Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Knowledge compilation and theory approximation

Abstract: Computational efficiency is a central concern in the design of knowledge representation systems In order to obtain efficient systems, it has been suggested that one should limit the form of the statements in the knowledge base or use an incomplete inference mechanism The former approach is often too restrictive for practical applications, whereas the latter leads to uncertainty about exactly what can and cannot be inferred from the knowledge base We present a third alternative, in which knowledge given in a general representation language is translated (compiled) into a tractable form—allowing for efficient subsequent query answeringWe show how propositional logical theories can be compiled into Horn theories that approximate the original information The approximations bound the original theory from below and above in terms of logical strength The procedures are extended to other tractable languages (for example, binary clauses) and to the first-order case Finally, we demonstrate the generality of our approach by compiling concept descriptions in a general frame-based language into a tractable form

Dynamic Ontologies on the Web

Abstract: We discuss the problems associated with managing ontologies in distributed environments such as the Web. The Web poses unique problems for the use of ontologies because of the rapid evolution and autonomy of web sites. We present SHOE, a web-based knowledge representation language that supports multiple versions of ontologies. We describe SHOE in the terms of a logic that separates data from ontologies and allows ontologies to provide different perspectives on the data. We then discuss the features of SHOE that address ontology versioning, the effects of ontology revision on SHOE web pages, and methods for implementing ontology integration using SHOE’s extension and version mechanisms.

SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings

Abstract: With the recent development of deep learning, research in AI has gained new vigor and prominence. While machine learning has succeeded in revitalizing many research fields, such as computer vision, speech recognition, and medical diagnosis, we are yet to witness impressive progress in natural language understanding. One of the reasons behind this unmatched expectation is that, while a bottom-up approach is feasible for pattern recognition, reasoning and understanding often require a top-down approach. In this work, we couple sub-symbolic and symbolic AI to automatically discover conceptual primitives from text and link them to commonsense concepts and named entities in a new three-level knowledge representation for sentiment analysis. In particular, we employ recurrent neural networks to infer primitives by lexical substitution and use them for grounding common and commonsense knowledge by means of multi-dimensional scaling.