Application of Semantic Technology to Define Names for Fungi

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Concepts:eScience & Web Science


The need for well-defined, persistent descriptions of taxa that can be accurately interpreted by computers is becoming increasingly clear. The goal of this work is to develop named descriptions of Fungi that enable automated reasoning by computers. We encode these descriptions using the Web Ontology Language (OWL). The initial target audience is field mycologists using the Mushroom Observer website, who range from professional scientists to beginning mushroom enthusiasts. We describe our mycology ontology and propose developing a transparent, community-based ontology evolution process. The ontology was designed to focus on properties that can be observed in the field, but the framework is proving to be suitable for microscopic, chemical, and genomic properties as well. Concrete examples are provided where field mycologists need names for groups of similar-looking Fungi that are known to belong to different species, and where our approach can significantly increase the precision of information recorded by the observer. Such a system is important for enabling the field mycologist to make more meaningful contributions to the modern scientific literature. In addition, the resulting ontology and descriptions provides a foundation for consistent, unambiguous, computational representations of Fungi. Finally, we expect that such a system will enable more people to become active field mycologists by providing a more robust way to document field observations and connect those observations with information about similar fungi.


DateCreated ByLink
May 28, 2012
Patrick WestDownload
May 28, 2012
Patrick WestDownload

Related Projects:

SVF LogoSemantic Vernaculars for Fungi (SVF)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Nathan Wilson
Description: Fungi are typically referred to by either scientific or common names. Neither of these terminologies meets the need for well-defined, persistent definitions of groups of fungi who exhibit similar macroscopic qualities, but may be dissimilar genetically. We propose a community-developed vocabulary that can be used to identify mushrooms based on properties that can be observed in the field (without microscopic or genomic examination). We show how an ontology can be used to develop and organize the terms and definitions and to enable applications based on the vocabulary.

Related Research Areas:

Semantic eScience
Lead Professor: Peter Fox
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience