Atif Latif

Weaving Twitter stream into Linked Data a proof of concept framework

Twitter is one of the most popular and well known micro blogging platforms. Its usage in all walks of life as a short message service makes it a highly valuable and trendy asset of todays web. But the knowledge and content delivered by Twitter explicitly or implicitly as short messages remains mostly unstructured and hidden for machine usage. In this paper, we have addressed the aforementioned problems by using the Semantic Web and Linked Data technologies. We explore an integrated approach by building a proof of concept framework, which uses Semantic Web technologies to triplify and link the unstructured content of tweets with Linked Data clouds as structured data. We are of the view that this proof of concept framework will be helpful in investigation of case studies like opinion mining, trend analysis in various settings and more importantly will bring the Social Web closer to the Semantic Web. In future we will extend our proposed framework in the domain of terrorism informatics.

Discovery and visualization of expertise in a scientific community

In numerous contexts and environments, it is necessary to identify and assign (potential) experts to subject fields. In the context of an academic journal for computer science (J.UCS), papers and reviewers are classified using the ACM classification scheme. This paper describes a system to identify and present potential reviewers for each category from the entire body of papers authors. The topical classification hierarchy is visualized as a hyperbolic tree and currently assigned reviewers are listed for a selected node (computer science category). In addition, a spiral visualization is used to overlay a ranked list of further potential reviewers (high-profile authors) around the currently selected category. This new interface eases the task of journal editors in finding and assigning reviewers. The system is also useful for users who want to find research collaborators in specific research areas.

Turning keywords into URIs: simplified user interfaces for exploring linked data

The Semantic Web strives to add structure and meaning to the Web, thereby providing better results and easier interfaces for its users. One important foundation of the Semantic Web is Linked Data, the concept of interconnected data, describing resources by use of RDF and URIs. Linked Data (LOD) provides the opportunity to explore and combine datasets on a global scale -- something which has never been possible before. However, at its current stage, the Linked Data cloud yields little benefit for end users who know nothing of ontologies, triples and SPARQL. This paper presents an intelligent technique for locating desired URIs from the huge repository of Linked Data. Search keywords provided by users are utilized intelligently for locating the intended URI. The proposed technique has been applied in a simplified end user interface for LOD. The system evaluation shows that the proposed technique has reduced users cognitive load in finding relevant information.

Retrieving and ranking scientific publications from linked open data repositories

Content enrichment of publications stored in different cross domain Digital Libraries can facilitate the scholarly communication in big way. However, current DL still entails limitation of interoperability between cross domain repositories. This paper emphasizes on this limitation and proposes an innovative approach for finding and recommending scientific publications which are stored in disparate repositories. At first Linked Open Data is considered by exploring existing alignments between Econstor and other datasets within the current LOD cloud through the STW Thesaurus. Moreover, incorporation of other relevant metadata is proposed by implementing a data mining approach which improves the semantic relativeness of the publications from the recommended list.

Constructing experts profiles from Linked Open Data

Identification and assignment of (potential) experts to subject field is an important task in various settings and environments. In scientific domain, the identification of experts is normally based on number of factors like: number of publications, citation record, and experience etc. However, the discovered experts cannot be assigned reviewing duties immediately. One also need further information about expert like the country, university, service record, contributions, honors, and name of conferences/journals where the discovered expert is already serving as editor/reviewer. To some extent, this information can be found from search engines using heuristics, by applying Natural Language Processing, and Machine Learning techniques. However, the emergence of many semantically rich and structured datasets from Linked Open Data movement (LOD) can facilitate in more controlled search and fruitful results. This paper employs an automatic technique to find the required information about experts using LOD dataset. The expert profile is discovered, aggregated, clustered, structured, and visualized to the administration of peer-review system. The system has been implemented for an electronic journal such as Journal of Universal Computer Science (J.UCS). The proposed system facilitates J.UCS administration to find potential reviewers for scientific papers to assign reviewing duties and to call new editors for computer science topics.

Does Tagging Indicate Knowledge Diffusion? An Exploratory Case Study

The high potential of knowledge to create economies of scale attracted the interest towards understanding the dynamics of its diffusion. The new developments in the collaborative and participatory role of web emerges new knowledge structures driving the need towards Web based indicators for studying the diffusion of knowledge on Web. We present the results of an exploratory case-study investigating the tagging and citing practices for the WWW `06 conference papers. We observed two important patterns: (1) Papers which got heavily tagged in all three applications Delicious, Citeulike and Bibsonomy (average total tags > 6) generally achieved a high number of citations. We also observed that most of the tags were from 2006 and most of the citations were from 2007. This indicated that tags may hold the potential of foretelling the future volume of regenerative diffusion of research. (2) Terms appearing in tag-clouds of highly tagged papers in 2006 reappear frequently in the titles of respective citing papers in 2007. This shows that tag clouds may have the potential of forecasting the context of diffusion as well. It furthermore indicates that the users tag the papers with multiple tags according to their specific contexts of use as well as content of that knowledge artifact. Based on these patterns we propose that tags may be used as a supplementary indicator to model the diffusion of knowledge on Web.

LOD for Library Science: Benefits of Applying Linked Open Data in the Digital Library Setting

Linked Open Data (LOD) has gained widespread adoption by large industries as well as non-profit organizations and governmental organizations. One of the early adopters of LOD technologies are libraries. Since the “early years”, libraries have been key use case and innovation driver for LOD and significantly contributed to the adoption of semantic technologies. The first part of this paper presents selected success stories of current activities in the Linked Data Library community. In a nutshell, these studies include (1) a conceptualization of the Linked Data Value chain, (2) a case study for consumption of Linked Data in a digital journal environment, and (3) an approach to publish metadata on the Semantic Web from an Open Access repository. These stories reveal a strong relationship between LOD in libraries and research topics addressed in traditional fields of computer science such as artificial intelligence, databases, and knowledge discovery. Thus, in the second part of this paper we systematically review the relation of LOD in digital libraries from a computer science perspective. We discuss current LOD research topics such as data integration and schema integration, distributed data management, and others. These challenges have been discussed with computer scientists at a German national database meetup as well as with librarians from ZBW—Leibniz Information Center for Economics and at international librarians meetup.

Bringing Scientific Blogs to Digital Libraries.

Research publication via scientific blogging is gaining momentum, with an ever-increasing number of researchers accepting it as their main or complementary research dissemination channel. This development has prompted both scientific bloggers and Digital Libraries (DL) to explore the potential of streamlining these resources along DL collections for increased and complementary user selection. In this paper we explore a methodology for achieving the integration of DL and a blog post collections, together with some use case scenarios that demonstrate the values and capabilities of this integration.

Interlinking Scientific Authors with the LOD Cloud: A Case Study

Linked Data has played a vital role in the realization of the Semantic Web on a global level. It motivates people to publish datasets which can be important for information linking and discovery and can further make contributions in streamlining the Web as a single connected data space. This effort has successfully amassed a variety of Linked Data and has introduced many novel ways for the publishing of data. As a result, putting Linked Data online has become rather easy, but actually linking the data with already existing data in the cloud is still a challenge. The search and identification of relevant datasets as well as devising a strategy for linking to these datasets is still a difficult task. In this paper, a novel approach is presented which highlights and implements the steps involved in the interlinking process. This approach is further applied and presented as a case study focusing on interlinking scholarly communication datasets and highlighting the potential benefits.

Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papers

New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.