Cognitively-inspired symbolic framework for knowledge representation

S. Savic, M. Gnjatović, D. Mišković, Jovica Tasevski, N. Maček
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引用次数: 7

Abstract

This paper introduces a cognitively-inspired symbolic framework for knowledge representation in human-machine interaction. The framework is developed within the ongoing research on a computational model of a hierarchical associative long-term memory. The model integrates neurocognitive understanding of the human memory system with selected insights from linguistics, and primarily addresses the storage aspect of the long-term memory. The proposed memory structure is conceptualized as a set of (multisource-multisink) semantic flow networks, including knowledge units of different complexity. It also provides algorithm for semantic integration and associative learning. The model is illustrated for a dedicated interaction domain, and implemented within a prototype system.
知识表示的认知启发符号框架
本文提出了一种基于认知启发的人机交互知识表示符号框架。该框架是在正在进行的分层联想长期记忆计算模型研究中开发的。该模型将人类记忆系统的神经认知理解与语言学的精选见解相结合,主要解决长期记忆的存储方面的问题。所提出的记忆结构被概念化为一组(多源-多汇)语义流网络,包括不同复杂性的知识单元。它还提供了语义整合和联想学习算法。该模型用于一个专用的交互领域,并在原型系统中实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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