https://philpapers.org/archive/RAPLFF.pdf

Logical Foundations for Belief Representation

Belief reports can be interpreted de re or de dicto, and we investigate the disambiguation of belief reports as they appear in discourse and narrative. In earlier work, representations for de re and de dicto belief reports were presented, and the distinction between de re and de dicto belief reports was made solely on the basis of their representations. This analysis is sufficient only when belief reports are considered in isolation. We need to consider more complicated belief structures, in addition to those presented earlier, in order to sufficiently represent de re and de dicto belief reports as they appear in discourse and narrative. Further, we cannot meaningfully apply one, but not the other, of the concepts de re and de dicto to these more complicated belief structures. We argue that the concepts de re and de dicto do not apply to an agent's conceptual representation of her beliefs, but that they apply to the utterance of a belief report on a specific occasion. A cognitive agent interprets a belief report such as "S believes that N is F," or "S said, 'N is F'" (where S and N are names or descriptions, and F is an adjective) de dicto if she interprets it from N's perspective, and she interprets it de re if she interprets it from her own perspective.

Representing de re and de dicto belief reports in discourse and narrative

Users of information retrieval systems (IRS) know and use many relationships between concepts a long time before these find their way into textbooks, printed thesauri, or classification schemes. We present here an IRS component called TEGEN, which taps this expertise by automatically drawing conclusions from actual search behavior about possible thesaurus entries. This is done during an iterative knowledge acquisition process: only after explicit or implicit confirmation by other users of the IRS during the knowledge verification process, the results are incorporated into a thesaurus. TEGEN is written in PASCAL using a knowledge-based programming method. It uses the relational database system IMF2 and is implemented at the Technical University of Munich and at the Leibniz Computer Center of the Bavarian Academy of Sciences.

Automatic thesaurus construction by machine learning from retrieval sessions

Bioexpert. An expert system for wastewater treatment process diagnosis

Automatic Thesaurus Construction by Machine Learning from Retrieval Sessions.

In contrast to conventional database systems, AI systems require a knowledge base with diverse kinds of knowledge. These include, but are not limited to, knowledge about objects, knowledge about processes, and hard-to-represent commonsense knowledge about goals, motivation, causality, time, actions, etc. Attempts to represent this breadth of knowledge raise many questions: (1) How do we structure the explicit knowledge in a knowledge base? (2) How do we encode rules for manipulating a knowledge base's explicit knowledge to infer knowledge contained implicitly within the knowledge base? (3) When do we undertake and how do we control such inferences? (4) How do we formally specify the semantics of a knowledge base? (5) How do we deal with incomplete knowledge? (6) How do we extract the knowledge of an expert to initially "stock" the knowledge base? (7) How do we automatically acquire new knowledge as time goes on so that the knowledge base can be kept current? This special issue introduces this important area of artificial intelligence to a wider audience. The core of the 15 articles, contributed by a broad spectrum of researchers on various aspects of knowledge representation, show the importance, diversity, and vigor of knowledge representation as a research activity. This introduction provides some background and context to these articles by mapping out the basic approaches to knowledge representation that have developed over the years.

Guest Editors' Introduction: Approaches to Knowledge Representation

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Guest Editors' Introduction: Approaches to Knowledge Representation