Advances in Computer Methods for Systematic Biology: Artificial Intelligence, Data-Bases, Computer Vision.

Fortuner, Renaud

doi:10.2307/2533239

Cited by 11 publications

(7 citation statements)

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“…At about the time that many specialized disciplines were struggling with electronic typesetting (ACM, 1988), Payne (1984) reported on such an effort for identification keys, solving problems that are now routine. By the early 1990s a wide range of software and database technologies had been applied to many problems of systematics (Fortuner, 1993).…”

Section: Relation To Other Workmentioning

confidence: 99%

An architecture for electronic field guides

2007

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People who classify and identify things based on their observable or deducible properties (called "characters" by biologists) can benefit from databases and keys that assist them in naming a specimen. This paper discusses our approach to generating an identification tool based on the field guide concept. Our software accepts character lists either expressed as XML (which biologists rarely provide knowingly-although most databases can now export in XML) or via ODBC connections to the data author's relational database. The software then produces an Electronic Field Guide (EFG) implemented as a collection of Java servlets. The resulting guide answers queries made locally to a backend, or to Internet data sources via http, and returns XML. If, however, the query client requires HTML (e.g., if the EFG is responding to a human-centric browser interface that we or the remote application provides), or if some specialized XML is required, then the EFG forwards the XML to a servlet that applies an XSLT transformation to provide the look and feel that the client application requires. We compare our approach to the architecture of other taxon identification tools. Finally, we discuss how we combine this service with other biodiversity data services on the web to make integrated applications.

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Section: Relation To Other Workmentioning

confidence: 99%

An architecture for electronic field guides

2007

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“…These include AI techniques and knowledge-based systems in particular the work of Fortuner. 30 The few examples of knowledge-based systems devoted to taxonomy are reviewed by Edwards and Morse. 29 Some were developed using commercial shells.…”

Section: B Taxonomymentioning

confidence: 99%

A knowledge level analysis of taxonomic domains

Domingo

Sierra

1997

Int. J. Intell. Syst.

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The Knowledge Level (KL) is an abstract level of description, prior to the symbol or software level, which aims at discovering the components of expertise without thinking of computational aspects. The KL analysis emphasizes the regularities in knowledge use for knowledge engineering. We consider the knowledge level analysis the AI counterpart of the specification of programs. Then, it must be possible to define formal ways of putting in relation the KL analysis with computational elements that implement it. The ultimate goal of the research presented in this article is to contribute in the filling of the gap between specification at the KL and implementation. To do so we propose (i) a particular interpretation of the three main concepts involved in the knowledge level theories, i.e., tasks, methods, and domain models, and (ii) a mapping between these notions and computational elements of Milord II, a shell developed at the IIIA Institute and used as the target programming environment of an example in biological identification.

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“…It is important to notice that it was during this period that small computers became more popular and affordable, added to the availability of database software and high-level programming languages, which made possible the creation of experimental databases dedicated to floristic and monographic data, as also morphological and chemical data, among others [7]. In September 1990, an interdisciplinary workshop, organized by University of California and funded by NSF, united different professionals to discuss the benefits of modern computer techniques -expert workstation for systematics, identification, phylogenetic trees, databases, and geographical information systems -for systematic biology [97]. In October 1990, a symposium called "Designs for a Global Plant Species Database", held in Delphi, Greece, focused in discussing different approaches for "creating and operating a global plant species information system -a data system that would provide international access to data accumulated on all of the world's plants."…”

Section: Introductionmentioning

confidence: 99%

A survey of biodiversity informatics: Concepts, practices, and challenges

Gadelha,

de Siracusa,

Ziviani

et al. 2018

Preprint

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The unprecedented size of the human population, along with its associated economic activities, have an ever increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide them. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision-makers in ways that they can effectively use them. The development and deployment of mechanisms to produce these indicators depend on having access to trustworthy data from field surveys and automated sensors, biological collections, molecular data, and historic academic literature. The transformation of this raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques used to manage and analyze this data comprise an area often called biodiversity informatics (or e-Biodiversity). Biodiversity data follows a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges.

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Advances in Computer Methods for Systematic Biology: Artificial Intelligence, Data-Bases, Computer Vision.

Cited by 11 publications

References 0 publications

An architecture for electronic field guides

An architecture for electronic field guides

A knowledge level analysis of taxonomic domains

A survey of biodiversity informatics: Concepts, practices, and challenges

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