Abstract Textual eligibility criteria in clinical trial protocols contain important information

Abstract. Under the umbrella of the Semantic Web, Linking Open Data projects have made available a large number of seman-tically intra- and inter-connected links. As an example, in the biomedical domain, data about disorders, disease related genes and proteins, clinical trials, and drugs or interventions are accessible on the Linked Open Data cloud. In addition, domain ontologies have been used to annotate scientific data. For instance, publications in PubMed have been annotated using controlled vocabulary (CV) terms from ontologies such as the Medical Subject Header (MeSH) or the Unified Medical Language System (UMLS). These annotations have been successfully mined to discover associations between drugs and diseases using techniques that have been labeled as Literature-Based Discovery (LBD). Given the large scale of the linked datasets in the Linked Open Data cloud, there is a need to develop scalable techniques that can provide answers in close to real time, to explain a phenomena, to identify anomalies, or to explore a discovery. This paper describes an authority flow based ranking technique that is inspired by LBD methods. The ranking is tailored to a layered graph. The input terms are in the first layer and the ranking will efficiently identify and assign high scores to terms in a third (or subsequent) layer, corresponding to potential novel discoveries. The terms, links and scores are modeled as a Bayesian network. Two sampling techniques are proposed to only traverse the terms that may have high scores. The first technique implements a Direct Sampling reasoning algorithm to approximate the ranking scores of nodes in the Bayesian network; it visits only the nodes with the highest probability. The second technique samples paths in the Bayesian network with the highest conditional probability. An experimental study reveals that the proposed ranking techniques are able to

Structured Product Labels (SPLs) contain information about drugs that can be valuable to clinical and translational research, especially if it can be linked to other sources that provide data about drug targets, chemical properties, interactions, and biological pathways. Unfortunately, SPLs currently provide coarsely-structured drug information and lack the detailed annotation that is required to support computational use cases. To help address this issue we created LinkedSPLs, a Linked Data resource that extends the "web of drug identity " using information extracted from SPLs. In this paper we describe the mapping that LinkedSPLs provides between SPL active ingredients and DrugBank chemical entities. These mappings were created using three approaches: InChI chemical structure de-scriptors comparison, exact string matching based on the chemical name, and automatic (unsupervised) linkage identification. Comparison of the approaches found that, while these three approaches are complementary, the automatic approach performs well in terms of precision and recall.

The Semantic Web provides a framework for the integration of resources on the web, which facilitates information integration and interoperability. RDF is the main representation format for Linked Open Data (LOD). However, datasets are not always made available in RDF by their producers and the Semantic Web community has had to con-vert some of these datasets to RDF in order for these datasets to participate in the LOD cloud. As a result, the LOD cloud sometimes contains outdated, partial and even inaccurate RDF datasets. We review the LOD landscape for one of these resources, MeSH, and analyze the characteristics of six existing representations in order to identify de-sirable features for an authoritative version, for which we create a prototype. We illustrate the suitability of this pro-totype on three common use cases. NLM intends to release an authoritative representation of MeSH in RDF (beta version) in the Fall of 2014. 1