Structured Representation of Information

The Need for Ontologies

Information is scattered within organizations and often not held in such a structured way as to be easily accessed by employees or software. This problem was examined by Lau et al (2005) using the example of McDonnell Douglas (now part of Boeing), that demonstrated how difficult it is to gather unstructured knowledge. Therefore, it is important that research is undertaken into methods of capturing, structuring, distributing, analyzing, and visualizing information.

Taxonomies, Ontologies and Structuring of Information

An ontology is a classification structure. A taxonomy can be just a convenient structure to assist programmers, or part of an overall 'thesaurus' which describes and agreements the meaning of things. This 'thesaurus' structure is the ontology and may contain one or more taxonomies. Engineers may have different names for the same thing, eg wing skin stiffeners may be referred to as stringers, but rib stiffeners are never called stringers. There is a relationship of stringer to stiffener, which needs to be defined, and this definition depends on the context. A classification scheme or ontology is necessary in order to make communication precise. Such an ontology can also be used to help non-specialists to understand the terminology of a particular domain. The ontology can also enable communication between the computer systems and users. Hunter (2002) explains how taxonomies can be the basis of the definitions for an ontology, and that commercial software is available. Hunter gives examples of the Ministry of Defense technology taxonomy, and the Boeing online ontology. The taxonomy "Type-Of" and "Part-Of" relationships can indicate how to construct the taxonomy. Veryard (2001) and McGuinness (2000) provide helpful guides on how ontologies can assist in linking distributed data. This linking and connectivity is also explained in 'Ontologies and Semantics for Seamless Connectivity' Uschold and Gruninger (2004).

Knowledge based systems need to allow a variety of people in different disciplines to share knowledge across functional, departmental, and disciplinary boundaries. Consideration is needed of the further problem that certain knowledge should be shared with others outside the organization such as suppliers, and customers.

There is a strong need for uniting of the approaches of top down ontology definition by a small group of experts with that of the bottom up approach of allowing all users to define the ontology. Software applications are needed that allow users with little software knowledge to edit and update ontologies themselves. The extent to which an organization allows this depends on its structure but if this is completely prevented or not enabled in the first place, there will be user dissatisfaction resulting from their lack of involvement. It is also likely that progress in defining and editing the ontology would be delayed.

The varied user base of knowledge systems results in a further problem, which is that of separation of the language itself. As the users are in different trades and professions they will not need to understand the same words, or assign them to the same meaning. Again this makes it necessary to structure the information in a knowledge-based system carefully, to ensure it can be well visualized, and agreements can be reached.

Relationships between terms such as type-of, and part-of become more important than the term itself, as the relationship defines the meaning of the term by relating it to the other terms. These relationships can then be represented in diagrammatic form and navigated, in order to allow the meaning of terms to be agreed and explained. A classification structure such as this is termed the ontology.

My objective is to build a catalog and make use of it for decision support and costing systems, while demonstrating that the same approach could have been used for other types of system (s). It is essential that this catalog can query information from organizations' existing database systems. Most large organizations have key operational knowledge and information dispersed across different types of information systems, often in relational databases. This has the advantage of allowing the use of the standardized language Structured Query Language (SQL) to access this information.

This research is explained in greater depth at and RDF / RDF.htm .


Hunter, A., 2002. Engineering Ontologies .

Lau, HCW, Ning, A., Pun, KF, Chin, KS, Ip, WH, 2005. A knowledge-based system to support procurement decision. Journal of Knowledge Management, 9 (1), pp. 87-100.

McGuinness, DL, 2000. Conceptual Modeling for Distributed Ontology Environments. Proceedings of the Eighth International Conference on Conceptual Structures Logical, Linguistic and Computational Issues (ICCS 2000), Darmstadt, Germany. August 14-18, 2000.

Uschold, M., Gruninger, M., 2004. Ontologies and Semantics for Seamless Connectivity, Association for Computer Machinery – Special Interest Group on Management of Data – SIGMOD Record December, 33 (4).

Veryard R., 2001. Data Mappings .

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