Dipe-R: a knowledge representation language
Introduction
For various reasons, a person may need knowledge on a specific subject area. One common reason is that it is necessary for successfully doing a task or one’s work, another common reason is curiosity. One way of becoming familiar with a specific subject area is to read (textual) documents about that area. By doing so, the reader gets to know the main concepts, and their names, and how they relate to each other and to other concepts. However, such reading may take a lot of effort, since often one has to read many texts before the answers one needs are given. Especially, it is difficult to obtain a quick overview of the area.
An alternative way of familiarising oneself with a subject area is to represent knowledge of that area in a more structured (formalised) manner, and to explore the thus obtained knowledge representation with a software tool. For example, the user may ask everything that is ‘known’ (i.e., represented) about the concept named ‘optical storage device’. The tool may then present all the statements (‘facts’) in the knowledge representation that contain this concept. Applications of this approach can be found in Information Retrieval, organisational or corporate memories, and expert systems.
It is the latter approach (i.e., the formalisation of knowledge) that we try to improve on in our research. We have explored the theory, and on that basis we have designed an improved knowledge representation language: Dipe-R.
Dipe-R was developed to support a tool that interactively formulates queries, i.e., Information Retrieval. The tool (Dipe-D) is reported in [44], and both Dipe-D and Dipe-R were originally reported in [43]. Our reason for developing a new knowledge representation language is that existing representation languages did not meet the requirements set by Dipe-D (and similar tools). Hence, they do not adequately represent knowledge. These shortcomings go beyond the specific purpose of query formulation; they seem generic for representing knowledge.
The paper is structured as follows. Section 2 poses requirements on Dipe-R. Section 3 discusses related research on representation languages. Section 4 introduces the design of Dipe-R, and 5 Content and expression in Dipe-R, 6 Source information, 7 Derivation work out the design. Section 8 provides some experimental support. Finally, Section 9 draws conclusions and makes suggestions for future research.
Section snippets
Assumptions and requirements
On the basis of a number of assumptions (Section 2.1), we specify our requirements for Dipe-R (Section 2.2).
Related research
The section explores related research in the field of knowledge representation as far as relevant to Information Retrieval. In the first place, these are representations common in Information Retrieval, such as (a) thesauri and (b) dictionaries. Additionally, there are also representations from outside Information Retrieval that partially meet our requirements: (c) semantic networks, (d) the KL-ONE family, and the products of two large-scale projects in knowledge representation, viz. (e) the
Overall design of Dipe-R
The main features of Dipe-R are introduced one by one.
Content and expression in Dipe-R
Section 5 treats the content of represented thoughts. Section 5.1 presents what types of thoughts Dipe-R represents and Section 5.2 elaborates Dipe-R’s expression mechanism.
Source information
In line with the general design of Section 4, Dipe-R stores source information for each represented thought. We decide to represent source information along with the content of a represented thought (instead of separately). This allows for a simple implementation (not needing second order expressions or other difficulties), and still allows for the source information to be used.
The source information of Dipe-R comprises:
- 1.
the name of the person holding the thought, or the URL of the document from
Derivation
As mentioned in Section 4, Dipe-R contains a derivation procedure. We mentioned in Section 4.1 that this allows for the representation of potential thoughts. By deriving obvious potential thoughts, that are not explicitly represented, less labour for representation is required. However, the advantage is at the cost of some certainty: the potential thoughts derived may be not accepted by the origins. Dipe-R only provides derivations that are very likely to be accepted by the users and that seem
Dipe-D
Dipe-R was developed simultaneously with Dipe-D, a tool for formulating (Boolean) queries. Dipe-D formulates queries in a two-step process:
- 1.
The identification of concepts (representing a user’s information need), by describing them by their types and features, and then solving this specification by consulting a knowledge base (in Dipe-R).
- 2.
The transformation of the collection of concepts into a Boolean query, essentially by looking up the designations for each concept.
In our experiments with
Conclusion(s)
On the basis of the preceding sections, we draw as conclusions:
- 1.
Existing knowledge representation languages do not meet the two requirements that we regard reasonable for knowledge representation.
- 2.
Dipe-R meets the requirements mentioned, and as such is in theory better suited for representing knowledge than other knowledge representation languages.
- 3.
On the basis of experiments, Dipe-R seems well suited for supporting query formulation processes comprising two steps, viz. concept identification and
Ruud van der Pol (1962) holds a MSc in engineering. After doing his military service, he worked as a thermal engineer for the aerospace company Fokker Space and Systems and as a patent attorney for the patent law firm EP&C. In 2000, he received a Ph.D. in Computer Science, titled “Knowledge-Based Query Formulation in Information Retrieval.” Currently he is developing a flexible activity and data management system for consultants and lawyers, thereby using his insights on knowledge
References (53)
- et al.
An overview of the KL-ONE knowledge representation system
Cognitive Science
(1985) A translation approach to portable ontologies
Knowledge Acquisition
(1993)Concepts, attributes and arbitrary relations; Some linguistic and ontological criteria for structuring knowledge bases
Data and Knowledge Engineering
(1992)- et al.
Using explicit ontologies in KBS development
International Journal of Human and Computer Studies
(1997) - et al.
Case-based reasoning: foundational issues, methodological variations, and system approaches
AI Communications
(1994) Epistemology: a contemporary introduction to the theory of knowledge
(1998)- A. Borgida, R.J. Brachman, D.L. McGuinness, L.A. Resnick, CLASSIC: a structural data model for objects, in: Proceedings...
- et al.
A semantics and complete algorithm for subsumption in the CLASSIC description logic
Journal of Artificial Intelligence Research
(1994) - et al.
Physical systems ontology
What IS-A is and isn’t: an analysis of taxonomic links in semantic networks
IEEE Computer
(1983)
I lied about the trees
AI Magazine
KRYPTON: a functional approach to knowledge representation
IEEE Computer
Prolog Programming for Artificial Intelligence
Rule-based expert systems: the MYCIN experiments of the stanford heuristic programming project
OntoSeek: using large linguistic ontologies for accessing on-line yellow pages and product catalogs
IEEE Intelligent Systems
CYC: a midterm report
AI Magazine
Enabling agents to work together
Communications of the ACM
A thesaurus data model for an intelligent retrieval system
Journal of Information Science
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
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Ruud van der Pol (1962) holds a MSc in engineering. After doing his military service, he worked as a thermal engineer for the aerospace company Fokker Space and Systems and as a patent attorney for the patent law firm EP&C. In 2000, he received a Ph.D. in Computer Science, titled “Knowledge-Based Query Formulation in Information Retrieval.” Currently he is developing a flexible activity and data management system for consultants and lawyers, thereby using his insights on knowledge representation obtained while working on his thesis. His interests include Information Retrieval, Knowledge Representation and Reasoning, Organisational Memories, WorkFlow Management, as well as cognitive neuroscience.