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In this guide, we have described an ontology-development methodology for declarative frame-based systems. We listed the steps in the ontology-development process and addressed the complex issues of defining class hierarchies and properties of classes and instances. However, after following all the rules and suggestions, one of the most important things to remember is the following: there is no single correct ontology for any domain. Ontology design is a creative process and no two ontologies designed by different people would be the same. The potential applications of the ontology and the designer’s understanding and view of the domain will undoubtedly affect ontology design choices. “The proof is in the pudding”—we can assess the quality of our ontology only by using it in applications for which we designed it. 

we try to provide a starting point; an initial guide that would help a new ontology designer to develop ontologies.

 Despite its explosive growth over the last decade, the Web remains essentially a tool to allow humans to access information. The next generation of the Web, dubbed the ‘Semantic Web’, will extend the Web’s capability through the increased availability machine-processable information. These machine-processable descriptions of Web information resources are called meta-data and are associated with ontologies, or conceptualisations of the domain of application. Meta-data and associated ontologies then allows more intelligent software systems to be written, automating the analysis and exploitation of Web-based information.This paper describes how knowledge management can be improved through the adoption of Semantic Web technology. To realise this, a number of different technologies need to be brought together. Their fusion provides the infrastructure which makes semantic knowledge management possible. Specifically, the paper discusses the use of knowledge discovery and human language technology to (semi-)automatically derive the required ontologies and meta-data, along with a methodology to support this process. We describe techniques for management and controlled evolution of ontologies and a set of semantic knowledge access tools for enhanced information access. Finally, a set of application scenarios for the technology are sketched.

This article is an attempt to clarify the distinct roles for ontologies and folksonomies, and previews some new work that applies the two ideas together - an ontology of folksonomy.

This piece is based on two talks I gave in the spring of 2005 -- one at the O'Reilly ETech conference in March, entitled "Ontology Is Overrated", and one at the IMCExpo in April entitled "Folksonomies & Tags: The rise of user-developed classification." The written version is a heavily edited concatenation of those two talks.

Peter Morville explores the future present in mobile devices, search algorithms, ontologies, folksonomies, findable objects, digital librarianship, and the long tail of the sociosemantic web

Knowledge organisation systems, such as taxonomies, thesauri or subject heading lists, play a fundamental role in information structuring and access. The Semantic Web Deployment Working Group aims at providing a model for representing such vocabularies on the Semantic Web: SKOS (Simple Knowledge Organisation System). This document presents the preparatory work for a future version of SKOS. It lists representative use cases, which were obtained after a dedicated questionnaire was sent to a wide audience. It also features a set of fundamental or secondary requirements derived from these use cases, that will be used to guide the design of SKOS.

To be able to answer this question in general principles, we can postulate that a search engine must understand what is going on, which requires algorithms capable of understanding content. This is what experts in the search field have called* Semantic Search Technology (SST).

We've engineered a radical new search technology that allows users to experience improved search results with features like categorization of search results, highlighting best sentences, and complete text snippets. This new approach is based on our proprietary knowledge-gathering system, called QDEX, and a ranking algorithm, called SemanticRank.