Nicholas Del Rio

The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org.

Inference Web in Action: Lightweight Use of the Proof Markup Language

The Inference Web infrastructure for web explanations together with its underlying Proof Markup Language (PML) for encoding justification and provenance information has been used in multiple projects varying from explaining the behavior of cognitive agents to explaining how knowledge is extracted from multiple sources of information in natural language. The PML specification has increased significantly since its inception in 2002 in order to accommodate a rich set of requirements derived from multiple projects, including the ones mentioned above. In this paper, we have a very different goal than the other PML documents: to demonstrate that PML may be effectively used by simple systems (as well as complex systems) and to describe lightweight use of language and its associated Inference Web tools. We show how an exemplar scientific application can use lightweight PML descriptions within the context of an NSF-funded cyberinfrastructure project. The scientific application is used throughout the paper as a use case for the lightweight use of PML and the Inference Web and is meant to be an operational prototype for a class of cyberinfrastructure applications.

Semantic annotation of maps through knowledge provenance

Maps are artifacts often derived from multiple sources of data, e.g., sensors, and processed by multiple methods, e.g., gridding and smoothing algorithms. As a result, complex metadata may be required to describe maps semantically. This paper presents an approach to describe maps by annotating associated provenance. Knowledge provenance can represent a semantic annotation mechanism that is more scalable than direct annotation of map. Semantic annotation of maps through knowledge provenance provides several benefits to end users. For example, a user study is presented showing that scientists with different levels of expertise and background are able to evaluate the quality of maps by analyzing their knowledge provenance information.

Browsing Proof Markup Language Provenance: Enhancing the Experience

Probe-It! is a browser that allows users to navigate through Proof Markup Language (PML) based provenance traces by interacting with a number of different perspectives or views [1]. These views provide specific renderings or presentations for the different kinds of provenance information defined in the PML ontology [2]. Throughout our three year experience with Probe-It! we have gathered requirements from users who have a need for browsing PML captured from theorem provers in the Thousands of Problems for Theorem Provers (TPTP) and Homeland Security domains as well as from scientific processes in areas such as solar astronomy, seismology, and environmental science. This paper briefly describes the enhancements made to Probe-It! to improve usability and performance with regards to visualization.