Knowledge Management and Acquisition for Intelligent Systems

291 Pages·2018·22.89 MB·English

by  Kenichi Yoshida

Kenichi Yoshida
Maria Lee (Eds.)
Knowledge Management
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0 and Acquisition
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A for Intelligent Systems
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15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018
Nanjing, China, August 28–29, 2018
Proceedings
123
fi
Lecture Notes in Arti cial Intelligence 11016
Subseries of Lecture Notes in Computer Science
LNAI Series Editors
Randy Goebel
University of Alberta, Edmonton, Canada
Yuzuru Tanaka
Hokkaido University, Sapporo, Japan
Wolfgang Wahlster
DFKI and Saarland University, Saarbrücken, Germany
LNAI Founding Series Editor
Joerg Siekmann
DFKI and Saarland University, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/1244
Kenichi Yoshida Maria Lee (Eds.)
(cid:129)
Knowledge Management
and Acquisition
for Intelligent Systems
15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018
Nanjing, China, August 28–29, 2018
Proceedings
123
Editors
Kenichi Yoshida Maria Lee
University of Tsukuba ShihChienUniversity
Tokyo TaipeiCity
Japan Taiwan
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Artificial Intelligence
ISBN 978-3-319-97288-6 ISBN978-3-319-97289-3 (eBook)
https://doi.org/10.1007/978-3-319-97289-3
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LNCSSublibrary:SL7–ArtificialIntelligence
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Preface
Thisvolumecontainsthepaperspresentedatthe2018PacificRimKnowledgeAcqui-
sitionWorkshop(PKAW2018)heldinconjunctionwiththe15thPacificRimInterna-
tionalConferenceonArtificialIntelligence(PRICAI2018),duringAugust28–29,2018
inNanjing,China.
Artificial intelligence (AI) research has evolved over the past few decades and
knowledge acquisition research is one of the cores of AI research. Three international
knowledge acquisition workshops have been held in the Pacific Rim, Canada, and
Europe since the 1990s. Over the years, AI and knowledge acquisition have adopted
manytechnologies andflourished.Industriesaidedinthiswayhaveprosperedandwe
hope they will continue to thrive. We invited the PKAW co-founders and honorary
chairs, Prof. Paul Compton and Prof. Hiroshi Motoda, to share and provide visionary
talks on “PKAW: From Past to Future” and “Social Network as a Rich Source of
Human Behavior” at PKAW 2018.
PKAW2018hadastrongemphasisonincrementalknowledgeacquisition,machine
learning,deeplearning,socialnetworkanalysis,bigdata,datamining,andagents.The
proceedingscontain15regularpapersandsevenshortpapersthatwereselectedbythe
Program Committee among 51 submitted papers. All papers were peer-reviewed by
threereviewers.Thepapersintheseproceedingscoverthemethodsandtoolsaswellas
theapplicationsrelatedtodevelopingaknowledgebase,healthcare,financialsystems,
and intelligent systems.
The workshop co-chairs would like to thank all those who contributed to PKAW
2018, including the PKAW Program Committee and reviewers for their support and
timelyreviewofpapers,andthePRICAIOrganizingCommitteeforhandlingallofthe
administrative and local matters. Thanks to EasyChair for streamlining the whole
process of producing this volume and Springer for publishing the proceedings in the
Lecture Note in Artificial Intelligence (LNAI) series. Particular thanks to those who
submitted papers, presented, and attended the workshop. We look forward to seeing
you at PKAW 2020.
August 2018 Kenichi Yoshida
Maria Lee
Organization
Program Committee
Nathalie Aussenac-Gilles IRIT, CNRS and University of Toulouse, France
Quan Bai Auckland University of Technology, New Zealand
Ghassan Beydoun University of Technology, Sydney, Australia
Xiongcai Cai The University of New South Wales, Australia
Tsung Teng Chen National Taipei University, Taiwan
Jérôme David Inria, France
Akihiro Inokuchi Kwansei Gakuin University, Japan
Toshihiro Kamishima National Institute of Advanced Industrial Science
and Technology (AIST), Japan
Byeong-Ho Kang University of Tasmania, Australia
Mihye Kim Catholic University of Daegu, South Korea
Yang Sok Kim Keimyung University, South Korea
Alfred Krzywicki The University of New South Wales, Australia
Maria Lee Shin Chien University, Taiwan
Kyongho Min The University of New South Wales, Australia
Toshiro Minami Kyushu Institute of Information Sciences and Kyushu
University Library, Japan
Luke Mirowski University of Tasmania, Australia
Tsuyoshi Murata Tokyo Institute of Technology, Japan
Kouzou Ohara Aoyama Gakuin University, Japan
Hayato Ohwada Tokyo University of Science, Japan
Tomonobu Ozaki Nihon University, Japan
Hye-Young Paik The University of New South Wales, Australia
Ulrich Reimer UniversityofAppliedSciencesSt.Gallen,Switzerland
Deborah Richards Macquarie University, Australia
Kazumi Saito University of Shizuoka, Japan
Derek Sleeman University of Aberdeen, UK
Vojtĕch Svátek University of Economics, Prague, Czech Republic
Takao Terano National Institute of Advanced Institute of Science
and Technology (AIST), Japan
Hiroshi Uehara Akita Prefectural University, Japan
Shuxiang Xu University of Tasmania, Australia
Takahira Yamaguchi Keio University, Japan
Kenichi Yoshida University of Tsukuba, Japan
Tetsuya Yoshida Nara Women’s University, Japan
Contents
Building a Commonsense Knowledge Base for a Collaborative
Storytelling Agent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Dionne Tiffany Ong, Christine Rachel De Jesus,
Luisa Katherine Gilig, Junlyn Bryan Alburo, and Ethel Ong
AKnowledge Acquisition Method for Event Extraction and Coding Based
on Deep Patterns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Alfred Krzywicki, Wayne Wobcke, Michael Bain, Susanne Schmeidl,
and Bradford Heap
Incremental Acquisition of Values to Deal with Cybersecurity
Ethical Dilemmas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Deborah Richards, Virginia Dignum, Malcolm Ryan,
and Michael Hitchens
Towards Realtime Adaptation: Uncovering User Models
from Experimental Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Deborah Richards, Ayse Aysin Bilgin, Hedieh Ranjbartabar,
and Anupam Makhija
Supporting Relevance Feedback with Concept Learning for Semantic
Information Retrieval in Large OWL Knowledge Base . . . . . . . . . . . . . . . . 61
Liu Yuan
Combining Concept Learning and Probabilistic Information Retrieval
Model to Understand User’s Searching Intent in OWL Knowledge Base . . . . 76
Liu Yuan
Diabetic Retinopathy Classification Using C4.5 . . . . . . . . . . . . . . . . . . . . . 90
Mira Park and Peter Summons
StockPriceMovementPredictionfromFinancialNewswithDeepLearning
and Knowledge Graph Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
Yang Liu, Qingguo Zeng, Huanrui Yang, and Adrian Carrio
Sample Dropout for Audio Scene Classification Using Multi-scale Dense
Connected Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . 114
Dawei Feng, Kele Xu, Haibo Mi, Feifan Liao, and Yan Zhou
LOUGA: Learning Planning Operators Using Genetic Algorithms. . . . . . . . . 124
Jiří Kučera and Roman Barták
X Contents
k-NN Based Forecast of Short-Term Foreign Exchange Rates. . . . . . . . . . . . 139
Haruya Umemoto, Tetsuya Toyota, and Kouzou Ohara
Multi-dimensional Banded Pattern Mining . . . . . . . . . . . . . . . . . . . . . . . . . 154
Fatimah B. Abdullahi and Frans Coenen
Automated Business Process Discovery and Analysis for the International
Higher Education Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Juan Gonzalez-Dominguez and Peter Busch
An Analysis of Interaction Between Users and Open Government Data
Portals in Data Acquisition Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
Di Wang, Deborah Richards, and Chuanfu Chen
Blockchain: Trends and Future. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Wenli Yang, Saurabh Garg, Ali Raza, David Herbert,
and Byeong Kang
Selective Comprehension for Referring Expression by Prebuilt Entity
Dictionary with Modular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Enjie Cui, Jianming Wang, Jiayu Liang, and Guanghao Jin
Pose Specification Based Online Person Identification. . . . . . . . . . . . . . . . . 221
Tao Guo, Jianming Wang, Rize Jin, and Guanghao Jin
Get the Whole Action Event by Action Stage Classification. . . . . . . . . . . . . 231
Weiqi Li, Jianming Wang, Shengbei Wang, and Guanghao Jin
Clothing Attribute Extraction Using Convolutional Neural Networks. . . . . . . 241
Wonseok Lee, Sangmin Jo, Heejun Lee, Jungmin Kim, Meejin Noh,
and Yang Sok Kim
Research Paper Recommender Systems on Big Scholarly Data. . . . . . . . . . . 251
Tsung Teng Chen and Maria Lee
Classification of CSR Using Latent Dirichlet Allocation and Analysis
of the Relationship Between CSR and Corporate Value. . . . . . . . . . . . . . . . 261
Kazuya Uekado, Ling Feng, Masaaki Suzuki, and Hayato Ohwada
AWeightedSimilarityMeasureBasedonMetaStructureinHeterogeneous
Information Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
Zhaochen Li and Hengliang Wang
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
Building a Commonsense Knowledge Base
for a Collaborative Storytelling Agent
Dionne Tiffany Ong, Christine Rachel De Jesus,
Luisa Katherine Gilig, Junlyn Bryan Alburo, and Ethel Ong(&)
De LaSalle University, Manila,Philippines
{dionne_ong,christine_dejesus,ethel.ong}@dlsu.edu.ph
Abstract. Storytellingisacommonactivitythatpeopleengageintoshareand
exchange information about their everyday life events. Children in particular
findstorytellingentertainingastheylearnabouttheirworldandevensharetheir
ownstories.Virtualagentsaregainingpopularityasconversationalagentswho
canengagewith theirhumanusersinadialogue toanswerqueriesandtofind
the necessary support in the performance of some tasks in a particular appli-
cation domain. For virtual agents to be able to share stories with their human
users,speciallythechildren,theyneedtobeprovidedwithabodyofknowledge
touseintheirdialogue.Inthispaper,wedescribeourapproachinbuildingan
ontology to provide a knowledge base of commonsense concepts and their
relationstoavirtualagent.Thisisthenusedbyavirtualagenttoprocessuser
inputas aformof storytext,andto generateappropriateresponses inorderto
encourage the childto sharehis/her story.
(cid:1) (cid:1)
Keywords: Commonsenseontology Virtual agent Storytelling
Dialogue
1 Introduction
Stories are part of our everyday lives. We use stories tocommunicate our experiences
andtoempathizewithothers.Asauniversalhabit[1],storytellingsessionsareusedin
educating positive attitudes such as respect and openness [2]. When shared orally,
stories are also effective in enhancing different communication skills such as articu-
lation, enunciation, and vocabulary as reported in the study of [3].
Storiesareusuallysharedwithanaudiencewhospinstheirownunderstandingonto
astory,whetheroutloudornot,thusbecomingpartofthestorytellingprocess[4].This
is evident even with young children, who may be silent at the onset of the activity
before turning into storytellers themselves by sharing their own personal recollections
and interpretations with the main storyteller [5]. When encountered by situations such
as writer’s block, which is a common occurrence in the production of any creative
artifact [6], storytellers often find themselves soliciting ideas from their audience. As
the questions, interpretations and feedback are raised, the story evolves and becomes
complete through this exchange of viewpoints among the collaborators.
In the field of computing, researchers in human-computer collaboration are
exploring various approaches in modelling collaboration and designing collaborative
©SpringerNatureSwitzerlandAG2018
K.YoshidaandM.Lee(Eds.):PKAW2018,LNAI11016,pp.1–15,2018.
https://doi.org/10.1007/978-3-319-97289-3_1