Knowledge Management and Acquisition for Intelligent Systems PDF
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Kenichi Yoshida Maria Lee (Eds.) Knowledge Management 6 1 0 and Acquisition 1 1 I A for Intelligent Systems N L 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 LibraryofCongressControlNumber:2018949383 LNCSSublibrary:SL7–ArtificialIntelligence ©SpringerNatureSwitzerlandAG2018 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbookare believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictionalclaimsin publishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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
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