments for a given reference paper to provide some features, advantages or disadvantages of the referenced research. A lexicon-based reference comments crawler (LRCC) classifies the comments about a reference paper and the surrounding sentences using part-of-speech lexicons and a dynamic text window into four categories (normal, advantage, disadvantage and complex). The extraction of comments and surrounding sentences from research papers is effectively and efficiently carried out using the reference identifier and some simple extraction rules. In this paper, we considered the various types of reference identifiers, because a reference identifier is a key solution for the sentence extraction in the LRCC system. Several experiments were performed using published research papers to evaluate the LRCC’s precision and recall. The results showed that the LRCC can extract and classify comments with a high degree of precision and recall, as well as present them to the user in an effective and efficient manner.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551515575921
      Issue No: Vol. 41, No. 3 (2015)
  • The structural effects of sharing function on Twitter networks: Focusing
           on the retweet function
    • Authors: Ahn, H; Park, J.-H.
      Pages: 354 - 365
      Abstract: Social network services (SNSs) provide the functions for both the management of social networks and information diffusion. We considered SNS networks as the networks that embrace both the strong links formed through the making-relationship function and the weak links formed through the sharing function. For identifying the structural role of each link, we constructed the SNS networks using Twitter data and analysed them focusing not on nodes but on links of the networks at a component level and at an ego-network level. More than 200,000 tweets were randomly sampled through the streaming API of Twitter. As a result, we found that weak links formed through the sharing function play a more important role in maintaining the range of information diffusion and provide more structural advantage in acquiring and controlling information than the strong links formed through the making-relationship function.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551515574974
      Issue No: Vol. 41, No. 3 (2015)
  • The potential relationship discovery model based on result fusion for
           biomedical medicine research
    • Authors: She, Y; Zhang, X, Wang, Q, Wu, Q.
      Pages: 366 - 382
      Abstract: With the amount of biomedical data growing explosively, medical scientists use many datasets in research for new medicine. However, the amount of biomedical data is growing too fast to abstract hidden information. At the same time, with the development of data storage diversification, scientists prefer to have data fusion based on heterogeneous data sources as opposed to a single data source, and ultimately to achieve knowledge and discovery across heterogeneous databases. Our study focuses on extending the application of latent semantic analysis methodologies into the area of biomedical research. Our purpose is to develop a model for discovering potential relationships between medicines and diseases based on biomedical latent semantic analysis. This model could be used in constructing link maps for biomedical entities, and provide a theoretical basis and practical support for biomedical scientists in their study of the disease–medicine relationship. In detail, we discuss the study of the integration of the latent semantic analysis model and data fusion methodologies. Our result fusion solution combines scientific literature repositories and a biomedical database based on context and the ABC model, and is supervised by a semi-supervised learning algorithm and data fusion algorithms. The expectation is that fused data could represent multilevel potential relationships between biological entities and related emotional relationship expression. The model is validated by experience and proven to be feasible and effective.
      PubDate: 2015-05-11T02:45:09-07:00
      DOI: 10.1177/0165551515578395
      Issue No: Vol. 41, No. 3 (2015)
  • A data-driven dynamic ontology
    • Authors: Fudholi, D. H; Rahayu, W, Pardede, E.
      Pages: 383 - 398
      Abstract: Valuable knowledge in every community is changed frequently. It often remains closely inside a community, even though it has huge potential to promote problem-solving in the wider community. Our research aims to increase the capability of communities in capturing, sharing and maintaining knowledge from any domain. We utilize an ontology, a shareable form, to collect, consolidate and find commonality inside knowledge. Most ontologies available these days were created by domain experts to fulfill certain domain requirements. However, in cases when domain experts are not obtainable or standard agreement within the domain is not available, such as in natural or herbal therapy domain, we propose that an ontology can also be extracted from existing knowledge-bases residing within the community. In order to achieve our aim, we design a data-driven dynamic ontology model. Our model consists of base knowledge creation and knowledge propagation phases. In the base knowledge creation phase, we define a general concept of capturing community knowledge from data into an ontology representation, rather than just transforming a specific data format into an ontology as found in existing studies. In our knowledge propagation phase, the dynamic community knowledge sources become the trigger of propagation. This is different from some approaches in existing studies, where the triggering event is an individual change inside the ontology and external data may not be the base source of the knowledge in the evolving ontology. We define the propagation feature with a novel delta script. The script is minimum yet complete to simplify and save knowledge sharing transportation resources. The evaluation result shows that the data-driven dynamic ontology with its propagation method not only delivers complete and correct semantics but also shows good performance in terms of operation cost and processing time.
      PubDate: 2015-05-11T02:45:10-07:00
      DOI: 10.1177/0165551515576478
      Issue No: Vol. 41, No. 3 (2015)
  • Enriching information science research through chronic disposition and
           situational priming: A short note for future research
    • Authors: Lim; W. M.
      Pages: 399 - 402
      Abstract: This article aims to encourage information specialists to consider chronic disposition and situational priming as avenues to enrich information science research. The author makes a case for conducting such investigations by articulating the potential contributions of advancing theoretical and practical understanding of information science in both physical and technological environments using these approaches. The author also offers a theoretical toolbox of conceptual underpinnings relevant to chronic disposition and situational priming to provide a general overview on how such investigations can be performed. It is hoped that the arguments and theoretical lenses put forth will inspire further research in the area.
      PubDate: 2015-05-11T02:45:10-07:00
      DOI: 10.1177/0165551515577913
      Issue No: Vol. 41, No. 3 (2015)
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