Plausible reasoning reflects the "plasticity" element of human reasoning, which, by leveraging the semantics of relevant concepts, allows dealing with incomplete data during decision making. We propose the SEmantics-based Data ANalytics (SeDan) framework that integrates plausible reasoning with expressive, fine-grained biomedical ontologies. Using this framework, an unresolvable query can be rewritten to explore the semantic knowledge graph and infer new knowledge. While the gained insights may be plausible, i.e., not supported by crisp deductive reasoning, they may still aid complex medical decision making by recommending plausible solutions. In this paper, we investigate the efficiency of SeDan in a real-world medical setting to pose intelligent medical queries from BioASQ challenges over the MEDLINE database. Experimental results show that SeDan can expand the query answering coverage by resolving up to 45% of initially unresolvable queries. The correctness of the inferred answers, as well as the underlying plausible reasoning processes, was verified by a domain expert.

Semantic Web technologies have shown to have great potential in many different domains, to facilitate knowledge representation, exchange and reasoning, in a formal and yet both human and machine understandable way. In particular, within the health domain, they enable knowledge integration and understanding by explicitly defining and linking concepts and relationships using ontologies to information within clinical knowledge bases. This additional metadata also allows for automated decision support and semantic based analytics to be implemented, that facilitate improved healthcare at a lower cost. Unfortunately many existing datasets in healthcare environments are still stored in relational databases, as opposed to using semantic technologies. Due to this, the link with explicit metadata is often lacking or non-existent. Furthermore, both the databases and the clinical terminologies can be considerably large, making the mapping and subsequent uses of the information a difficult process. In a full fledged decision support system the level and accuracy of the mapping can greatly influence the effectiveness of any subsequent analysis and decision support tasks. This is especially true in clinical scenarios, where very large and complex sets of terms need to be mapped to relational databases. In this paper we aim to provide a general approach for interlinking relational data with clinical ontology based metadata that allows for a fine grade evaluation, with respect to the mapping's impact on analytics. We evaluate our approach by mapping information from clinical terminologies, such as SNOMED CT, to a large laboratory dataset contained in a relational database, with the goal of creating a full fledged, semantically enabled, analytics and decision support system.

In a mobile setting, the user often browses the Web to consult information related to his current context and environment: e.g., reviews of nearby restaurants, or tourist information on visited monuments. On the other hand, the limitations of mobile devices (e.g., limited screen) and the peculiarities of mobile Web usage (e.g., walking around, driving a car) make it cumbersome to extensively browse a Web page for such useful information. In this paper, we present a client-side approach that aims to assist the mobile user in his browsing session, by correlating the Web page's content with the mobile user's context, and subsequently emphasizing and enriching relevant content with so-called context-sensitive cues. To achieve this, we utilize the SCOUT framework for mobile applications to model and access the user's context, and RDFa annotations present on existing Web pages to identify Web page elements suitable to enrich with context-sensitive cues. The cues themselves are injected using existing adaptation techniques, borrowed from the field of Adaptive Hypermedia.

Web-based systems can exploit Semantic Web-based approaches to link data and thus create applications that make the most out of the combination and integration of different sources and background knowledge. While a lot of attention is paid to the opportunities that this linking of data on the Web provides, the reality of implementing such solutions with currently available semantic technologies createsa serious engineering challenge. In developing such applications in a commercial setting, we have been confronted with requirements and conditions that show the limitations of current technologies for this type of Web applications. Using our experience from iFanzy, we illustrate in this paper the issues and steps in turning the concept of access to semantically integrated content into solutions that use available technology.