Cluster analysis and ontology generation techniques for the development of scholarly semantic web

CLUSTER ANALYSIS AND ONTOLOGY GENERATION TECHNIQUES FOR THE DEVELOPMENT OF SCHOLARLY SEMANTIC WEB By Quan Thanh Tho SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY AT SCHOOL OF COMPUTER ENGINEERING NANYANG TECHNOLOGICAL UNIVERSITY NANYANG AVENUE, SINGAPORE 639798 2005 Table of Contents Table of Contents ii List of Tables viii List of Figures x Abstract xiv Acknowledgements xvi Introduction 1.1 Scholarly Web Information 1.2 Scholarly Information Retrieval 1.3 The Semantic Web 1.4 Semantic Web-based Retrieval Systems 1.5 Objectives 1.6 Major Contributions 1.7 Organization of the Thesis The Semantic Web 2.1 11 Markup Languages 11 2.1.1 Hypertext Markup Language 12 2.1.2 Extensible Markup Language 12 2.1.3 Resource Description Framework 13 2.2 The Semantic Web 14 2.3 Ontology 16 2.4 Ontology Description Languages 17 2.4.1 17 HTML-based Ontology Description Languages ii 2.4.2 XML-based Ontology Description Languages 18 2.4.3 RDF-based Ontology Description Languages 20 Semantic Web Portals 24 2.5.1 Semantic Web Portal Architecture 25 2.5.2 Requirements on Semantic Web Portals 25 2.6 Web Services and Semantic Web Services 27 2.7 Summary 29 2.5 Context-based Cluster Analysis 3.1 3.2 3.3 3.4 31 Clustering Methods 32 3.1.1 Hierarchical Clustering Methods 32 3.1.2 Partitioning Clustering Methods 33 3.1.3 Other Clustering Methods 35 3.1.4 Discussion 37 Context-based Cluster Analysis 38 3.2.1 Cross-Clustering Relation Generation 39 3.2.2 Cross-Clustering Context Generation 51 Performance Evaluation 55 3.3.1 Experiment 55 3.3.2 Evaluation Measures 57 3.3.3 Experimental Results 59 Summary 64 Expert and Expertise Finding 4.1 65 Related Work 65 4.1.1 Expertise Recommender Systems 66 4.1.2 Web Mining for Finding Expertise 66 4.1.3 Author Co-citation Analysis Approach 67 4.1.4 Discussion 68 4.2 CCA-based Expert Finding 68 4.3 Document Clustering 70 4.3.1 Feature Selection 70 4.3.2 Pre-processing 70 4.3.3 Transformation 71 4.3.4 Document Clusters Generation 71 Author Clustering 72 4.4 iii 4.4.1 Creating Author Co-Citation Pairs 72 4.4.2 Creating Raw Co-Citation Matrix 72 4.4.3 Converting into Correlation Matrix 73 4.4.4 Generating Author Clusters 73 4.5 Context-based Cluster Analysis 74 4.6 Expert Information Generation 75 4.6.1 Identifying Researchers’ Research Areas 75 4.6.2 Ranking Expert 76 4.6.3 Retrieving Expert Information 76 Expert Retrieval and Visualization 76 4.7.1 Expert Retrieval 77 4.7.2 Expert Visualization 77 Performance Evaluation 78 4.8.1 Experiment 79 4.8.2 Experimental Results 79 4.8.3 Comparison with Other Approaches 84 Summary 88 4.7 4.8 4.9 Research Trend Detection 5.1 90 Related Work 90 5.1.1 Semi-automatic Approaches 91 5.1.2 Automatic Approaches 92 5.1.3 Discussion 94 5.2 CCA-based Trend Detection 95 5.3 Keyword-based Clustering 96 5.3.1 Document Clustering 97 5.3.2 Publisher Clustering 97 5.3.3 Temporal Clustering 98 5.4 Context-based Cluster Analysis 99 5.5 Trend Information Generation 102 5.5.1 Current Trend Identification 103 5.5.2 Trend Information Extraction 103 5.5.3 Emerging Trend Identification 104 5.6 Trend Retrieval 105 5.7 Performance Evaluation 105 iv 5.8 5.7.1 Experiment 106 5.7.2 Trend Identification 106 5.7.3 Trend Information Extraction and Retrieval 109 5.7.4 Trend Visualization 109 Summary 111 Fuzzy Concept Hierarchy Generation 6.1 112 Related Work 113 6.1.1 Concept Hierarchy Generation 114 6.1.2 Conceptual Clustering 114 6.1.3 Formal Concept Analysis 115 6.1.4 Discussion 116 6.2 Fuzzy Theory 117 6.3 Fuzzy Concept Hierarchy Generation 119 6.4 6.5 6.6 6.7 6.3.1 Fuzzy Formal Concept Analysis 119 6.3.2 Fuzzy Conceptual Clustering 127 6.3.3 Hierarchical Relation Generation 129 Research Concept Hierarchy Generation 132 6.4.1 Fuzzy Formal Concept Analysis 133 6.4.2 Fuzzy Conceptual Clustering 133 6.4.3 Hierarchical Relation Generation 134 6.4.4 Performance Evaluation 135 Machine Faults Concept Hierarchy Generation 142 6.5.1 Fuzzy Formal Concept Analysis 143 6.5.2 Fuzzy Conceptual Clustering 143 6.5.3 Hierarchical Relation Generation 144 6.5.4 Performance Evaluation 145 News Topic Themes Concept Hierarchy Generation 149 6.6.1 Fuzzy Formal Concept Analysis 150 6.6.2 Fuzzy Conceptual Clustering 150 6.6.3 Hierarchical Relation Generation 151 6.6.4 Performance Evaluation 152 Summary 155 v Scholarly Ontology Generation 7.1 7.2 7.3 7.4 157 Related Work 158 7.1.1 Ontology Generation 158 7.1.2 Generating Ontology from Scholarly Knowledge 159 7.1.3 Discussion 160 Fuzzy Ontology Generation 161 7.2.1 The FOGA Approach 161 7.2.2 Incremental Ontology Update 166 7.2.3 Research Hierarchy Ontology Generation 170 Cluster-based Ontology Generation 170 7.3.1 The COGA Approach 171 7.3.2 Experts Ontology Generation 172 7.3.3 Trends Ontology Generation 173 Ontology Integration 174 7.4.1 Ontology Integration Framework 174 7.4.2 Scholarly Ontology Generation 175 7.5 Semantic Web Representation 178 7.6 Browsing Scholarly Ontology 182 7.7 Summary 183 Scholarly Semantic Web 8.1 184 Related Work 184 8.1.1 Citation-based Retrieval 184 8.1.2 Semantic Web-based Information Retrieval 187 8.1.3 Discussion 188 8.2 System Overview of SSWeb 188 8.3 Scholarly Semantic Web Services 189 8.4 8.3.1 Scholarly Service Provider 190 8.3.2 Scholarly Service Requester 192 8.3.3 Matchmaking Agent 193 8.3.4 Scholarly Information Retrieval 194 Summary 197 Conclusions 198 9.1 Summary 198 9.2 Future Work 201 vi 9.2.1 Discovering Other Scholarly Knowledge 201 9.2.2 Fuzzy Semantic Query Languages 201 9.2.3 Automatic Ontology Integration 203 9.2.4 Fuzzy Query Expansion using Fuzzy Concept Hierarchy 204 A List of Publications 205 A.1 Refereed Conferences and Workshops 205 A.2 Book Chapters 206 A.3 Journals 206 B 20 Queries for Performance Evaluation on Expert Finding 207 Bibliography 208 vii List of Tables 3.1 A distance matrix 45 3.2 A cross-table of a document clustering context 52 3.3 A cross-table of an author clustering context 53 3.4 A cross-clustering context from the document and author clustering contexts 55 3.5 Different combinations of clusters mining 57 4.1 An example of the Keyword-Author Cross-Clustering Context 74 4.2 Manually classified experts 79 4.3 Performance results based on the average F-measure 80 5.1 An example of a document clustering context 101 5.2 An example of a topic clustering context 101 5.3 An example of a temporal clustering context 101 5.4 An example of the Keyword-Topic-Temporal Cross-Clustering Context 102 5.5 Manually predefined trends in the Information Retrieval field 106 5.6 Trends identification results using the single link method 107 5.7 Trends identification results using the complete link method 107 5.8 Trends identification results using the average link method 108 5.9 Trends identification results using the Ward’s method 108 5.10 Performance results of trends information extraction 109 6.1 A cross-table of a formal context 120 6.2 A cross-table of a fuzzy formal context 122 6.3 Fuzzy formal context in Table 6.2 with α-cut = 0.5 122 6.4 A cross-table of a L-fuzzy context 125 viii 6.5 Full context of a L-fuzzy context 126 6.6 Number of research clusters using FCHG and LFCA-based conceptual clustering methods based on different similarity thresholds Ts 134 6.7 Runtime (in sec.) required to generate conceptual clusters 134 6.8 Performance results based on precision 137 6.9 Performance results based on recall 137 6.10 Performance results based on F-measure 137 6.11 Performance comparison based on precision 138 6.12 Performance comparison based on precision 138 6.13 Performance comparison based on F-measure 138 6.14 Number of research clusters using FCHG and LFCA-based conceptual clustering methods based on difference confidence thresholds TC 144 6.15 Runtime (in sec.) required to generate conceptual clusters 144 6.16 Retrieval accuracy 149 6.17 Number of research clusters using FCHG and LFCA-based conceptual clustering methods based on difference confidence thresholds TC 151 6.18 Runtime (in sec.) required to generate conceptual clusters 151 6.19 Manually classified themes of Reuters news topics 153 6.20 Performance results based on precision 154 6.21 Performance results based on recall 154 6.22 Performance results based on F-measure 154 B.1 20 queries for performance evaluation on expert finding 207 ix List of Figures 1.1 System architecture of the proposed Scholarly Semantic Web 2.1 Representation of a publication using XML 13 2.2 Another representation of the publication using XML 13 2.3 RDF data model 14 2.4 Representation of a publication using RDF 14 2.5 Architecture of the Semantic Web 15 2.6 Representation of semantic information using SHOE 18 2.7 Representation of semantic information using Ontobroker 19 2.8 Class representation using DAML-ONT 21 2.9 Class representation using OIL 22 2.10 Class representation using DAML+OIL 23 2.11 Class Representation using OWL 24 2.12 Semantic Web Portal 25 2.13 Operational mechanism in Web Services 27 2.14 Technologies used on the operational mechanism in Web Services 28 3.1 Fuzzy clustering 36 3.2 Context-based Cluster Analysis 38 3.3 Cross-Clustering Relation Generation 38 3.4 Vectorization from document and author clustering 41 3.5 Algorithm for distance matrix generation 46 3.6 Clustering multi-dimensional combined vectors with AHC 48 3.7 Relationship vector generation 50 3.8 A cross-clustering relation 51 x Bibliography [140] U of Princeton, “WordNet - a lexical database for English.” Availaible at http://wordnet.princeton.edu/online/ [141] S Kaski, “Dimensionality reduction by random mapping: Fast similarity computation for clustering,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN’98), vol 1, (NJ,USA), pp 413–418, IEEE Service Centre, 1998 [142] A Fong, S Hui, and H Vu, “Effective techniques for automatic extraction of Web publications,” Online Information Review, vol 26, no 1, pp 4–18, 2002 [143] Y Ding, “Visualization of intellectual structure in information retrieval: Author co-citation analysis,” International Forum on Information and Documentation, vol 23, no 1, pp 25–36, 1998 [144] W P S R D P A Kontostathis, L Galitsky, A Comprehensive Survey of Text Mining, ch A Survey of Emerging Trend Detection in Textual Data Mining Springer-Verlag, 2003 [145] A Porter and M Detampel, “Technology opportunities analysis,” Technological Forecasting and Social Change, vol 49, pp 237–255, 1995 [146] L Nowell, R France, D H an L.S Heath, and E A Fox, “Visualizing search results: Some alternatives to query-document similarity,” in Proceedings of SIGIR’96, (Zurich, Switzeland), 1996 [147] G Blank, W Pottenger, G Kessler, M Herr, H Jaffe, S Roy, D Gevry, and Q Wang, “CIMEL: Constructive, collaborative inquiry-based multimedia elearning,” in Proceedings of the 6th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), (United Kingdom), 2001 [148] S Roy, D Gevry, and W Pottenger, “Methodologies for trend detection in textual data mining,” in Proceedings of the Textmine’02 Workshop,Second Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining, (Washington, USA), 2002 221 Bibliography [149] R Bader, M Callahan, D Grim, J Krause, N Miller, and W Pottenger, “The role of the HDDIT M collection builder in hierarchical distributed dynamic indexing,” in Proceedings of the Textmine’01 Workshop, First SIAM International Conference on Data Ming, 2001 [150] W Pottenger and T Yang, Computational Information Retrieval, ch Detecting Emerging Concepts in Textual Data Mining Philadelphia, USA: SIAM, 2001 [151] A Popescul, G Flake, S L S., L Ungar, and C Giles, “Clustering and identifying temporal trends in document databases,” IEEE Advances in Digital Libraries, pp 173–182, 2000 [152] D J R Swan, “TimeMines: Constructing timelines with statistical models of word usage,” in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (Boston, MA, USA), 2003 [153] S Havre, E Hetzler, P Whitney, and L Nowell, “Themeriver: Visualizing thematic changes in large document collection,” IEEE Transactions on Visualization and Computer Graphics, vol 8, no 1, 2002 [154] J Allan, R Papka, and V Lavrenko, “On-line new event detection and tracking,” in Proceedings of ACM SIGIR, pp 37–45, 1998 [155] D Price, “Networks of scientific papers,” Science, vol 149, no 510-515, 1965 [156] H White and K McCain, “Bibliometrics,” Annual Review of Information Science and Technology, vol 24, pp 119–186, 1995 [157] V Harinarayan, A Rjaraman, and J Ullman, “Implementing data cubes efficiently,” in Proceedings of 1996 ACM-SIGMOD International Conference Management of Data, (Montreal, Canada), pp 205–216, 1996 [158] M Wang and B Iyer, “Efficient roll-up and drill-down analysis in relational database,” in Proceedings of 1997 SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pp 39–43, 1997 222 Bibliography [159] C Incorporated, “PowerPlay: Packaging information with transformer,” 1996 1996 [160] M Corey and M Abbey, Oracle Data Warehousing CA, USA: Osborne McGrawHill: Oracle Press, 1997 [161] M Incorporated, “DSSArchitect,” 1997 VA, 1997 [162] M Kamper, L Winstone, W Gong, S Cheng, and J Han, “Generalization and decision tree induction: Efficient classification in data mining,” in Proceedings of 1997 International Workshop on Research Issues on Data Engineering (RIDE’97), (Birmingham, England), pp 111–120, 1997 [163] R Michalski, “Inductive learning as rule-guided generalization and conceptual simplification of symbolic description: Unifying principles and a methodology,” in Proceedings of Workshop on Current Developments in Machine Learning, (PA, USA), 1980 [164] C Brew, “Systemic classification and its efficiency,” Computational Linguistics, vol 17, no 4, pp 375–408, 1991 [165] R Agrawal, T Imielinski, and A Swami, “Mining association rules between sets of items in large databases,” in Proceedings of the ACM SIGMOG Conference on Management of Data, pp 207–216, 1993 [166] R Agrawal and R Srikant, “Fast algorithm for mining association rules,” in Proceedings of 1994 International Conference of Very Large Databases, (Santiagio, Chile), pp 487–499, 1994 [167] R Sikal and R Agrawal, “Mining generalized association rules,” in Proceedings of 1995 International Conference of Very Large Database, (Zurich, Switzeland), pp 407–419, 1995 [168] D Keim, H Kriegel, and T Seidl, “Supporting data mining of large databases by visual feedback queries,” in Proceedings of the 10th International Conference on Data Engineering, (Houston, USA), pp 302–313, 1994 223 Bibliography [169] G Mineau and R Godin, Formal Methods in Databases and Software Engineering, ch An Incremental Concept Formation Approach for Learning from Databases, pp 39–53 Springer-Verlag, 1993 [170] D Fisher, “Knowledge acquisition via incremental conceptual clustering,” Machine Learning, vol 2, pp 139–172, 1987 [171] A Gordon, Classification: Methods for the Exploratory Analysis and Multivariate Chapman and Hall, 1981 [172] J Lebbe and R Vignes, Ordinal and Symbolic Data Analysis, ch Optimal Hierarchical Clustering with Order Constraint, pp 256–276 Springer-Verlag, 1996 [173] N Katayama and S Satoh, SIGMOD’97, ch The SR-Tree: An Index Structure for High-dimensional Nearest Neighbor Queries, pp 369–380 1997 [174] P Cheeseman, J Kelly, M Self, J Stutz, W Taylor, and D.Freeman, “AutoClass: A bayesian classification system,” in Proceedings of the Fifth International Workshop on Machine Learning, (CA, USA), pp 54–64, Morgan Kauffmann, 1988 [175] M Lebowitz, “Experiments with incremental concept formation: UNIMEM,” Mach Learn., vol 2, no 2, pp 103–138, 1987 [176] G Biswas, J Weinberg, and D Fisher, “Iterate: A conceptual clustering algorithm for data minining,” IEEE Transactions of Systems, Man and Cybernetics - Part C: Applications and Reviews, vol 28, no 2, 1998 [177] J Gennari, P Langley, and D Fisher, Machine Learning: Paradigms and Methods, ch Models of Incremental Concept Formation, pp 11–62 MIT Press, 1990 [178] Y Reich and S Fenves, Concept Formation: Knowledge and Experience in Unsupervised Learning, ch The Formation and Use of Abstract Concepts in Design, pp 323–353 Morgan Kaufmann, 1991 [179] C Li and G Biswas, “Conceptual clustering with numeric and nominal mixed data - a new similarity based system,” IEEE Transactions on Knowledge and Data Engineering, 1996 224 Bibliography [180] U Priss, “Linguistic applications of Formal Concept Analysis,” in Proceedings of The 1st International Conference on Formal Concept Analysis, (Darmstadt), 2003 [181] C Sporleder, “A Galois lattice based approach to lexical inheritance hierarchy learning,” in Proceedings of the ECAI 2002 Workshop on Machine Learning and Natural Language Processing for Ontology Engineering (OLT 2002), 2002 [182] W Petersen, “A set-theoretical approach for the induction of inheritance hierarchies,” Theoretical Computer Science, vol 51, 2002 [183] A Hotho, S Staab, and G Stumme, “Explaining text clustering result using semantic structures,” in Proceedings of the 7th European Conference on Principles of Data Mining and Knowledge Discovery PKDD 2003, pp 217–228, 2003 [184] A Hotho, S Staab, and G Stumme, “Text clustering based on background knowledge,” technical report, Univeristy of Karlsruhe, 2003 [185] B.Ganter and G.Stumme, “Creation and merging of ontology top-levels,” in Proceedings of Conceptual Structures for Knowledge Creation and Communication ICCS’03 (A Moor, W.Lex, and B.Ganter, eds.), vol LNAI 2746, pp 131–145, 2003 [186] R Cole and G Stumme, “CEM - a Conceptual Email Manager,” in Proceedings of ICCS 2000 (B Ganter and G W.Mineau, eds.), vol LNAI 1867, pp 438–452, 2000 [187] K.Jones, “View mail users manual,” 1999 Availaible at http://www.wonderworks.com/vm [188] C Schmitz, S Staab, R Studer, G Stumme, and J Tane, “Accessing distributed learning repositories through a courseware watchdog,” in Proceedings of E -Learn 2002 World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education (E-Learn 2002) (M Driscoll and T Reeves, eds.), (Norfolk), pp 909–915, 2002 225 Bibliography [189] R.E.Kent and C.Neuss, “Creating a Web analysis and visualization environment,” ComputerNetworks and ISDN Systems, vol 28, no 1-2, pp 109–117, 1995 [190] D.Richards and P.Compton, “Combining Formal Concept Analysis and ripple down rules to support reuse,” in In Proceedings of The 9th Internaltional Conference on Software Engineering and Knowledge Engineering (SEKE ’97), Springer, 1997 [191] G Stumme, R Taouil, Y Bastide, N Pasquier, and L Lakhan, “Computing Iceberg concept lattice with TITANIC,” Journal on Knowledge and Data Engineering, vol 42, no 2, pp 189–222, 2002 [192] B Ganter and R Wille, Applications of Combinatorics and Graph Theory to the Biological and Social Sciences, ch Conceptual Scaling, pp 139–167 New York, USA: Springer-Verlag, 1989 [193] G Birkhoff, Lattice Theory American Mathematical Society, 3rd ed., 1967 [194] F Vogt and R Wille, GraphDrawing’ 94, ch TOSCANA: a Graphical Tool for Analyzing and Exploring Data, pp 226–233 Heidelberg, 1995 [195] C Carpineto and G Romano, “GALOIS: An order-theoric approach to conceptual clustering,” in Proceedings of International Conference on Machine Learning ICML 1993, Morgan Kaufmann Publishers, 1993 [196] T Ho, “An approach to concept formation based on Formal Concept Analysis,” IECE Transactions of Information and Systems, vol E78-D, no 5, pp 553–559, 1995 [197] T Ho, KDD: Techniques and Applications, ch Incremental Conceptual Clustering in the Framework of Galois Lattice, pp 49–64 World Scientific, 1997 [198] J F Sowa, Conceptual Structures - Information Processing in Mind and Machine Addison - Wesley Publishing Company, 1984 226 Bibliography [199] G Mineau and R Godin, “Automatic structuring of knowledge bases by conceptual clustering,” IEEE Transactions on Knowledge and Data Engineering, vol 7, no 5, pp 824–828, 1995 [200] S Pollandt, Fuzzy-Begriffe: Formale Begriffsanalyse unscharfer Daten Berlin Heidelberg: Springer Verlag, 1996 [201] A Burusco and R F Gonzlez, “The study of the L-fuzzy concept lattice,” Mathware and Soft Computing, vol 1, no 3, pp 209–218, 1994 [202] V Huynh and Y Nakamori, Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies, ch Fuzzy Concept Formation Based on Context Model, pp 687–691 Amsterdam, Holland: IOS Press, 2001 [203] J Goguen, “L-fuzzy sets,” Journal of Mathematics, Analysis and Applications, vol 18, pp 145–157, 1967 [204] G Klir and B Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications Prentice Hall, 1995 [205] Y Lu, “Concept hierarchy in data mining: Specification, generation and implementation.” Availaible at http://gunther.smeal.psu.edu/3024.html [206] W Chu and K Chiang, “Abstraction of high level concepts from numerical values in databases,” in Proceedings of AAAI Workshop on Knowledge Discovery in Databases, pp pp 133–144, 1994 [207] N.Nanas, V.Uren, and A de Roeck, “Building and applying a concept hierarchy representation of a user profile,” in Proceedings of the 26th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, 2003 [208] I Watson, Applying Case-based Reasoning: Techniques for Enterprise Systems Morgan Kaufman Publishers, 1997 227 Bibliography [209] B Lees and J Corchado, “Case-based reasoning in a hybrid agent-oriented system,” in Proceedings of the 5th German Workshop on Case-based Reasoning, pp 139–144, 1997 [210] M Papagni, V Cirillo, and A Micarelli, “A hybrid architecture for a user-adapted training system,” in Proceedings of the 5th German Workshop on Case-based Reasoning, pp 181–188, 1997 [211] M Richter and S Wess, “Similarity, uncertainty and case-based reasoning in patdex.” Availaible at http://citeseer.csail.mit.edu/49639.html [212] I Corporation, “CBR content navigator.” Availaible at http://www.inference.com/products/ [213] “Rdf site summary (rss) 1.0.” Availaible at http://web.resoource.org/rss/1.0/spec [214] D Lewis, “Reuters-21578 text categorization test collection distribution 1.0.” Availaible at http://www.research.att.com/ lewis [215] N Noy and D L McGuinness, “Ontology development 101: A guide to creating your first ontology,” report smi-2001-0880, Department of Mechanical and Industrial Engineering, University of Toronto, 2001 [216] S Bechhofer, I Horrocks, P Patel-Schneider, and S Tessaris, “A proposal for a description logic interface,” in Proceedings of the International Workshop on Description Logics, pp 33–36, 1999 [217] E Morin, “Automatic acquisition of semantic relations between terms from technical corpora,” in Proceedings of the Fifth International Congress on Terminology and Knowledge Engineering (TKE-99), (Vienna, Austria), 1999 [218] M Hearst, “Automatic acquisition of hyponyms from large text corpora,” in Proceedings of the Fourteenth International Conference on Computational Linguistic, (France), 1992 [219] P Compton and A Jansen, Knowledge Acquisition, ch A Philosophical Basis for Knowledge Acquisition, pp 241–257 228 Bibliography [220] H Suryanto and P Compton, “Discovery of ontologies from knowledge bases,” in Proceedings of The 5th International Conference on Knowledge Capture (Y Gil, M Musen, J Shavlik, and Victoria(, eds.), (Canada), pp 171–178, 2001 [221] A Deitel, C Faron, and R Dieng, “Learning ontologies from RDF annotations,” in Proceedings of the IJCAI Workshop in Ontology Learning, (Seattle,USA), 2001 [222] C Papatheodorou, A Vassiliou, and B Simon, “Discovery of ontologies for learning resources using word-based clustering,” in Proceedings of ED-MEDIA 2002, (Denver,USA), 2002 [223] A Doan, P Domingos, and A Levy, “Learning source descriptions for data integration,” in Proceedings of the Third International Workshop on the Web and Databases, pp 81–86, 2000 [224] P Johannesson, “A method for transforming relational schemas into conceptual schemas,” in Proceedings of the 10th International Conference on Data Engineering (M Rusinkiewicz, ed.), (Houston, USA), pp 115–122, IEEE Press, 1994 [225] V Kashyap, “Design and creation of ontologies for environmental information retrieval,” in Proceedings of the Twelveth Workshop on Knowledge Acquisition, (Canada), 1999 [226] D Rubin, M Hewett, D Oliver, T Klein, and R Altman, “Automatic data acquisition into ontologies from pharmacogenetics relational data sources using declarative object definitions and XML,” in Proceedings of the Pacific Symposium on Biology (R.B.Altman, A Dunker, L Hunter, K Lauderdale, and T Klein, eds.), (Lihue, HI), 2002 [227] L Stojanovic, N Stojanovic, and R Volz, “Migrating data-intensive Web sites into the Semantic Web,” in Proceedings of the 17th ACM symposium on Applied Computing (SAC), pp 1100–1107, ACM Press, 2002 [228] A I B Bachimont and R Troncy, “Semantic commitment for designing ontologies: a proposal,” in Proceedings of 13th International Conference on Knowledge 229 Bibliography Engineering and Management (EKAW2002) (A Gomez-Perez and V Benjamins, eds.), vol LNAI 2473, pp 114–221, Springer-Verlag Berlin Heidelberg, 2002 [229] K Gupta, D Aha, E Marsh, and T Maney, “An architecture for engineering sublanguage WordNets,” in Proceedings of the First International Conference On Global WordNet, (Mysore, India: Central Institute of Indian Languages), pp 207– 215, 2002 [230] D Lonsdale, Y Ding, D Embley, and A Melby, “Peppering knowledge sources with SALT; boosting conceptual content for ontology generation,” in Proceedings of the AAAI Workshop on Semantic Web Meets Language Resources, 2002 [231] D I Moldovan and R C Girju, “An interactive tool for the rapid development of knowledge bases,” International Journal on Artificial Intelligence Tools (IJAIT), vol 10, no 1-2, 2001 [232] B Bibow and S Szulman, “TERMINAE: a linguistic-based tool for the building of a domain ontology,” in Proceedings of the 11th European Workshop on Knowledge Acquisition, Modelling and Management(EKAW’99), (Germany, Berlin), pp 49– 66, Springer-Verlag Berlin Heidelberg, 1999 [233] A Maedche and S Staab, “Ontology learning for the Semantic Web,” IEEE Intelligent Systems, Special Issue on the Semantic Web, vol 16, no 2, 2001 [234] E Agirre, O Ansa, E Hovy, and D Martinez, “Enriching very large ontologies using the WWW,” in Proceedings of the Workshop on Ontology Construction of the European Conference of AI (ECAI-00), 2000 [235] A Faatz and R Steinmetz, “Ontology enrichment with texts from the WWW,” in In Proceedings of Semantic Web Mining 2nd Workshop at ECML/PKDD-2002, (Helsinki, Finland), 2002 [236] C H Hwang, “Incompletely and imprecisely speaking: Using dynamic ontologies for representing and retrieving information,” in Proceedings of the 6th International Workshop on Knowledge Representation meets Databases (KRDB’99), (Sweden), 1999 230 Bibliography [237] R Navigli, P Velardi, and A Gangemi, “Ontology learning and its application to automated terminology translation,” IEEE Intelligent Systems, vol 18, no 1, 2003 [238] A Wagner, “Enriching a lexical semantic net with selectional preferences by means of statistical corpus analysis,” in Proceedings of the ECAI-2000 Workshop on Ontology Learning, (Berlin,Germany), pp 37–42, 2000 [239] F Xu, D Kurz, J Piskorski, and S Schmeier, “A domain adaptive approach to automatic acquisition of domain relevant terms and their relations with bootstrapping,” in Proceedings of LREC 2002, The 3rd International Conference on Language Resources and Evaluation, (Spain), 2002 [240] L Khan and F Luo, “Ontology construction for information selection,” in Proceedings of 14th IEEE International Conference on Tools with Artificial Intelligence, (Washington DC, USA), pp 122–127, 2002 [241] P Clerkin, P Cunningham, and C Hayes, “Ontology discovery for the Semantic Web using hierarchical clustering,” in Proceedings of Workshop at ECML/PKDD2001, (Germany), 2001 [242] G Bisson and C Nedellec, “Designing clustering methods for ontology building: The Mo’K workbench,” in Proceedings of the Workshop on Ontology Learning, 14th European Conference on Artificial Intelligence, ECAI’00 (S Staab, A Maedche, C Nedellec, and P WiemerHasting, eds.), (Germany), 2000 [243] DLF, CNI, and NSF, “Open Archives Initiative.” Availaible at http://www.openarchives.org/organization/index.html [244] L.A.Zadeh, “Fuzzy logic and approximate reasoning,” Synthese, vol 30, pp 407– 428, 1975 [245] M Islam and L Brankovic, “A framework for privacy preserving data mining,” in Proceedings of Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), (Dunedin, New Zealand), pp 163–168, 2004 231 Bibliography [246] D.H.Widyantoro and J.Yen, “A fuzzy ontology-based abstract search engine and its user studies,” in Proceedings of the 10th IEEE International Conference on Fuzzy Systems, (Melbourn, Australia), pp 1291–1294, 2001 [247] I Bratko, PROLOG Programming for Artificial Intelligence Pearson Education Limited, 3rd ed., 2000 [248] D Calvanese, G D Giacomo, and M Lenzerini, “A framework for ontology integration,” in Proceedings of the 2001 Internaltional Semantic Web Working Symposium (SWWS 2001), pp 303–316, 2001 [249] R Lara, S Han, H Lausen, M Stollberg, Y Ding, and D Fensel, “SWRL: A Semantic Web Rule Language combining owl and ruleml ” Availaible at http://www.daml.org/2003/11/swrl/ [250] S.Harnad and L Carr, “Integrating, navigating and analyzing E-print archives through open citation linking (the OpCit project),” Current Science, vol 79, pp 629–638, September 2000 [251] “PreScript - a utility for extracting text from PostScript files.” Availaible at http://www.nzdl.org/html/prescript.html [252] M Fern´andez, J Sim´eon, P Wadler, S Cluet, A Deutsch, D Florescu, A Levy, D Maier, J McHugh, J Robie, D Suciu, and J Widom, “XML query languages: Experiences and exemplars,” 1999 Available from http://www-db.research belllabs.com/user/simeon/xquery.ps [253] DAML, “OWL-S: Semantic markup for Web Services.” Availaible at http://www.daml.org/services/owl-s/1.0/owl-s.html [254] S Abiteboul, D Quass, J McHugh, J Widom, and W J., “The LOREL query language for semi-structured data,” International Journal on Digital Libraries, vol 1, no 1, pp 68–88, 1997 232 Bibliography [255] D Florescu, D Chamberlin, and J Robie, “Quilt: An XML query language for heterogeneous data sources,” in Proceedings of the 3rd International Workshop on the Web and Databases (WebDB’2000), (Dallas, US), pp 53–62, 2000 [256] W W W Consortium, “XQuery 1.0: An XML query language.” Availaible at http://www.w3.org/TR/xquery/, 2005 [257] V C D P M S G Karvounarakis, S Alexaki, “RQL: A declarative query language for RDF,” in Proceedings of the 11th International World Wide Web Conference (WWW2002), (Honolulu, Hawaii, USA), 2002 [258] L Miller, A Seaborne, and A Reggiori, “Three implementations of SquishQL, a simple RDF query language,” in Proceedings of the 1st International Semantic Web Conference (ISWC2002), (Italy), 2002 [259] S Kokkelink, “Transforming RDF with RDFPath.” Availaible at http://zoe.mathematik.uni-osnabrueck.de/QAT/Transform/RDFTransform.pdf, 2001 [260] RDFQL, “RDFQL reference manual.” Availaible at http://www.intellidimension.com/RDFGateway/ Docs/rdfqlmanual.asp, 2000 [261] M Sintek and S Decker, “TRIPLE - an RDF query, inference, and transformation language,” in Proceedings of the Deductive Databases and Knowledge Management Workshop (DDLP’ 2001), (Japan), 2001 [262] A de Vos, “An RDF query language based on DAML.” Availaible at http://www.langdale.com.au/RDF/DAML-Query.html, 2002 [263] I Horrocks and S Tessaris, “Querying the Semantic Web: a formal approach,” in Proceedings of the 1st International Semantic Web Conference (ISWC2002), (Italy), 2002 [264] Y Kalfoglou and M Schorlemmer, On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE Lecture Notes in Computer Science 2519, ch Information-flow-based Ontology Mapping, pp 1132–1151 Springer, 2002 233 Bibliography [265] N Noy and M Musen, “SMART: Automated support for ontology merging and alignment,” in Proceedings of the 12th Workshop on Knowledge Acquisition, Modelling and Management (KAW’99), (Banff, Canada), 1999 [266] N Noy and M Musen, “PROMPT: Algorithm and tool for automated ontology merging and alignment,” in Proceedings of the 17th National Conference on Artificial Intelligence (AAAI’00), (Austin, TX, USA), 2000 [267] E Motta, “Reusable components for knowledge models: Case studies in parametric design problem solving,” Frontiers in Artificial Intelligence and Applications, vol 53, 1999 [268] J R D McGuinness, R Fikes and S Wilder, “An environment for merging and testing large ontologies,” in Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR-2000), (Colorado, USA), 2000 [269] A Doan, J Madhavan, P Domingos, and A Halevy, “Learning to map between ontologies on the Semantic Web,” in Proceedings of the 11th International World Wide Web Conference (WWW 2002), (Hawaii, USA), 2002 [270] M Lacher and G Groh, “Facilitating the exchange of explicit knowledge through ontology mappings,” in Proceedings of the 14th International FLAIRS conference, (Key West, FL, USA), 2001 [271] E Efthimiadis, “Query expansion,” Annual Review of Information Science and Technology, vol 31, pp 121–187, 1996 [272] T Doszkocs, “CITE NLM: Natural-language searching in an online catalog,” Information Technology and Libraries, vol 2, no 4, pp 364–380, 1983 [273] M Porter and V Galpin, “Relevance feedback in a public access catalogue for a research library: Muscat at the Scott Polar Research Institute,” Program, vol 22, no 1, pp 1–20, 1988 234 Bibliography [274] S Wade and P Willett, “INSTRUCT: a teaching package for experimental methods in information retrieval part iii browsing, clustering and query expansion,” Program, vol 22, no 1, pp 44–61, 1988 [275] M Beaulieu and M Sanderson, “Concept-based interactive query expansion support tool (CIQUEST).” [276] A Sieg, B Mobasher, S Lytinen, and R Burke, “Using concept hierarchies to enhance user queries in Web-based information retrieval.” [277] M Sanderson and W Croft, “Deriving concept hierarchies from text,” in Proceedings of the 22nd Annual International ACM SIGIR conference on research and development in Information Retrieval (SIGIR ’99), (Berkeley, USA), pp 206–213, ACM, 1999 [278] M Sanderson and D Lawrie, Advances in information retrieval: recent research from the CIIR, ch Building, testing and applying concept hierarchies Kluwer Academic Publishers, 2000 235 [...]... concludes the thesis with a summary and states the future directions for further research works 10 Chapter 2 The Semantic Web In this chapter, we review the development and the state -of -the- art of technologies for the Semantic Web First, we discuss the traditional World Wide Web, which is the precursor of the Semantic Web, and markup languages Then, the Semantic Web is introduced Next, we discuss ontology, ... (FOGA), Cluster- based Ontology Generation frAmework (COGA) and Ontology Integration Framework (OIF), for ontology generation Chapter 8 presents the proposed system on the Scholarly Semantic Web In this chapter, the distributed architecture of the system is given We then discuss the Scholarly Semantic Web Services that enable the scholarly knowledge understandable, sharable and accessible on the Semantic Web. .. which is adopted for knowledge representation for the Semantic Web, and in particular ontology description languages Finally, we discuss Semantic Web Portals for Semantic Web applications, and Web Services for the delivery of services on the Semantic Web 2.1 Markup Languages The World Wide Web, proposed by Tim Berners-Lee [43], is the universe of networkaccessible information On the Web, information is... investigate the integration of different types of ontologies that are generated from cluster analysis and fuzzy conceptual clustering • Scholarly Semantic Web Services To provide scholarly information retrieval services over the Semantic Web, we will investigate a Semantic Web- based architecture for the delivery of Scholarly Semantic Web Services The proposed architecture should enable the retrieval of scholarly. .. further explored for supporting advanced search functions such as expert finding and trend detection The development of the Semantic Web has provided a very suitable environment for supporting the sharing of scholarly knowledge among different scholarly research communities However, one of the challenges for the development of Semantic Webbased retrieval systems is on the construction of scholarly ontology. .. for expert finding, trend detection and fuzzy document retrieval 1.7 Organization of the Thesis This chapter has discussed the background and motivation of this research work The objectives of the research have been given We have also listed the contributions that have been achieved The rest of the thesis is organized as follows Chapter 2 reviews the Semantic Web and the state -of -the- art Semantic Web. .. scholarly knowledge as ontology, which is distributed on the Semantic Web As such, scholarly information can be managed and refined by the corresponding domain 5 Chapter 1: Introduction Citation Database Cluster Analysis Ontology Generation Scholarly Semantic Web Scholarly Ontology Organization 1 Scholarly Web Services Scholarly Ontology Web Browser User Organization 2 Scholarly Ontology Organization... archives based on the Semantic Web However, one of the major obstacles for developing Semantic Web- based retrieval systems is on the construction of ontology for the corresponding domain In the scholarly domain, the scholarly ontology of the existing Semantic Web- based retrieval systems is constructed mainly based on explicit information from scientific documents (such as titles, authors and abstracts)... technologies, which include ontology, Semantic Web Portals and Semantic Web Services In Chapter 3, we discuss the proposed cluster analysis technique for mining cluster relationships from multiple clustering data The technique, which is known as Contextbased Cluster Analysis, is capable of representing cluster relationships among multiple clusters as mathematical models The performance of the proposed technique... ontology The construction process for scholarly ontology should be easy and preferably automatic rather than manual, which is tedious and time-consuming This research aims to develop a Semantic Web- based system for the sharing and retrieval of scholarly information based on a citation database The proposed system is known as Scholarly Semantic Web (or SSWeb) The proposed SSWeb system will organize scholarly