Practical non-monotonic knowledge-base system for un-regimented domains: A Case-study in digital humanities

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Highlights

  • Un-regimented information systems can be developed using non-monotonic KB-services.

  • Flexible and dynamic information systems can be supported by non-monotonic KB-services.

  • Idiosyncrasies in digital humanities can be captured with non-monotonic ontologies.

Abstract

Information systems for un-regimented domains such as museums, art and book collections, face representational and usability challenges that surpass the demands of traditional information systems for regimented domains. While the former require complex conceptual models supporting a set of dynamic and evolving qualitative properties of a small number of objects, the latter focus on the quantitative aspects of a possibly very large number of objects but with a relatively small and stable set of properties. In this paper we study the use of a non-monotonic knowledge-base system for the development of information systems for un-regimented domains. We discuss the ontological assumptions of the formalism, its structure and its inferential mechanisms through a simple example. Then we present an information system for a highly un-regimented domain in the digital humanities with promising results. The present study shows that the so-called extensible, flexible, dynamic or evolving information systems need the expressive power of non-monotonic knowledge-base systems, and that such phenomena should be addressed explicitly.

Section snippets

Information systems for un-regimented information domains

Regimented Information Domains admit standardization and can be modeled with the normal assumptions of spread-sheets and relational data-bases. These domains may include a huge set of objects but with a relatively small and stable number of mostly quantitative properties of a moderate number of types. These oppose to Un-regimented Information Domains that have a large number of types with an open ended set of mostly qualitative properties, although the number of individuals of each type may be

Expressive power and un-regimented information

Regimented information can be handled by representations with a limited expressive power. In the case of relational data-bases all objects must have unique identifiers to avoid ambiguities –the so-called unique name assumption– and the information expressed in the tables is assumed to be complete –the so-called Closed-World Assumption (CWA)) (Levesque & Brachman, 1985)). Conversely, if something is not expressed in the system it is assumed to be false. However, there are knowledge-domains where

Related work

Knowledge representation and reasoning (KRR) have traditionally been one of the main fields of study in Artificial Intelligence. Throughout more than 60 years of research, different logical formalisms have been exploited for this purpose, most commonly first-order logic and description logic (DL). Multiple languages and inference systems to represent and reason about knowledge have been derived from these formalisms, including KL-ONE (Brachman & Schmolze, 1985), LOGIN (Aït-Kaci, Nasr, & Seo,

Non-monotonic taxonomies

The basic ontological assumption of the present knowledge-base system is that there is a set of individual objects constituting the universe or domain of discourse. The domain is divided into a set of mutually exclusive partitions. We define a class as a partition abstracting away their individual objects. Each partition can be further divided to conform subclasses down to the basic partitions representing the basic classes. This is illustrated in Fig. 1 where each individual object is

Discussion and further work

In the present paper we have investigated the creation and use of knowledge resources for un-regimented knowledge domains. These domains are focused on the representation of individuals with unique characteristics or uncommon objects that merit particular studies. The specification of the domain involves the definition of a taxonomy, with classes and individuals, that have general and specific properties and relations respectively. The knowledge is normally acquired incrementally, its

CRediT authorship contribution statement

Luis A. Pineda: Writing - original draft, Writing - review & editing. Noé Hernández: Writing - review & editing. Iván Torres: Writing - review & editing. Gibrán Fuentes: Writing - review & editing. Nydia Pineda De Avila: Writing - review & editing.

Acknowledgments

The authors acknowledge the support of CONACyT’s Project 178673 and PAPIIT-UNAM Projects IN109816 and IN112819.

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