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By Achim Hoffmann, Byeong-ho Kang, Debbie Richards, Shusaku Tsumoto

This publication constitutes the completely refereed post-proceedings of the 2006 Pacific Rim wisdom Acquisition Workshop, PKAW 2006, held in Guilin, China in August 2006 as a part of ninth Pacific Rim foreign convention on synthetic Intelligence, PRICAI 2006.

The 21 revised complete papers and six revised brief papers awarded including four invited talks have been conscientiously reviewed and chosen from eighty one submissions. The papers are geared up in topical sections on ontology and data acquisition, set of rules techniques to wisdom acquisition, incremental wisdom acquisition and RDR, wisdom acquisition and purposes, in addition to computer studying and knowledge mining.

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Additional info for Advances in Knowledge Acquisition and Management: Pacific Rim Knowledge Acquisition Workshop, PKAW 2006, Guilin, China, August 7-8, 2006, Revised Selected

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G. g. “km”, “m”). For example, the lexical information for “km” is (:POS (Part of Speech) LU (Length Unit)) with its meaning KILOMETER. The system uses 64 context-free rules to represent the structural form of affixed numeral strings. g. a character or a few characters and a number) produced by morphological analysis of the affixed string. g. DATE Æ (DAY Heuristic and Rule-Based Knowledge Acquisition 43 DOT MONTH DOT YEAR), Constraints: ((LEAPDATEP DAY MONTH YEAR))). The interpretation rules are discussed in the next section in detail.

E. product or outcome) of an activity being executed by some cause or agent. It indicates the achievement of a certain environmental state communicated through an event. An effect annotation relates a specific result or outcome to an activity on a business process model. It explicitly states a result of the activity if the conceptual model were to be hypothetically executed. e. the process activity causes the effect to occur). An activity can cause many effects and an effect can be caused by a number of activities.

In this paper, the different types of rule needed to classify numeral strings are described in detail. In the next section, the categories and rules used in this system are described. In section 3, we describe the understanding process for both separate and affixed numeral strings in more detail, and focus on classification rules. Section 4 describes preliminary experimental results obtained with this approach, and discussion and conclusions follow. e. context-free rules used for affixed numeral strings) used to parse numeral strings in real text are described.

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