國立虎尾科技大學 |

Knowledge Authoring With Factual English, Rules, and Actions.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Knowledge Authoring With Factual English, Rules, and Actions./
Author:	Wang, Yuheng.
Description:	1 online resource (99 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
Contained By:	Dissertations Abstracts International85-07A.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798381400052

Knowledge Authoring With Factual English, Rules, and Actions.
Wang, Yuheng.

Knowledge Authoring With Factual English, Rules, and Actions. - 1 online resource (99 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: A.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2023.

Includes bibliographical references

Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricted grammars of the language (so-called controlled natural languages, or CNL) are hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. Besides the CNL restrictions, KALM has limitations in terms of the types of knowledge it can represent. For example, KALM users cannot author rules to support multi-step reasoning, nor can they author actions associated with occurrences of events, which hinders its ability to do time-related reasoning. Apart from the aforementioned shortcomings, the system's speed was insufficient to adequately support the overall knowledge authoring process.To address these issues, we propose an extension of KALM called KALM for Factual Language (KALMFL). KALMFL uses a neural parser for natural language, mStanza, to parse what we call factual English sentences, which require little grammar training to use. Building upon KALMFL, we propose KALM for Rules and Actions (KALMRA), to represent and reason with rules and actions. Furthermore, we identify the reasons behind the slow speed of KALM and make optimizations to address this issue. Our evaluation using multiple benchmarks shows that our approaches achieve a high level of correctness on fact and query authoring (95%) and on rule authoring (100%). When used for authoring and reasoning with actions, our approach achieves more than 99.3% correctness, demonstrating its effectiveness in enabling more sophisticated knowledge representation and reasoning. We also illustrate the logical reasoning capabilities of our approach by drawing attention to the problems faced by the famous AI, ChatGPT. Finally, the evaluation of the newly proposed speed optimization points not only to a 68% runtime improvement but also yields better accuracy of the overall system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381400052Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Logic programmingIndex Terms--Genre/Form:

554714
Electronic books.

Knowledge Authoring With Factual English, Rules, and Actions.
LDR:04303ntm a22004217 4500 001 1147311
005 20240909100753.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381400052
035 $a (MiAaPQ)AAI30695759
035 $a AAI30695759
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Wang, Yuheng. $3 1473009
245 1 0 $a Knowledge Authoring With Factual English, Rules, and Actions.
264 0 $c 2023
300 $a 1 online resource (99 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
500 $a Advisor: Kifer, Michael;Fodor, Pual.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2023.
504 $a Includes bibliographical references
520 $a Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricted grammars of the language (so-called controlled natural languages, or CNL) are hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. Besides the CNL restrictions, KALM has limitations in terms of the types of knowledge it can represent. For example, KALM users cannot author rules to support multi-step reasoning, nor can they author actions associated with occurrences of events, which hinders its ability to do time-related reasoning. Apart from the aforementioned shortcomings, the system's speed was insufficient to adequately support the overall knowledge authoring process.To address these issues, we propose an extension of KALM called KALM for Factual Language (KALMFL). KALMFL uses a neural parser for natural language, mStanza, to parse what we call factual English sentences, which require little grammar training to use. Building upon KALMFL, we propose KALM for Rules and Actions (KALMRA), to represent and reason with rules and actions. Furthermore, we identify the reasons behind the slow speed of KALM and make optimizations to address this issue. Our evaluation using multiple benchmarks shows that our approaches achieve a high level of correctness on fact and query authoring (95%) and on rule authoring (100%). When used for authoring and reasoning with actions, our approach achieves more than 99.3% correctness, demonstrating its effectiveness in enabling more sophisticated knowledge representation and reasoning. We also illustrate the logical reasoning capabilities of our approach by drawing attention to the problems faced by the famous AI, ChatGPT. Finally, the evaluation of the newly proposed speed optimization points not only to a 68% runtime improvement but also yields better accuracy of the overall system.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Computer engineering. $3 569006
650 4 $a Information technology. $3 559429
650 4 $a Logic. $3 558909
650 4 $a Information science. $3 561178
653 $a Logic programming
653 $a Natural language processing
653 $a Natural language understanding
653 $a Question answering
653 $a Industrial enterprises
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0489
690 $a 0464
690 $a 0723
690 $a 0395
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a State University of New York at Stony Brook. $b Computer Science. $3 1180378
773 0 $t Dissertations Abstracts International $g 85-07A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30695759 $z click for full text (PQDT)