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  • Nicola, A.D., Missikoff, M. & Navigli, R. (2009). A software engineering approach to ontology building. Information Systems, 34, 258-275
Article saved as: nicola_2008
The steps followed for the terms were: (a) the relevant terms in the domain were identified and gathered in a lexicon, (b) these terms were enriched with a definition, creating a glossary, (c) the basic semantic network was produced by populating the glossary with the basic ontological relationships and (d) the final formalization procedures created the domain ontology.
The scope of the ontology is to identify the concepts and their characteristics. The Competency Questions are the ones that the ontology must be in position to answer. The Web Ontology Language (OWL) is currently the main candidate for encoding an ontology to be used on the Semantic Web. The quality of the ontology must be divided in the following aspects: (a) syntactic: that measures the quality of the ontology according to the way it is written, (b) semantic: where the primary concern is the absence of contradictory concepts, (c) pragmatic: the ontology’s content and usefulness for the users, irrespectively of the syntax and the semantics, (d) social: the members of the ontologies that are linked too. IEEE 1074-1995 is the Standards that apply to ontologies;

  • Deliverable 1.4: [ A survey on the methodologies for developing, maintaining, evaluating and reengineering ontologies] (2002). Retrieved July 7, 2009
Article saved as: onto_web_2002
The approaches for building ontologies from scratch are:

Cyc Methodoloy (

  • The first phase proposes to manually code the explicit and implicit knowledge appearing in the knowledge sources without the help of natural language and learning systems. This first phase is carried out by hand, since current natural language systems and learning machines do not handle enough common sense knowledge to search for new common sense knowledge. “Development of knowledge representation and top level ontology containing more abstract concepts”.
  • The second phase proposes knowledge codification that is aided by tools using knowledge already stored in the Cyc KB. “Representation of the rest of the knowledge using primitives”.
  • The third phase delegates to the tools the majority of the work. People will only recommend knowledge sources to read, and they will explain the most difficult parts of the text.
Uschikd and King’s method:
1. Identify purpose why the ontology is being built
2. Building the ontology, which is broken down into three steps:
2.1. Ontology capture, which means:
− Identification of the key concepts and relationships in the domain of interest, that is, scoping. It is important to centre on the concepts as such, rather than the words representing them.
− Production of precise unambiguous text definitions for such concepts and relationships.
− Identification of terms to refer to such concepts and relationships.
2.2. Coding. Involves explicitly representing the knowledge acquired in step 2.1 in a formal language.
3. Evaluation of the ontology
4. Documentation recommends that guidelines be established for documenting ontologies, possibly differing according to the type and purpose of the ontology

  • Becker, P., Eklund, P. & Roberts, N. (2006). RDF-based P2P based ontology editing. Journal of Digital Information Management, 4(1), p. 50- 55
Article saved as: becker_2006
This article explains the creation of ontologies based on RDF and using Peer-to-Peer (P2P) networking architecture. Extensions to an ontology browsers, called ONTORAMA.

The ontologies servers available currently are: (a) Ontolingua server, which permits the re-use of the Knowledge Interface Format (KIF) files (b) Ontosaurus in another web-accessible ontology server, which permits each user to build or edit ontological content (c) TADZEBAO and WEB-ONTO support some synchronous cooperation between co-temporal users. It runs as an applet. Ontology merging means that a single ontology is created from n existing ontologies. Ontology alignment refers to creating links between ontologies so that the ontologies can be used as one. Editing ontologies in a P2P network means more freedom and less control in comparison to the classical client/server approach. This approach is based on RDF so an ontology can be moved into a served once it has reached a stable state. In this case, all users have access to the ontologies and they decide which ontologies fit best for the term described. The ones that are not accepted as suitable ontologies, are deleted, but are stored, as another user may approve the term. The terms with the most deletions are the ones that are taken out from the set of ontologies. At the end of the article there is a description on the ONTORAMA software.

  • Aiello, A. et al. (2006). An experimental ontology server for an information grid environment. International Journal of Parallel Programming, 34(6).
Article saved as: aiello_2006
The authors of the article create a repository for ontologies and their metadata. The service is summarized with the following applications:
  • annotation services: entities together with their metadata, in order to attach semantic content to those entities
  • ontology services: provide access to concepts in an underlying ontology data model
  • inference engine: applies different kinds of reasoning over the same ontologies and the same metadata set.

The ontologies lifecycle is consisted of the following steps: (a) a well defined syntax, (b) formal semantics, (c) an efficient support, (d) sufficient expressiveness power. The Resource Description Framework (RDF) provides a foundation for representing and processing data. The key concepts of RDF Schema are classes, subclass relations, property, subproperty relations, as well as domain and range restrictions. Ontology Web Language (OWL) is the standard for ontologies proposed by W3C. The OWL builds on top of the RDF and RDF Schema, uses RDF’s XML-based syntax and allos to describe the semantics of knowledge in a machine accessible way. Building ontologies tools: (a) Protégé-2000 editor, (b) OilEd, (c) OntoEdit, (d) Ontolingua and (e) WebODE. Most of these tools provide an integrated environment to build ontologies, check for errors, and inconsistencies, to browser multiple ontologies, and to share and reuse existing data by establishing mappings among different ontological entities.

  • Stecher, R., Niederee, C., Nejdl, W. & Bouquet, P. (2008). Adaptive ontology re-use: finding and re-using sub-ontologies. International Journal of Web Information Systems, 4(2), p.198-214
Article saved as: stecher_2008
This article is not very relevant, it talks about re-use of existing ontologies as a starting point.

Considering this more systematically, we distinguish three types of ontology re-use: (1) With conservative re-use the re-used ontology stays unaffected. Concepts, properties or individuals are used in the way they are defined in the re-used ontology, e.g. for defining new subclasses. This type of re-use is, for example, reflected in the work of Grau et al. (2007). (2) In adaptive re-use, the re-used ontology provides a starting point for local definitions, possibly changing the way concepts and properties are defined to fit the own purposes. (3) In best practice re-use, the know-how, best practices, and experiences of how an ontology is constructed are re-used as in Uschold et al. (1998) and Rector (2003).

  • Yi, M. (2008). Information Organization and Retrieval Using a Topic Maps-Based Ontology: Results of a Task-Based Evaluation. Journal of the American Society for Information Science and Technology, 59(12) p. 1898-911
Article saved as: yi_2008
This article may not be relevant as it describes topic maps. In each topic map any term is called a topic, and any term in the thesaurus can be seen as a topic in Topic Maps. The most powerful aspect of Topic Maps is the ability to create associations between topics.

Information organization is created with three ways: (a) term lists, which contains lists of words, phrases and definitions, eg. dictionaries (b) classification and categorization methods, which sort information into groups of similar units, which adds a structure, eg. taxonomies and faceted classification. (c) relationship groups, which emphasize relationships between the terms, eg. thesauri, concept maps and ontologies are other examples of relationship groups. Because ontologies can encode semantic relationships between data elements rather than just basic information about the elements themselves, ontologies are more capableof describing complex, less-structured data than are typical databases (Krishnamurthy, 2006). Studies use the Resource Description Frameworkd (RDF) to implement ontologies. Topic Maps, RDF and OWL are the three options available for ontology.

  • Smith, B. et al. (2007). The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology, 25(11), p. 1251- 1255
Article saved as: smith_2007
The OBO project aims to become an umbrella for the developers of life-science ontologies. It is supported by the NIH Roadmap National Center for Biomedical Ontology (NCBO). The Foundry initiative also serves to align ontology development efforts carried out by separate communities, for example in research on different model organisms. The initiation of the Common Anatomy Reference Ontology (CARO) provides guidelines both for model-organism communities with legacy anatomy ontologies who wish to initiate reforms in the direction of compatibility and for communities who wish to build new ontologies from scratch.

Methodology: the ontologies created express relations. As an example, the words “is_a” or “part_of” are used to give the relationships of the terms. The Foundry is providing guidelines for the creation of the relationships, naming conventions and pathway representations. Compound terms should be created from constituent terms drawn from Foundry ontologies. The Protégé editor is used, which converts the OBO format files into OWL files and vice versa. The following link includes information on mapping the OBO to OWL Tools: OBO-Edit [1] , an open source Java tool, offers a convenient visual interface for creating, editing, and browsing ontologies. It was initially created to work on OBO-format ontologies, but is currently being extended to handle other ontology formats, such as OWL. How should we resolve conflicts when ontologies overlap? Make every attempt to resolve inconsistencies and merge the ontologies. Tools are available for aiding this process (such as OBOL [@Berkley] and Prompt [2]) and the community can be used to help clarify best practices. Also, face-to-face meetings can helpful to resolve details that cannot be covered through email.

Ontolingua: a distributed collaborative environment to browse, create, edit, modify, and use ontologies. [@]


Ontolingua provides a distributed collaborative environment to browse, create, edit, modify, and use ontologies. The server supports over 150 active users, some of whom have provided us with descriptions of their projects.

Chimaera is a software system that supports users in creating and maintaining distributed ontologies on the web. Two major functions it supports are merging multiple ontologies together and diagnosing individual or multiple ontologies. It supports users in such tasks as loading knowledge bases in differing formats, reorganizing taxonomies, resolving name conflicts, browsing ontologies, editing terms, etc. The user manual may be viewed from here.