LEADER 09354nam 22005895 450 001 996466176203316 005 20240110003801.0 010 $a3-319-59268-8 024 7 $a10.1007/978-3-319-59268-8 035 $a(CKB)4340000000061543 035 $a(DE-He213)978-3-319-59268-8 035 $a(MiAaPQ)EBC6283546 035 $a(MiAaPQ)EBC5591592 035 $a(Au-PeEL)EBL5591592 035 $a(OCoLC)990344644 035 $a(PPN)202990656 035 $a(EXLCZ)994340000000061543 100 $a20170614d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTransactions on Computational Collective Intelligence XXVI$b[electronic resource] /$fedited by Ngoc Thanh Nguyen, Ryszard Kowalczyk, Alexandre Miguel Pinto, Jorge Cardoso 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XI, 233 p. 92 illus.) 225 1 $aTransactions on Computational Collective Intelligence,$x2190-9288 ;$v10190 300 $aIncludes index. 311 $a3-319-59267-X 327 $aIntro -- Transactions on Computational Collective Intelligence XXVI -- Preface -- Transactions on Computational Collective Intelligence -- Contents -- Professional Collaborative Information Seeking: Towards Traceable Search and Creative Sensemaking -- 1 Introduction -- 2 Review on Related Work -- 2.1 Information Behavior and Seeking Models -- 2.2 Collaborative Information Seeking -- 2.3 Professionals as Information Users -- 3 Towards an Integrated Model for Collaborative Professional Search -- 3.1 Requirements -- 3.2 Challenges -- 4 Requirements Analysis: A Case Study in Technology Scouting -- 4.1 Use Case in Technology Scouting -- 4.2 User Survey -- 5 Professional Information Seeking Support for Technology Scouting -- 5.1 Traceable Collaborative Search -- 5.2 Creativity-Focused Sensemaking -- 5.3 Conclusion -- 6 Summary -- References -- Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study -- 1 Introduction -- 2 Related Work -- 3 Study of Techniques for Recommending Web Pages -- 3.1 Representation of the User Context and the Website -- 3.2 Methods -- 4 Experimental Evaluation -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Results of the Experiments -- 4.4 Using Statistical and Network Analysis for Improving the Accuracy -- 4.5 Further Improvements Using Lexical and Semantic Analysis -- 5 Conclusions and Future Work -- References -- Large Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest -- 1 Introduction -- 2 Big Knowledge---Evolving Knowledge Ecosystems -- 2.1 Efficiency Versus Effectiveness -- 2.2 Evolving Knowledge Ecosystems -- 3 Locality-Sensitive Hashing -- 3.1 LSH Forest -- 3.2 Sowing Knowledge Tokens Using LSH Forest -- 3.3 Distance Metrics and Locality-Sensitive Hash Functions -- 4 Evaluation -- 4.1 Single Data Source---Single Tree. 327 $a4.2 Connecting Knowledge Tokens Using LSH Forest, i.e. Matching -- 4.3 Adding Dynamics -- 5 Results -- 5.1 Single Data Source---Single Tree -- 5.2 Connecting Knowledge Tokens Using LSH Forest, i.e. Matching -- 5.3 Adding Dynamics -- 6 Conclusions and Outlook -- References -- Keyword-Based Search of Workflow Fragments and Their Composition -- 1 Introduction -- 2 Approach Overview -- 3 Related Work -- 4 Mining Frequent Workflow Fragments -- 4.1 Homogenizing Activity Labels -- 4.2 Workflow Encoding -- 4.3 Empirical Evaluation -- 5 Keyword-Based Search of Frequent Workflow Fragments -- 6 Composing Workflow Fragments -- 7 Example from eScience -- 8 Conclusion -- References -- Scientific Footprints in Digital Libraries -- 1 Introduction -- 2 Scientific Impact -- 2.1 Paper Impact -- 2.2 Journal Impact -- 2.3 Author Impact -- 3 Digital Libraries -- 3.1 Google Scholar -- 3.2 DBLP -- 4 Citation Context -- 4.1 Role of Referenced Work -- 4.2 Scientific Scope -- 5 Scientific Footprints -- 5.1 Two Indicative Examples -- 5.2 Following the Footprints -- 6 Semantic Citation Analysis -- 6.1 Preparatory Steps -- 6.2 Processing Steps, for Each Work -- 6.3 Processing Steps, for Each Author -- 7 Integrated System -- 7.1 Architecture -- 7.2 Problems Encountered -- 7.3 Software Licensing -- 8 Experimental Results -- 9 Conclusions -- References -- Mining and Using Key-Words and Key-Phrases to Identify the Era of an Anonymous Text -- Abstract -- 1 Introduction -- 2 Related Research -- 3 Semi-automatic Boosting Mining of Key-Phrases -- 3.1 The Algorithm -- 3.2 Algorithm Results -- 4 Rules-Based Constraints -- 4.1 "Iron-Clad" and Heuristic Rules with Key-Phrases -- 4.2 Greedy Rules -- 4.3 Birth and Death Year Tuning -- 4.4 Example of the Use of a Certain Heuristic Rule and the Key-Phrase "Late" -- 5 The Model -- 6 Examined Corpus, Experiments and Results. 327 $a7 Summary, Conclusions and Future Work -- Appendix -- References -- Toward Optimized Multimodal Concept Indexing -- 1 Introduction -- 2 Related Work -- 3 Concept-Based Multimedia Retrieval -- 4 Application of Concept-Based Retrieval -- 4.1 Experiment Setup -- 4.2 Combinatorial Method -- 4.3 Greedy Method -- 5 Optimizing Semantic Text Similarity -- 5.1 Two-Phase Process -- 5.2 Approximate Nearest Neighborhood -- 6 Conclusions and Future Work -- References -- Improving Document Retrieval in Large Domain Specific Textual Databases Using Lexical Resources -- 1 Introduction -- 2 The Database of Geological Projects -- 2.1 Motivation -- 2.2 The Initial Solution for Document Retrieval -- 3 The Improved Solution -- 3.1 Used Resources -- 3.2 The Architecture of the New System -- 4 Evaluation -- 5 Conclusion and Future Work -- References -- Domain-Specific Modeling: A Food and Drink Gazetteer -- 1 Introduction -- 1.1 Related Work -- 2 Europeana Food and Drink -- 2.1 Wikipedia Categories Related to FD -- 3 A Method for Domain-Specific Modeling -- 3.1 Wikipedia Categories -- 3.2 Method Overview -- 4 Properties of the FD Classification Hierarchy -- 4.1 Reasons for Irrelevant Inclusions -- 5 Top-Down Expert Pruning -- 6 Bottom-Up Data-Driven Enrichment -- 6.1 Mapping the Horniman Thesaurus to Wikipedia Articles -- 6.2 Scoring FD Categories w.r.t. Mapped Horniman Concepts -- 6.3 Other CHO Collections: Alinari, TopFoto and Wolverhampton -- 7 A Food and Drink Statistical Classifier -- 8 Food and Drink Modeling for Other Languages - Outlook -- 8.1 Top-Down Category Harvesting from FD Root -- 8.2 Article-Driven Reconstruction Approach -- 9 Evaluation -- 9.1 Method -- 9.2 Results -- 10 Comments and Future Work -- References -- What's New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval -- 1 Introduction. 327 $a2 Creation of the Language-Specific Entity Context -- 2.1 Language-Specific Entity Context Definition -- 2.2 Context Similarity Measure -- 2.3 Article-Based Context Creation -- 2.4 Graph-Based Context Creation -- 3 News Retrieval Using Language-Specific Entity Context -- 3.1 Entity-Centric News Retrieval -- 3.2 Approach to Context-Based Information Retrieval -- 4 Entity Context Analysis -- 4.1 Dataset Description -- 4.2 Context Similarity Analysis -- 5 Language-Specific Retrieval of News Articles for Entity-Centric Queries -- 5.1 Dataset Description -- 5.2 Precision-Recall Analysis -- 5.3 Analysis of Language-Specific Results -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Author Index. 330 $aThese transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This twenty-sixth issue is a special issue with selected papers from the First International KEYSTONE Conference 2015 (IKC 2015), part of the keystone COST Action IC1302. 410 0$aTransactions on Computational Collective Intelligence,$x2190-9288 ;$v10190 606 $aArtificial intelligence 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.3 702 $aNguyen$b Ngoc Thanh$c(Computer scientist).$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKowalczyk$b Ryszard$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPinto$b Alexandre Miguel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCardoso$b Jorge$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466176203316 996 $aTransactions on Computational Collective Intelligence XXVI$92829816 997 $aUNISA