REASONING WITH MULTILEVEL CONTEXTS IN SEMANTIC METANETWORK

In this paper, a multilevel semantic network is proposed to be used to represent knowledge within several levels of contexts. The zero level of representation is semantic network that includes knowledge about basic domain objects and their relations. The first level of presentation uses semantic network to represent contexts and their relationships. The second level presents relationships of metacontexts i.e. contexts of contexts, and so on at the higher levels. The topmost level includes knowledge which is considered to be “truth” in all the contexts. Thus a semantic metanetwork is the hierarchical set of semantic networks above each other so that relations of each previous level are context objects of the next level. Such representation allows to reason with contexts towards solution of the following problems: to derive knowledge interpreted using all known levels of its context; to derive unknown knowledge when interpretation of it in some context and the context itself are known; to derive unknown knowledge about a context when it is known how the knowledge is interpreted in this context; to transform knowledge from one context to another. Possible transformations with contexts are described using special algebra. Equations of the algebra are discussed and used to reason with this multilevel context structure.

[1]  Varol Akman,et al.  Steps Toward Formalizing Context , 1996, AI Mag..

[2]  Ian A. Mason,et al.  Propositional Logic of Context , 1993, AAAI.

[3]  Bruce Edmonds A Simple-Minded Network Model with Context-like Objects , 1997 .

[4]  Maria Simi,et al.  A Formalization of Viewpoints , 1995, Fundam. Informaticae.

[5]  J. McCarthy A LOGICAL AI APPROACH TO CONTEXT , 1995 .

[6]  Patrick Brézillon,et al.  Cooperating for assisting intelligently operators , 1995 .

[7]  Lokendra Shastri,et al.  Default Reasoning in Semantic Networks: A Formalization of Recognition and Inheritance , 1989, Artificial Intelligence.

[8]  Peter Martin,et al.  Knowledge acquisition and synthesis in a multiple source multiple domain process context , 1995 .

[9]  Sasa Buvac Quantificational Logic of Context , 1996, AAAI/IAAI, Vol. 1.

[10]  Stuart C. Shapiro,et al.  Cables, Paths, and "Subconscious" Reasoning in Propositional Semantic Networks , 1991, Principles of Semantic Networks.

[11]  Seppo Puuronen,et al.  Modeling Consensus Knowledge from Multiple Sources Based on Semantics of Concepts , 1996, ER Workshop Challenges of Application and Challenges of Design.

[12]  John McCarthy,et al.  Notes on Formalizing Context , 1993, IJCAI.

[14]  Stuart J. Russell,et al.  Principles of Metareasoning , 1989, Artif. Intell..

[15]  Joseph Y. Halpern,et al.  A Guide to Completeness and Complexity for Modal Logics of Knowledge and Belief , 1992, Artif. Intell..

[16]  Patrick Brézillon,et al.  Using Knowledge in Its Context: Report on the IJCAI-93 Workshop , 1995, AI Mag..

[17]  William A. Woods,et al.  Understanding Subsumption and Taxonomy: A Framework for Progress , 1991, Principles of Semantic Networks.

[18]  Ian A. Mason,et al.  The Semantics of Propositional Contexts , 1994, ISMIS.

[19]  Ian A. Mason,et al.  Metamathematics of Contexts , 1995, Fundam. Informaticae.