Representing and reasoning about concrete domains with inference fusion.

Description Logic (DL)-based concept modelling formalisms provide a powerful means to represent and reason about taxonomic knowledge. They benefit from unambiguous semantics which make possible the automatic classification of concept definitions. However, the inevitable "trade-off" between the expressive power and the computational complexity prevents DLs from widely being applied to model problem domains featured by heterogeneous knowledge (e.g, mechanical domains). Current DL-based inferential systems adopt a tableau-based algorithm to enable automated reasoning, which, to some extent, can be considered as a constraint system that relies on concept constructors for specifying restrictions on the defining concepts. Such facts present a reasonable inspiration to envisage a hybrid approach extending existing DL-based inferential engines with expressive and deductive powers of concrete constraints and, in the meantime, leaving the original systems intact—without modifying their underlying inference algorithms. This idea is presented as a generic schema, the inference fusion framework, which dynamically fuses homogeneous reasoners to provide a heterogeneous inference. The fusion process is facilitated by fragmenting and rescheduling reasonings with regard to concrete knowledge to constraint solvers and updating DL-based inferential systems with the corresponding feedback. A mapping mechanism is responsible for the communication between the DL-based reasoning and the constraint-based one. In this thesis, a hybrid modelling language, DL(D)/S, is proposed to demonstrate the applicability of inference fusion. DL(D)/S extends DLs with two extensions that are the Knowledge Base (KB) global constraints defined on hybrid role successors, hybrid concepts, and the numeric constraints on role cardinality variables. A series of examples are used to give an in-depth explanation of the hybrid reasoning regarding DL(D)/S. The CONstrained COncept Reasoning system (CONCOR) system is proposed as the inference fusion-based hybrid reasoning system. CONCOR takes full advantage of existing DL-based inferential systems and constraint solvers. Input hybrid concepts are fragmented, normalised and distributed to different inferential engines if appropriate. With the help of CONCOR, DL-based systems can take the results of the constraint reasoning "blindly" and upgrade the conceptual hierarchical structures accordingly. DL(D)/S is presented as an extension of the expressive DL-based language, ALCN. However, since the inference fusion framework and the CONCOR system do not explicitly depend on any particular DL-based systems for the taxonomic reasoning, other DLs could have been used instead, as long as they meet the basic expressive requirements of the hybrid reasoning. Evaluation of CONCOR with real-world examples presents promising results. Concept taxonomy is correctly updated based on the inferential results of concrete constraints. Evaluation also supports the announced advantages of the CONCOR system architecture. The work presented in this thesis is only an experiment of the inference fusion approach. Further validations and evaluations of this idea and the practical hybrid reasoning systems still require more efforts to be made in the next stage.

Grantor dc:publisher.institution

Robert Gordon University

Year dc:date.issued

2004

Author dc:creator

• Hu, Bo

Advisor dc:contributor.advisor

• I. Arana, E. Compatangelo and S. Craw

• Knowledge representation
• Description logic (DL)
• Taxonomic knowledge
• Constraint satisfaction problems
• Constrained concept reasoning system (CONCOR)
• Hybrid modelling language

Language dc:language

en

Identifier

oai:rgu-repository.worktribe.com:2807415
https://doi.org/10.48526/rgu-wt-2807415

OAI identifier oai:identifier

oai:rgu-repository.worktribe.com:2807415