What KL-ONE lookalikes need to cope with natural language
Proceedings of the workshop on Sorts and types in artificial intelligence
ACM SIGART Bulletin - Special issue on implemented knowledge representation and reasoning systems
SNePS: a logic for natural language understanding and commonsense reasoning
Natural language processing and knowledge representation
An Integrated Approach to Semantic Evaluation and Content-Based Retrieval of Multimedia Documents
ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries
Extraction of Conditional and Causal Sentences from Queries to Provide a Flexible Answer
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Dually structured concepts in the semantic web: answer set programming approach
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
University of hagen at CLEF 2004: indexing and translating concepts for the GIRT task
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Question answering using sentence parsing and semantic network matching
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
| Hi-index | 0.00 |
Multilayered Extended Semantic Networks (abbreviated: MultiNet) are one of the few knowledge representation paradigms along the line of Semantic Networks (abbreviated: SN) with a comprehensive, systematic, and publicly available documentation. In contrast to logically oriented meaning representation systems with their extensional interpretation, MultiNet is based on a use-theoretic operational semantics. MultiNet is distinguished from the afore-mentioned systems by fulfilling the criteria of homogeneity and cognitive adequacy. The paper describes the main features of MultiNet and the standard repertoire of representational means provided by this system. Besides of the structural information, which is manifested in the relational and functional connections between nodes of the semantic network, the conceptual representatives of MultiNet are characterized by embedding the nodes of the network into a multidimensional space of layer attributes. To warrant cognitive adequacy and universality of the knowledge representation system, every node of the SN uniquely represents a concept, while the relations between them have to be expressed by a predefined set of about 110 semantic primitive relations and functions. The knowledge representation language MultiNet has been used as an interface in several natural language processing systems. It is also suitable as an interlingua for machine translation systems.