Discoveries of Contextualized Research Areas for Scientific Community

Abstract: In scientific community searching the relevant paper is a time critical task and often leads to misdirection. This is mainly due to the unstructured or semi-structured information indexed by search engines. However, with the evolution of social web (Web 2.0), the social community is being engaged in a system where they provide useful annotations (termed as tags) to the electronic resources. The current research makes use of social web to discover the most relevant papers from the Web. In our previous work, we compared author's keywords in a publication with the tags provided by the social community for serendipitous discoveries of related concepts and papers. However, in the current work, we focused to find a hierarchy of concepts related to focused publication. This hierarchy is discovered from Wordnet ontology, producing the convergent and divergent concepts for the author's keywords in a publication. These extended sets of keywords are matched with tags in social Web to find related concepts. In this way, the system is able to not only find the concepts in social Web that are directly related to the topics of the paper, instead, the system also discover the divergent and convergent concepts related to the topic of the paper. In this way, the users will be able to explore a topic hierarchy related to the paper's topics. The user can follow a concept to see the associated resources that have been annotated by the social community. Introduction Since the advent of the Word Wide Web, we see the information flux has been extending at the phenomenal speed. The huge amount of information at the WWW is being indexed by the search engines like Google, Yahoo etc The focus of current research is in utilizing rich metadata available in terms of tags in social bookmarking Aust. J. Basic & Appl. Sci., 5(6): 1641-1647, 2011 1642 systems for serendipitous discoveries of related research areas and papers in scientific domain. For example when a user is reading a particular research paper, the user is interested to find related papers and emerging fields in the context of the current paper. This is applicable to different types of users such as a user who wants to get new research directions for the focused area, a user who wants to see the community trends for the focused research area, and a user who is interested to discover most relevant recent papers in the focused domain. In our previous work, we have recommended newly evolving concepts, trends, and papers for the topic of the paper being read by the user (Afzal, 2010) using CiteULike tagging system. For the discovery of relevant and evolving concepts from CiteULIke, we utilized tags and compared them (directly and partially) with the keywords of the focused paper. The discovery was very useful, however, it lacks in recommending the hierarchy of the topics, for example converged and diverged concepts (topics) for the focused topics were not recommended. For example if an author' keyword is "Wiki", the system was able to find the evolving concepts from CiteULike such as "semantic wikis", "wikification", "Wikipedia" etc. However, the system was unable to find that the concept wiki is converging from the concepts (such as blogs, knowledge management, Wikipedia etc), and diverging to concepts (such as social networking sites, collaborative content etc). To give a broader picture of the related and emerging research areas for the focused research area, we have proposed a framework to utilize the tagging information to enhance the semantic context of a paper by extending the keyword dataset of research publication using the senses of WordNet ontology (Christiane Fellbaum, 1998). The convergent and divergent concepts for a concept defined in a paper (in terms of a keyword) are retrieved from Wordnet. Subsequently, the extended set of concept is mapped on the tags available in tagging application. The framework focus on indentifying all the convergent and divergent concepts of vocabulary for the keywords data set of the papers. The extended dataset of keywords have further been matched directly and partially with CiteULike tags for serendipitous discoveries of related concepts. The prototype has been implemented for an online journal such as Journal of Universal Computer Science. All of the journal's papers have been acquired along with the associated keywords. The keyword dataset has been extended using Wordnet ontology to find convergent and divergent concepts, subsequently; the extended set of keywords is searched within CiteULike dataset. The discovered concepts are pushed to users's local context. For example a user is reading a research paper in journal's environment; the user is able to see how the research topics of the focused paper are evolving and what are other related concepts from where the research topics of the focused paper have evolved. This broad picture helps users to set their directions. Related Work: This section provides a brief literature review about the related areas of the research. The paper focus on utilizing social bookmarking systems, enhance the system discoveries using Wordnet, embed the developed system in "Links into the Future" system. The following text explains the above mentioned areas briefly. Social Bookmarking: As the social bookmarking services have build up a parallel and influencing social based community with huge amount of data extending at phenomenal speed. There are some popular social bookmarking applications running across the web. CiteUlike is one of the bookmarking application which offers the services of sharing and discovering the information regarding the academics and research papers. Bibsonomy, another socialbookmarking application which offers its users in sharing bookmarks and publication references. Similarly, Delicious allows its users to bookmark/tag the web resources , its storage,sharing and viewing the bookmarks of other people. The collaborative tags have been used in proposing the recommendations for the scientific papers Similarly, the importance of tagging based systems have been realized in the knowledge discovery domain, where discovering the relevant information for contents stored at digital journal using the other alternative sources of social bookmarking data repositories. The potentials of Tags have also been exploited as a secondary measure for the knowledge diffusion, The results showed that there exist positive correlation between tags and citations and tags have potential to be used as one of the factor for the knowledge diffusion. Aust. J. Basic & Appl. Sci., 5(6): 1641-1647, 2011 1643 To prove the experiment, three datasets from the popular social bookmarking applications (i.e CitUlike, Bipsonomy, Delicious) were used. The above findings in the academic domain provide an opportunity to exploit and identify the potential advantages of the socially bookmarked data for extending the research scholar's context when he is going to read the scientific manuscript. As keyword tag is an important element in the taxonomy of a research paper and helps the reader in broadening his dimensions about the domain of the paper. The purpose of this research work is to broaden the user's context using the collaborative tagging. Wordnet: Wordnet is a semantically rich database which comprises of language semantics like noun,adjective,verb,adverb etc (Christiane Fellbaum, 1998). It include the hypernym and hyponym relations which allow in associating the concepts semantically. The synset (Hypernyms and hyponyms) offered by the WordNet has been used in enriching the bookmark collections, correlation among tags on the basis of semantics, semantics relatedness of searching query terms, etc In the proposed methodology is to extend the research publication's keyword dataset using the semantic based WordNet ontology (especially with the help of semantic relations offered by Hypernym and hyponym). This is done through adding the two data sets of concept vocabulary, which will be populated on the basis of keywords dataset of the research publication. One dataset is representing the 'Convergent to' means generalized concept list and will be built on the basis of Hypernyms for the paper's keyword using Word Net. Similarly the other table is recognized as 'Divergent from' means specialized concept list and be generated on the basis of Hyponyms of WordNet. Proposed Architecture: The proposed architecture of broadening the overall user's context on the basis of semantically extending the paper's keyword dataset is shown in Content Discovery Component: This component is responsible of selecting all the papers from the digital library. In this case we have selected only JUCS dataset. These papers are in the PDF format. So first these papers are converted into the text stream and then sent to the keyword selection module

Which Hashtag to use? Building a Hashtag recommender system and understanding the textual features surrounding Hashtags

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Reverse k-Ranks Queries on Large Graphs

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