Semantic Web: – Interoperability,Usability,Applicability

The sharing of product and process information plays a central role in coordinating supply chains operations and is a key driver for their success. “Linked pedigrees” - linked datasets, that encapsulate event based traceability information of artifacts as they move along the supply chain, provide a scalable mechanism to record and facilitate the sharing of track and trace knowledge among supply chain partners. In this paper we present “OntoPedigree” a content ontology design pattern for the representation of linked pedigrees, that can be specialised and extended to define domain specific traceability ontologies. Events captured within the pedigrees are specified using EPCIS - a GS1 standard for the specification of traceability information within and across enterprises, while certification information is described using PROV - a vocabulary for modelling provenance of resources. We exemplify the utility of OntoPedigree in linked pedigrees generated for supply chains within the perishable goods and pharmaceuticals sectors.

The Web of Data has grown enormously over the last years. Currently, it comprises a large compendium of interlinked and distributed datasets from multiple domains. Running complex queries on this compendium often requires accessing data from different endpoints within one query. The abundance of datasets and the need for running complex query has thus motivated a considerable body of work on SPARQL query federation systems, the dedicated means to access data distributed over the Web of Data. However, the granularity of previous evaluations of such systems has not allowed deriving of insights concerning their behavior in different steps involved during federated query processing. In this work, we perform extensive experiments to compare state-of-the-art SPARQL endpoint federation systems using the comprehensive performance evaluation framework FedBench. In addition to considering the tradition query runtime as an evaluation criterion, we extend the scope of our performance evaluation by considering criteria, which have not been paid much attention to in previous studies. In particular, we consider the number of sources selected, the total number of SPARQL ASK requests used, the completeness of answers as well as the source selection time. Yet, we show that they have a significant impact on the overall query runtime of existing systems. Moreover, we extend FedBench to mirror a highly distributed data environment and assess the behavior of existing systems by using the same performance criteria. As the result we provide a detailed analysis of the experimental outcomes that reveal novel insights for improving current and future SPARQL federation systems.

The increasing and unprecedented publication rate in the biomedical field is a major bottleneck for knowledge discovery in the Life Sciences. The manual curation of facts from published scientific papers is slow and inefficient, and therefore new approaches are needed that can enable the automatic, scalable and reliable extraction of assertions. While the publication of scientific assertions and datasets on the Semantic Web is gaining traction, it also creates new challenges such as the proper representation of provenance and versioning. Here, we address these issues and describe our efforts to represent the DisGeNET database of human gene-disease associations as permanent, immutable, and provenance rich digital objects called nanopublications. Our nanopublications are the first instance of a Linked Data model that ensures stable interlinking of the assertion and its metadata by Trusty URIs. As DisGeNET integrates manually curated as well as text-mined data of different origins, the semantic description of the evidence for each assertion is important to provide trust and allow evidence-based hypothesis generation. Here, we describe our steps to ensure high quality and demonstrate the utility of linking our data to other datasets on the emerging Semantic Web.

The re-engineering of vocabularies into ontologies can save considerable time in the development of ontologies. Current methods that guide the re-engineering of thesauri into ontologies often convert vocabularies merely syntactically and ignore problems arising from interpreting vocabularies as ontologies, i.e. as sets of statements of facts. Current re-engineering methods also do not make use of the semantic capabilities of formal languages in order to detect logical mistakes and improve vocabularies. In this paper, we introduce a content-focused method for building domain-specific ontologies based on a thesaurus, a popular type of vocabulary. Application of the method results in an ontology that not only adheres to the semantics of the description logic OWL, but also contains a semantically rich description of the modeled entities, enables non-trivial, automated reasoning, and can be integrated with other ontologies following the same development principles. We explain the motivation and sub-activities for each of the steps in our method and illustrate their application through a case study in the domain of agricultural fertilizers based on the ACROVOC Thesaurus. Our method shows, first and foremost, that a considerable manual effort is required to derive a semantically rich ontology from a thesaurus, particularly in connection with the alignment to a top-level ontology as well as for the identification and formal specification of membership conditions. Applying our method will likely change the structure of a thesaurus considerably. Our method is particularly useful where a highly reliable is-a hierarchy or consistent definitions are crucial.