Webbing semantified scholarly communication datasets for improved resource discovery - Latif, A.; Tochtermann, K. (Journal of Digital Information Management, 2012)
The success of Linked Data project has played a vital role in the realization of the Semantic Web on a global stage. It has motivated people to publish datasets which are important for information linking, resource discovery and can further make contributions in shaping 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 and further on presentation of post-interlinking data are still researchable challenges. The search and identification of relevant datasets as well as devising a strategy for linking to these datasets with post-organization of interlinked data for human perceivable presentation is still a difficult task. In this paper, a novel approach is presented which 1) highlights and implements the steps involved in the interlinking process and 2) proposed a proof of concept application for automatic generation of an organized profile for post interlinked data presentation. These approaches are applied and presented as a case study focusing on interlinking scholarly communication datasets and highlighting the potential benefits of Linked Data. This study has interlinked two semantified scholarly datasets and was successfully able to discover new resources which were further organized into profile for showcasing potentials of Linked Data.
Keywords: Case study; Interlinking scholarly datasets; Linked Open Data (LOD); Semantic web

Harvesting pertinent resources from Linked Open Data - Latif, A.; Afzal, M.T.; Saeed, A.U.; Hoefler, P.; Tochtermann, K. (Journal of Digital Information Management, 2010)
Linked Open Data (LOD) is becoming an essential part of the Semantic Web. Although LOD has amassed large quantities of structured data from diverse, openly available data sources, there is still a lack of user-friendly interfaces and mechanisms for exploring this huge resource. In this paper, we describe a methodology for harvesting relevant information from the gigantic LOD cloud. The methodology is based on combination of information: identification, extraction, integration and presentation. Relevant information is identified by using a set of heuristics. The identified information resource is extracted by employing an intelligent URI discovery technique. The extracted information is further integrated with the help of a Concept Aggregation Framework. Then the information is presented to end users in logical informational aspects. Thereby, the proposed system is capable of hiding complex underlying semantic mechanics from end users and reducing the users' cognitive load in locating relevant information. In this paper, we describe the methodology and its implementation in the CAF-SIAL system, and compare it with the state of the art.
Keywords: CAF-SIAL; Information presentation; Information retrieval; Linked Open Data (LOD); Semantic web; URI; User interfaces