Alexandre Miguel Pinto · Jorge Cardoso e n Guest Editors i l b u S l a n r u o J Transactions on 0 9 1 0 Computational 1 S C Collective Intelligence XXVI N L Ngoc Thanh Nguyen · Ryszard Kowalczyk Editors-in-Chief 123 Lecture Notes in Computer Science 10190 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zurich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany More information about this series at http://www.springer.com/series/8851 Ngoc Thanh Nguyen Ryszard Kowalczyk (cid:129) Alexandre Miguel Pinto Jorge Cardoso (Eds.) (cid:129) Transactions on Computational Collective Intelligence XXVI 123 Editors-in-Chief Ngoc ThanhNguyen Ryszard Kowalczyk Institute of Informatics Swinburne University of Technology Wroclaw University of Technology Hawthorn, SA Wroclaw Australia Poland GuestEditors Alexandre Miguel Pinto Jorge Cardoso University of Lisbon HuaweiGerman Research Center Lisbon Munich Portugal Germany and University of Coimbra Coimbra Portugal ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Computer Science ISSN 2190-9288 ISSN 2511-6053 (electronic) Transactions onComputational Collective Intelligence ISBN 978-3-319-59267-1 ISBN978-3-319-59268-8 (eBook) DOI 10.1007/978-3-319-59268-8 LibraryofCongressControlNumber:2017942988 ©SpringerInternationalPublishingAG2017 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. 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Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Transactions on Computational Collective Intelligence XXVI Preface It is our pleasure to present the XXVI volume of the LNCS Transactions on Com- putational Collective Intelligence. This Special Issue is the compilation of selected papersoftheFirstInternationalKEYSTONEConference2015(IKC2015),partofthe Keystone COST Action IC1302 (www.keystone-cost.eu). COST (European Coopera- tion in Science and Technology – www.cost.eu) is a pan-European intergovernmental framework. Its mission is to enable breakthrough scientific and technological devel- opmentsleadingtonewconceptsandproductsandtherebycontributetostrengthening Europe’s research and innovation capacities. It allows researchers, engineers, and scholars to jointly develop their own ideas and take new initiatives across all fields of science and technology, while promoting multi- and interdisciplinary approaches. COSTaimsatfosteringabetterintegrationofcountriesthatarelessresearch-intensive to the knowledge hubs of the European research area. The COST Association, an international not-for-profit association under the Belgian law, integrates all manage- ment, governing, and administrative functions necessary for the operation of the framework. The COST Association currently has 36 member countries. This volume collects and analyzes the main results achieved by the research areas covered by KEYSTONE (the Action: Semantic Keyword-Based Search on Structured data Sources). For Action members, the conference was also the place to discuss the resultsobtainedduringthefirsttwoyearsofactivities.TheresearchthemeofIKC2015 was“Keyword-SearchonMassiveDatasets.”Itisanemergingandchallengingtheme. In particular, since large-scale data sources usually comprise very large schemas and billionsofinstances,keywordsearchoversuchdatasetsfaceseveralchallengesrelated to scalability and interpretation of the keyword query intended meaning. Whereas state-of-the-art keyword search techniques work well for small or medium-size data- bases in a particular domain, many of them fail to scale on heterogeneous databases thatarecomposedofthousandsofinstances.Thediscoveryofsemanticallyrelateddata sources is another critical issue, hindered by the lack of sufficient information on availabledatasetsandendpoints.Browsingandsearchingfordataatthisscaleisnotan easy task for users. Semantic search can support the process aiming at leveraging semantics to improve the accuracy and recall of search mechanisms. This volume inaugurates the year 2017, the seventh year of TCCI activities. In the past 25 issues, we have published 253 high-quality papers. This issue contains 10 papers. In the first paper “Professional Collaborative Information Seeking: Towards Traceable Search and Creative Sensemaking,” Andreas Nuernberger et al. propose an VI Transactions onComputational Collective Intelligence XXVI adapted model for professional collaborative information seeking. The authors also introduce a system that has been specifically developed to support collaborative technology search. The second paper entitled “Exploiting Linguistic Analysis on URLs for Recom- mending Web Pages: A Comparative Study” by Sara Cadegnani et al. analyzes and comparesthreedifferentapproachestoleverageinformationembeddedinthestructure ofwebsitesandthelogsoftheirwebserverstoimprovetheeffectivenessofwebpage recommendation. Their proposals exploit the context of users’ navigations, i.e., their currentsessionswhensurfingaspecificwebsite.Theseapproachesdonotrequireeither information about the personal preferences of the users to be stored and processed or complex structures to be created and maintained. In the third paper, “Large-Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest” by Michael Cochez et al., the authors investigate the use of LSH Forest (a self-tuning indexing schema based on locality-sensitivehashing)forsolvingtheproblemofplacingnewknowledgetokensin the right contexts of the environment. They argue and show experimentally that LSH Forestpossessestherequiredpropertiesandcouldbeusedforlargedistributedset-ups. Further, they show experimentally that for their type of data minhashing works better than random hyperplane hashing. The fourth paper, “Keyword-Based Search of Workflow Fragments and Their Composition”byKhalidBelhajjameetal.,presentsamethodforidentifyingfragments that are frequently used across workflows in existing repositories, and therefore are likely to incarnate patterns that can be reused in new workflows. They present a keyword-based search method for identifying the fragments that are relevant for the needsofagivenworkflowdesigner.Theygoontopresentanalgorithmforcomposing the retrieved fragments with the initial (incomplete) workflow that the user designed based on compatibility rules that they identified, and showcase how the algorithm operates using an example from eScience. Thefifthpaper,entitled“ScientificFootprintsinDigitalLibraries”byClaudiaIfrim etal.,analyzescitationliststonotonlyquantifybutalsounderstandimpactbytracing the “footprints” that authors have left, i.e., the specific areas in which they have made animpact.Theyusethepublicationmedium(specificjournalorconference)toidentify the thematic scope of each paper and feed from existing digital libraries that index scientificactivity,namely,GoogleScholarandDBLP.Thisallowsthemtodesignand develop a system, the Footprint Analyzer, which can be used to successfully identify the most prominent works and authors for each scientific field, regardless of whether their own research is limited to or even focused on the specific field. Various real-life examplesdemonstratetheproposedconcepts,andresultsfromthedevelopedsystem’s operation prove the applicability and validity. Inthesixthpapertitled“MiningandUsingKey-wordsandKey-phrasestoIdentify theEraofanAnonymousText,”DrorMughazetal.determinethetimeframeinwhich theauthorofagivendocumentlived.Thedocumentsarerabbinicdocumentswrittenin Hebrew-Aramaic languages. The documents are undated and do not contain a Transactions onComputational Collective Intelligence XXVI VII bibliographic section, which constitutes a substantial challenge. The authors define a set of key phrases and formulate various types of rules – “Iron-clad,” Heuristic, and Greedy – to define the time frame. These rules were tested on two corpora containing response documents, and the results are promising. They are better for larger corpora than for smaller corpora. The next paper, “Toward Optimized Multimodal Concept Indexing” by Navid Rekabsaz et al., presents an approach for semantic-based keyword search and focuses especiallyonitsoptimizationtoscaletoreal-world-sizedcollectionsinthesocialmedia domain.Furthermore,thepaperpresentsafacetedindexingframeworkandarchitecture thatrelatescontenttosemanticconceptstobeindexedandsearchedsemantically.The authors study the use of textual concepts in a social media domain and observe a significant improvement from using a concept-based solution for keyword searching. In the eighth paper, entitled “Improving Document Retrieval in Large-Domain Specific Textual Databases Using Lexical Resources,” Ranka Stanković etal. propose the use of documentindexing as a possible solution to document representation. They use metadata for generating a bag of words for each document with the aid of mor- phologicaldictionariesandtransducers.Acombinationofseveraltf-idf-basedmeasures was applied for selecting and ranking of retrieval results of indexed documents for a specific query and the results were compared with the initial retrieval system that was alreadyinplace.Ingeneral,asignificantimprovementhasbeenachievedaccordingto the standard information retrieval performance measures, where the InQuery method performed the best. In the ninth paper, “Domain-Specific Modeling: A Food and Drink Gazetteer,” Andrey Tagarev et al. build a food and drink (FD) gazetteer for classification of general, FD-related concepts, efficient faceted search or automated semantic enrich- ment.Forgeneraldomains(suchastheFDdomain),re-usingencyclopedicknowledge bases like Wikipedia may be a good idea. The authors propose a semi-supervised approach that uses a restricted Wikipedia as a base for the modeling, achieved by selecting a domain-relevant Wikipedia category as root for the model and all its sub- categories, combined with expert and data-driven pruning of irrelevant categories. The last paper, “What’s New? Analyzing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval” authored by Yiwei Zhou et al., focuses on the problem of creating language-specific entity contexts to support entity-centric, language-specific information retrieval applications. First, they discuss alternative ways such contexts can be built, including graph-based and article-based approaches. Second, they analyze the similarities and the differences in these contexts inacasestudyincluding220entitiesandfiveWikipedialanguageeditions.Third,they propose a context-based entity-centric information retrieval model that maps docu- ments to aspect space, and apply language-specific entity contexts to perform query expansion.Last,theyperformacasestudytodemonstratetheimpactofthismodelina newsretrievalapplication.Thestudyillustratesthattheproposedmodelcaneffectively improve the recall of entity-centric information retrieval while keeping high precision and can provide language-specific results. VIII Transactions onComputational Collective Intelligence XXVI We would like to thank all the authors for their valuable contributions to this issue and all the reviewers for their opinions, which contributed greatly to the high quality of the papers. Our special thanks go to the team at Springer, who have helped to publish the many TCCI issues in due time and in good order. February 2017 Alexandre Miguel Pinto Jorge Cardoso Transactions on Computational Collective Intelligence ThisSpringerjournalfocusesonresearchincomputer-basedmethodsofcomputational collectiveintelligence(CCI)andtheirapplicationsinawiderangeoffieldssuchasthe Semantic Web, social networks, and multi-agent systems. It aims to provide a forum forthepresentationofscientificresearchandtechnologicalachievementsaccomplished by the international community. The topics addressed by this journal include all solutions to real-life problems for which it is necessary to use computational collective intelligence technologies to achieveeffectiveresults.Theemphasisofthepaperspublishedisonnovelandoriginal research and technological advancements. Special features on specific topics are welcome. Editor-in-Chief Ngoc Thanh Nguyen Wroclaw University of Technology, Poland Co-Editor-in-Chief Ryszard Kowalczyk Swinburne University of Technology, Australia Editorial Board John Breslin National University of Ireland, Galway, Ireland Longbing Cao University of Technology Sydney, Australia Shi-Kuo Chang University of Pittsburgh, USA Oscar Cordon European Centre for Soft Computing, Spain Tzung-Pei Hong National University of Kaohsiung, Taiwan Francesco Guerra University of Modena and Reggio Emilia, Italy Gordan Jezic University of Zagreb, Croatia Piotr Jędrzejowicz Gdynia Maritime University, Poland Kang-Huyn Jo University of Ulsan, South Korea Yiannis Kompatsiaris Centre for Research and Technology Hellas, Greece Jozef Korbicz University of Zielona Gora, Poland Hoai An Le Thi Metz University, France Pierre Lévy University of Ottawa, Canada Tokuro Matsuo Yamagata University, Japan Kazumi Nakamatsu University of Hyogo, Japan Toyoaki Nishida Kyoto University, Japan Manuel Núñez Universidad Complutense de Madrid, Spain Julian Padget University of Bath, UK Witold Pedrycz University of Alberta, Canada