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The Bibliographic Ontology has just been published.

Despite significant advancement in technology and tools, building ontologies, annotating data, and aligning multiple ontologies remain tasks that highly depend on human intelligence, both as a source of domain expertise and for making conceptual choices. This means that people need to contribute time, and sometimes other resources, to this endeavor. As a novel solution, we have proposed to masquerade core tasks of weaving the Semantic Web behind on-line, multi-player game scenarios, in order to create proper incentives for humans to contribute. Doing so, we adopt the findings from the already famous "games with a purpose" by von Ahn, who has shown that presenting a useful task, which requires human intelligence, in the form of an on-line game can motivate a large amount of people to work heavily on this task, and this for free.

Rapid advances in information technologies continue to drive a flood of data and analysis techniques in ecological and environmental sciences. Using these resources more effectively and taking advantage of associated cross-disciplinary research opportunities poses a major challenge to both scientists and information technologists. These challenges are now being addressed in projects that apply knowledge representation and Semantic Web technologies to problems in discovering and integrating ecological data and data analysis techniques. In this paper, we present an overview of the major ontological components of our project, SEEK ("Science Environment for Ecological Knowledge"). We describe the concepts and models that are represented in each, and present a discussion of potential applications of these ontologies on the Semantic Web

Our aim is to advance the state of the art in using ontologies for large-scale semantic applications in the distributed organizations. Particularly, we aim at improving the capability to handle multiple networked ontologies that exist in a particular context, are created collaboratively, and might be highly dynamic and constantly evolving.

VoCamp is a series (hopefully) of informal events where people can spend some dedicated time creating lightweight vocabularies/ontologies for the Semantic Web/Web of Data. The emphasis of the event(s) is not on creating the perfect ontology in a particular domain, but on creating vocabs that are good enough for people to start using for publishing data on the Web. The intention is to follow a "paper first, laptops second" format, where the modelling is done initially on paper and only later committed to code. The VoCamp idea is heavily influenced by BarCamp, although the VoCamp should only have presentations where strictly necessary.

MICRA is hosting the COSMO (COmmon Semantic MOdel) ontology, a public, open foundation ontology that is intended to include all the basic elements required to specify the meanings of any more specific concept in any domain.

This is Draft 2.0 of The Open Group's SOA ontology. This draft is being exposed for comment outside The Open Group prior to formal Open Group company review. Interested parties are invited to sent comments, and those comments will be addressed in the version submitted to formal review by The Open Group.

The OntologyDesignPatterns.org is a semantic web portal dedicated to ontology design patterns (OPs) for the Semantic Web developed in the context of the Neon project (http://www.neon-project.org).

NCBO Seminar Series talks occur via the Web and teleconference at 10:00AM, Pacific time, the 1st and 3rd Wednesday of every month. This series aims to showcase new projects, technologies and ideas in biomedical ontology by featuring the work of a different collaborator each session. It is open to anyone interested, regardless of location or affiliation.

Ontology Repository

# The primary intention of creating aTags is not the categorization of the document, but the representation of the key facts inside the document. Key facts in the biomedical domain might be, for example, "Protein A interacts with protein B" or "Overexpression of protein A in tissue B is the cause of disease C". # An aTag is comprised of a set of associated entities. The size of the set is arbitrary, but will typically lie between 2 and 5 entities. For example, the fact "Protein A binds to protein B" can be represented with an aTag comprising of the three entities "Protein A", "Molecular interaction" and "Protein B". Similarly, the fact "Overexpression of protein A in tissue B is the cause of disease C" can be represented with an aTag comprising of the four entities "Overexpression", "Protein A", "Tissue B" and "Disease C". # Each document or database entry can be described with an arbitrary number of such aTags. Each aTag can be associated with the relevant portions of text or data in a fine granularity. # The entities in an aTag are not simple strings, but resources that are part of ontologies and RDF/OWL-enabled databases. For example, "Protein A" and "Protein B" are resources that are defined in the UniProt database, whereas "Molecular Interaction" is a class in the branch of biological processes of the Gene Ontology. They are identified with their URIs.

A semantic web experiment with a focus on Ontology Visualisation and Querying.

The research team Edelweiss aims at offering models, methods and techniques for supporting knowledge management and collaboration in virtual communities interacting with information resources through the Web. This research will result in an ergonomic graph-based and ontology-based platform.

In 2000, Nicola Guarino and Chris Welty published a series of papers on what came to be named OntoClean, a methodology for ontology-driven conceptual analysis. The methodology has been receiveing increasing levels of attention in various communities, and on this page we hope to provide a central repository for information regarding OntoClean, to help keep the community in touch with how the methodology is being implemented, supported, used, extended, and referenced.

mSpace is an interaction model designed to allow a user to navigate in a meaningful manner the multi-dimensional space that an ontology can provide. mSpace offers potentially useful slices through this space by selection of ontological categories.

My topics of main interest are: 1) Associative Tags; 2) Agreement, Disagreement, discourse; 3) Corporate Semantic Web, 4) Are upper level ontologies/vocabularies not so bad after all?, 5) Cleaner schemas and ontologies

MnM is an annotation tool which provides both automated and semi-automated support for annotating web pages with semantic contents. MnM integrates a web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools.

Natural languages are easy to learn by infants, they can express any thought that any adult might ever conceive, and they accommodate the limitations of human breathing rates and short-term memory. The first property implies a finite vocabulary, the second implies infinite extensibility, and the third implies a small upper bound on the length of phrases. Together, they imply that most words in a natural language will have an open-ended number of senses - ambiguity is inevitable.To achieve a comparable level of flexibility with formal ontologies, this paper proposes an organization with a dynamically evolving collection of formal theories, systematic mappings to formal concept types and informal lexicons of natural language terms, and a modularity that allows independent distributed development and extension of all resources, formal and informal.

By integrating heterogeneous original data with uniform RDF format, creating additional ontology mappings between GO terms, and introducing a set of reasoning rulebases, we proposed a RDF model GORouter.

The prototype we describe is a biomedical knowledge base, constructed for a demonstration at Banff WWW2007 , that integrates 15 distinct data sources using currently available Semantic Web technologies such as the W3C standard Web Ontology Language [OWL] and Resource Description Framework [RDF]. This report outlines which resources were integrated, how the knowledge base was constructed using free and open source triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL [SPARQL], and what resources and inferences are involved in answering complex queries. While the utility of the knowledge base is illustrated by identifying a set of genes involved in Alzheimer's Disease, the approach described here can be applied to any use case that integrates data from multiple domains.