Principles of Ontological Design

Though there is no set of formal principles that define precisely the structure or content of the ontology we are developing, we can list several principles that constitute the foundation of our methodology and that suggest the guidelines we follow. These can also be viewed as the desiderata for the ontology we are developing.

These principles are more task oriented or situated unlike the structural principles for ontologies that have been proposed in the literature (Bouaud, Bachimont, Charlet, and Zweigenbaum, 1995; Zarri, 1995). In fact, we show below that many of those structural principles had to be violated in our ontology in order to meet the practical objectives of simplicity in representation and software requirements and understandability and ease of browsing by non-experts.

The K ontology is based on the following principles:

1. The ontology is language independent. An ontology is not a transfer dictionary between a pair of languages. Our ontology is language independent in two ways:
   1. It is not specific to any particular natural language such as English or Spanish.
   2. The concepts in the ontology do not have a one-to-one mapping to word senses in natural languages. Many concepts may not map to any single word in a language; other concepts may map to more than one word in the same language.
2. An ontology must be independently motivated, not dictated by the lexicon of a particular language. Ontology development is not subservient to lexicography. The two are sister processes that aid each other and at the same time constrain each other in significant ways.
3. An ontology must be well-formed according to a precise, axiomatic specification (such as the one shown in Appendix B) and internally consistent in structure, naming, and content across concepts.
4. Consistent and compatible with other knowledge and processing resources such as the lexicon, the semantic analyzer, the language of the TMR, and any expert microtheories.
5. An ontology must be rich in content, conceptual structure, and degree of connectivity between concepts. For machine translation and other NLP tasks, its structure is necessarily rich; it is not just a hierarchy of concept names. Each concept is not just a label but has rich internal structure resulting in a high degree of connectivity with other concepts in the ontology.
6. Limited expressiveness is a virtue. In particular, we reject full first-order predicate logic and the ability to make any assertion within the ontology. Limited expressiveness enables users to browse and comprehend the ontology.
7. An ontology must be easy to comprehend and simple to search for concepts. it must be easy to browse, easy to train acquirers, presentable, and so on. For example, an And-Or tree with disjunctive inheritance is not suitable for our ontology because it is too hard to comprehend and use for ontology acquirers and lexicographers. Computational complexity of the inheritance algorithm is not the reason for rejecting complex inheritance methods.
8. It must have a high utility for the task it is meant for. It must ultimately aid language processing in resolving a variety of ambiguities and making necessary inferences. Ontology development is a goal driven process: we have a task, well understood ways of using the product, and an immediate need for the knowledge base we are acquiring. Ontology is language independent but rather language-processing dependent in our methodology:
   1. must have all the necessary knowledge to sufficiently constrain language interpretation and generation.
   2. must be a richly connected network of concepts to enable a variety of inferences.
   3. variable-depth semantics but nevertheless deep. Deeper semantics than possible with just word names or word senses.
9. Cost effectiveness: Do not add all the knowledge just because we know how.
10. Limited scope: This is not an Encyclopedia Britannica. We are different from CyC.
11. Non-episodic. The knowledge we are acquiring is conceptual, not episodic. Non-stereotypic and less unbounded. Onomasticon development is significantly different in scope, methodology, and cost from ontology development. Our methods for concept acquisition are not cost-effective for acquiring instances in the onomasticon. Onomasticon acquisition must be automated to a far higher degree.
12. Not domain specific but does focus on a domain of choice.
13. Technology Aided: The task is made more tractable by the deployment of latest technologies: faster machines, color graphical user interfaces, graphical browsers and editors, on-line lexica, corpora, and other ``ontologies,'' as well as semi-automated interfaces for customer interactions and database maintenance.

• Limited Expressiveness and Usability
• Ontology Development: Desiderata