Investigating the use of background knowledge for assessing the relevance of statements to an ontology in ontology evolution

The tasks of learning and enriching ontologies with new concepts and relations have attracted a lot of attention in the research community, leading to a number of tools facilitating the process of building and updating ontologies. These tools often discover new elements of information to be included in the considered ontology from external data sources such as text documents or databases, transforming these elements into ontology compatible statements or axioms. While some techniques are used to make sure that statements to be added are compatible with the ontology (e.g. through conflict detection), such tools generally pay little attention to the relevance of the statement in question. It is either assumed that any statement extracted from a data source is relevant, or that the user will assess whether a statement adds value to the ontology. In this paper, we investigate the use of background knowledge about the context where statements appear to assess their relevance. We devise a methodology to extract such a context from ontologies available online, to map it to the considered ontology and to visualize this mapping in a way that allows to study the intersection and complementarity of the two sources of knowledge. By applying this methodology on several examples, we identified an initial set of patterns giving strong indications concerning the relevance of a statement, as well as interesting issues to be considered when applying such techniques

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease