Reuse of terminological resources for efficient ontological engineering in Life Sciences

This paper is intended to explore how to use terminological resources for ontology engineering. Nowadays there are several biomedical ontologies describing overlapping domains, but there is not a clear correspondence between the concepts that are supposed to be equivalent or just similar. These resources are quite precious but their integration and further development are expensive. Terminologies may support the ontological development in several stages of the lifecycle of the ontology; e.g. ontology integration. In this paper we investigate the use of terminological resources during the ontology lifecycle. We claim that the proper creation and use of a shared thesaurus is a cornerstone for the successful application of the Semantic Web technology within life sciences. Moreover, we have applied our approach to a real scenario, the Health-e-Child (HeC) project, and we have evaluated the impact of filtering and re-organizing several resources. As a result, we have created a reference thesaurus for this project, named HeCTh

Linked Humanities Data: The Next Frontier? A Case-study in Historical Census Data

This paper discusses the use of Linked Data to harmonize the Dutch censuses (1795-1971). Due to the long period they cover, census data is notoriously difficult to compare, aggregate and query in a uniform fashion. In social history, harmonization is the (manual) process of restructuring, interpreting and correcting original data sources to make a comparison possible. We describe a harmonization methodology based on standard Linked Data principles, illustrate how the size and complexity of the resulting linked data source poses new challenges for Semantic Web technology, and discuss potential solutions

QolA: Fostering Collaboration Within QA

In this paper we suggest a QA pilot task, dubbed QolA, whose joint rationale is allow for collaboration among systems, increase multilinguality and multicollection use, and investigate ways of dealing with different strengths and weaknesses of a population of QA systems. We claim that merging answers, weighting answers, choosing among contradictory answers or generating composite answers, and verifying and validating information, by posing related questions, should be part and parcel of the question answering process. The paper motivates these ideas and suggests a way to foster research in these areas by deploying QA systems as Web services

Learning concept mappings from instance similarity

Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are actually being used. However such methods have not at present been widely investigated in ontology mapping, compared to linguistic and structural techniques. Furthermore, previous instance-based mapping techniques were only applicable to cases where a substantial set of instances was available that was doubly annotated with both vocabularies. In this paper we approach the mapping problem as a classification problem based on the similarity between instances of concepts. This has the advantage that no doubly annotated instances are required, so that the method can be applied to any two corpora annotated with their own vocabularies. We evaluate the resulting classifiers on two real-world use cases, one with homogeneous and one with heterogeneous instances. The results illustrate the efficiency and generality of this method