Practical semantic web mining platform

1
Practical semantic web mining platform
2
What is?

• SWM includes
• Semantic Web and RDF
• Regular Expressions, Web Agents
• HMMs and Information Extraction
• Rule Mining, F-Logic, Description Logic
• Information Integration
• Planning for Data Gathering
• Ontologies, Learning, Editing
• Text Classification
• Applications E-Commerce
• Web services
• Semantic Web Browser
• etc

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Some Background
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Algorithm/theory of ML

• Techniques of Machine Learning /Data Mining
• Bayesian classification/NN/GA
• Statistical technique
• Active Learning, Multi-View Learning
• Risk Minimization/Maximum Entropy Model

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Annotation

• Multiple Sources
• Annotation tools
• Using ML to automate the process
• Learn annotation rule
• Active Learn Driven (reduce training sample)
• Multi-view (improve performance)
• Multi-view detection (improve again)

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Mapping Link

• Mapping
• Find mapping points
• Find Complex mapping points (subof, superof,
  5(ab), even conjunct of, etc)
• Translate instances based on Mapping
• Link
• Find Link Points
• Find Complex Links
• Integrate Ontology
• Mapping/Link detection.

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Mapping Link

• Multi-view
• name
• Instance
• Relationship, etc
• Active learning. Ask the user to specify the most
  confused mapping/link
• Multi-view detection. Improve the performance

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Indexing

• What is the difference between SI and Text
  indexing/XML indexing?
• How to define the data structure of SI? (note
  that such structure should represent the
  characters of SW Ontology)
• How to make it efficient? (how to compare to
  others work? Are there some works on it?)

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Semantic Retrieval

• Domain vs. General
• Make use of SI Ontology to improve the
  performance.
• Make use of reasoning technique to improve.

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Reasoning

• Reasoning rules learning
• Example Resumes, Jobs
• How to find the most appropriate job for
  individual?
• How to find the most appropriate person for
  specified job?
• Define the Rules if Person.Age(x)lt30 then
  Job(y).Salarygt8000
• Rule Discovery

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Applications

• Jobs Resumes
• E-Commerce. E.g. Travel, Tickets, etc.
• Personal Assistant. Track ones work and interest
  to find new information automatically.
• Semantic Web Browser

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Free discussion for the platform
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Aspects

• Data
• Content
• what will to do, what can do, what not.
• Semantic web, semantic web services
• Theory-gtgtmay be basic for SCI?
• Practical application!!!! important
• Proposal Schedule.

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Data

• Data preparation
• Domain jobresume, software (from sourceforge),
  travel web services.
• ontology. Metadata instance
• Works
• metadata definition?integrate a ontology editor
  (protégé or ontoedit or orient)
• Instance database, ? use technique of annotation
  or IE to extract information from specific web
  sites.
• How to save? use jena to save the data in
  database and query it by RQL? indexing?

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Content

• Ontology building, knowledge base building?use
  wordnet to assist
• Composition for web services. If not web
  services, what we can do, such as jobs resumes.
• Annotation deep annotation. Web service
  annotation, text annotation, even image
  annotation.
• Mapping. concept mapping, instance mapping. ?
  translation, merge, meaning negotiation(mapping
  representation)
• Data Integration. Combine annotation and mapping

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Content

• Semantic search engine. Its definition? Simple
  searchdata search, then how to make use of
  ontology. Reasoning?
• How to make it practical, that is, how to do it
  in our domain. Shall it be a general one or
  domain one?
• Ontology summary. Need a better name. output
  knowledge in ontology by NLP.
• Indexing?
• Tools integration

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Theory

• ML, data mining.
• Inductive learning NN, Bayes, SVM, GA. Code them
  or one of them by ourselves. It will cost our
  time, but it doesnt mean waste time.
• Transductive learning.
• Selective learning.
• More general theory, risk minimization. Note that
  RM is an algorithm. It is a framework for ML. Any
  learning algorithms can be used as its
  implementation.
• Active learning multi-view
• Reduce the samples of training.
• Improve the precision.

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Practical application

• Jobs resumes
• Targets to find the best qualified
  resumes/persons for specified job or to find the
  best jobs for a person.
• Software from sourceforge, etc.
• Aim at software composition. ? web service
  composition. Software search

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Practical application

• more?

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Proposal schedule

• Why proposal?
• Why schedule?
• Can we work together for the possible platform?

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Further Reading
24
Further reading on Semantic Annotation

• A. Kiryakov, B. Popov, et al. Semantic
  Annotation, Indexing, and Retrieval. 2nd
  International Semantic Web Conference (ISWC2003),
  http//www.ontotext.com/publications/index.htmlKi
  ryakovEtAl2003
• Alani, 2003 Alani, H., Kim, S., Millard, D.,
  Weal, M., Hall, W., Lewis, P. and Shadbolt, N.
  Automatic Ontology-Based Knowledge Extraction
  from Web Documents. IEEE Intelligent Systems
  18(1)pp. 14-21.
• Bemjamins, 2002Richard Benjamins, Jesus
  Contreras. White Paper Six Challenges for the
  Semantic Web. Intelligent Software Components.
  Intelligent software for the networked economy
  (isoco). April, 2002.
• Berners-Lee, 1999 Tim Berners-Lee, Mark
  Fischetti (Contributor), Michael L. Dertouzos
  Weaving the Web The Original Design and
  Ultimate Destiny of the World Wide Web 1999.
• Califf, 1998 Califf M. E. (1998), Relational
  Learning Techniques for Natural Language
  Information Extraction, Ph.D. thesis, Univ.
  Texas, Austin, 1998
• Ciravegna, 2001 Fabio Ciravegna. (LP)2, an
  adaptive algorithm for information extraction
  from web-related texts. In Proceedings of the
  IJCAI-2001 Workshop on Adaptive Text Extraction
  and Mining held in conjunction with 17th
  International Joint Conference on Artificial
  Intelligence (IJCAI), Seattle, Usa, August 2001.

25
Further reading on Semantic Annotation

• Cohen, 2001 W. Cohen, L. Jensen, A structured
  wrapper induction system for extracting
  information from semi-structured documents, in
  Proceedings of the Workshop on Adaptive Text
  Extraction and Mining (IJCAI01), 2001.
• Cunningham. 2002 H. Cunningham, D. Maynard, K.
  Bontcheva, and V. Tablan. GATE A Framework and
  Graphical Development Environment for Robust NLP
  Tools and Applications. In Proceedings of the
  40th Anniversary Meeting of the Association for
  Computational Linguistics, 2002.
• Czejdo, 2000 B. Czejdo, J. Dinsmore, C. H.
  Hwang, R. Miller, M. Rusinkiewicz. Automatic
  Generation of Ontology Based Annotations in XML
  and Their Use in Retrieval Systems. Proceedings
  of the First International Conference on Web
  Information Systems Engineering (WISE'00)-Volume
  1. IEEE Computer Society Washington, DC, USA.
  2000. 296-300
• Dhamankar, 2004 Robin Dhamankar, Yoonkyong Lee,
  AnHai Doan, Alon Halevy, Pedro Domingos. iMAP
  Discovering Complex Semantic Matches between
  Database Schemas. SIGMOD 2004 June 1318, 2004,
  Paris, France.

26
Further reading on Semantic Annotation

• Dill, 2003 Stephen Dill, Nadav Eiron, David
  Gibson, Daniel Gruhl, R. Guha, Anant Jhingran,
  Tapas Kanungo, Kevin S. McCurley, Sridhar
  Rajagopalan, Andrew Tomkins, John A. Tomlin,
  Jason Y. Zien. A case for automated large-scale
  semantic annotation. Journal of Web Semantics
  Science, Services and Agents on the World Wide
  Web. Published by Elsevier B.V. July,
  2003115-132
• Eriksson, 1999 H. Eriksson, R. Fergerson, Y.
  Shahar, and M. Musen. Automatic generation of
  ontology editors. In Proceedings of the 12th
  Banff Knowledge Acquisition Workshop, Banff
  Alberta, Canada, 1999.
• Handschuh, 2002 S. Handschuh, S. Staab, F.
  Ciravegna, S-CREAMsemi-automatic creation of
  metadata, in Proceedings of the 13th
  International Conference on Knowledge Engineering
  and Management (EKAW 2002), Siguenza, Spain,
  2002, pp. 358-372.
• Heflin, 2000 J. Heflin, J. Hendler, Searching
  the web with shoe, in AAAI-2000 Workshop on AI
  for Web Search, Austin, Texas, 2000.
• Kahan, 2001 J. Kahan, M.-R. Koivunen, Annotea
  an open RDF infrastructure for shared web
  annotations, in World Wide Web, 2001, pp.
  623-632.

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Further reading on Semantic Annotation

• Kogut, 2001 P. Kogut, W. Holmes, AeroDAML
  applying information extraction to generate DAML
  annotations from web pages, 2001.
• Kushmerick, 1997 N. Kushmerick, D.S. Weld, R.B.
  Doorenbos, Wrapper induction for information
  extraction, in Proceedings of the International
  Joint Conference on Artificial Intelligence
  (IJCAI), 1997, Nagoya, Japan, pp. 729-C737.
• Leonard, 2001 T. Leonard, H. Glaser, Large
  scale acquisition and maintenance from the web
  without source access, http//www.
  semannot2001.aifb.uni-karlsruhe.de/positionpapers/
  Leonard. pdf, 2001.
• Lerman, 2001 K. Lerman, C. Knoblock, S. Minton,
  Automatic data extraction from lists and tables
  in web sources, in IJCAI-2001 Workshop on
  Adaptive Text Extraction and Mining, Seattle, WA,
  August 2001.
• Li, 2001 L.Z. Jianming Li, Y. Yu, Learning to
  generate semantic annotation for domain specific
  sentences, in Knowledge Markup and Semantic
  Annotation Workshop in K-CAP 2001, Victoria, BC,
  2001.
• Popov, 2003 Borislav Popov, Atanas Kiryakov,
  Dimitar Manov, Angel Kirilov, Damyan Ognyanoff,
  and Miroslav Goranov. Towards Semantic Web
  Information Extraction. In ISWC'03 Workshop on
  Human Language Technology for the Semantic Web
  and Web Services, 2003.1-21

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Further reading on Semantic Annotation

• Schaffer, 1993 Selecting a classification
  method by cross-validation. Machine Learning,
  13(1)135-143
• Soderlan, 1999 Soderland, S. Learning
  information extraction rules for semi-structured
  and free text. Machine Learning. 1999,1. 1-44
• Soo, 2003 Von-Wun Soo, Chen-Yu Lee, Chung-Cheng
  Li, Shu Lei Chen and Ching-chih Chen. Automated
  Semantic Annotation and Retrieval Based on
  Sharable Ontology and Case-based Learning
  Techniques. Proceedings of the 2003 Joint
  Conference on Digital Libraries. 2003 IEEE.
• Vargas-Vera, 2001 M. Vargas-Vera, E. Motta, J.
  Domingue, S. Buckingham Shum, and M. Lanzoni.
  Knowledge Extraction by using an Ontology-based
  Annotation Tool. In K-CAP 2001 workshop on
  Knowledge Markup and Semantic Annotation,
  Victoria, BC, Canada, October 2001.
• Vargas-Vera, 2002 M. Vargas-Vera, E. Motta, J.
  Domingue, M. Lanzoni, A. Stutt, F. Ciravegna,
  MnM ontology driven semiautomatic and automatic
  support for semantic markup, in Proceedings of
  the 13th International Conference on Knowledge
  Engineering and Management (EKAW 2002), Siguenza,
  Spain, 2002.

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Further reading on Ontology Mapping

• 1 Berger, J. Statistical decision theory and
  Bayesian analysis. Springer-Verlag. 1985
• 2 Calvanese, D. De Giacomo, G. and Lenzerini,
  M. 2002. A framework for ontology integration. In
  Cruz, I. Decker, S. Euzenat, J. and
  McGuinness, D., eds., The Emerging Semantic Web.
  IOS Press. 201-214.
• 3 H. Cunningham, D. Maynard, K. Bontcheva, and
  V. Tablan. GATE A Framework and Graphical
  Development Environment for Robust NLP Tools and
  Applications. In Proceedings of the 40th
  Anniversary Meeting of the Association for
  Computational Linguistics, 2002.
• 4 Robin Dhamankar, Yoonkyong Lee, AnHai Doan,
  etal. iMAP Discovering Complex Semantic Matches
  between Database Schemas. Proceedings of the 2004
  ACM SIGMOD International Conference on Management
  of Data, 2004. Paris, France ACM Press.
• 5 H. Do and E. Rahm. Coma A system for
  flexible combination of schema matching
  approaches. In Proc. of VLDB-2002.
• 6 Doan, A.H., P. Domingos, A. Halevy
  Reconciling Schemas of Disparate Data Sources A
  Machine-Learning Approach. SIGMOD 2001.
• 7 A. Doan, J. Madhavan, P. Domingos, and A.
  Halevy. Learning to map between ontologies on the
  semantic web. In Proceedings of the World-Wide
  Web Conference (WWW-2002), pages 662-673. ACM
  Press, 2002.

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Further reading on Ontology Mapping

• 8 J. Kang and J. Naughton. On schema matching
  with opaque column names and data values. In
  Proc. of SIGMOD-2003.
• 9 W. Kim and J. Seo. Classifying schematic and
  data heterogeneity in multidatabase systems. IEEE
  Computer, 1991, 24(12)12-18
• 10 J. Madhavan, P. Bernstein, and E. Rahm.
  Generic schema matching with cupid. In Proc. of
  VLDB-2001.
• 11 A. Maedche, B. Moltik, N. Silva and R. Volz.
  MAFRA -An Ontology MApping FRAmework in the
  Context of the Semantic Web. In Proceeding of the
  EKAW'2002, Siguenza, Spain. 2002.
• 12 Alexander Maedche, Steffen Staab Ontology
  Learning for the Semantic Web. IEEE Intelligent
  Systems 16(2) 72-79 (2001)
• 13 Jayant Madhavan, Philip Bernstein, Kuang
  Chen, Alon Halevy, and Pradeep Shenoy. Corpus
  based schema matching. In Proc. of the IJCAI-03
  Workshop on Information Integration on the Web
  (IIWeb-03), 2003.
• 14 McGuinness D., Fikes R., Rice J., and Wilder
  S. An environment for merging and testing large
  ontologies. Proceedings of the 7th International
  Conference on Principles of Knowledge
  Representation and Reasoning. Colorado, USA.

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Further reading on Ontology Mapping

• 15 S. Melnik, H. Molina-Garcia, and E. Rahm.
  Similarity flooding a versatile graph matching
  algorithm. In Proc. of ICDE-2002.
• 16 N. F. Noy and M. A. Musen. PROMPT Algorithm
  and Tool for Automated Ontology Merging and
  Alignment. In Proc. of AAAI-2000, pages 450-455,
  2000.
• 17 Nuno Silva and Joao Rocha. Semantic Web
  Complex Ontology Mapping. IEEE/WIC International
  Conference on Web Intelligence (WI'03) October
  13-17, 2003 Halifax, Canada82-100
• 18 Omelayenko, B. RDFT A Mapping Meta-Ontology
  for Business Integration Workshop on Knowledge
  Transformation for the Semantic Web (KTSW 2002)
  at ECAI'2002. Lyon, France 200276-83
• 19 Palopoli, L., G. Terracina, D. Ursino The
  System DIKE Towards the Semi-Automatic Synthesis
  of Cooperative Information Systems and Data
  Warehouses. ADBIS-DASFAA 2000, 108C117
• 20 Park, J. Y., Gennari, J. H. and Musen, M.
  A. "Mappings for Reuse in Knowledge-based
  Systems" 11th Workshop on Knowledge Acquisition,
  Modelling and Management (KAW 98) Banff, Canada
  1998.
• 21 Patrick. P, Dekang. L. Discovering Word
  Senses from Text. In Proceedings of ACM SIGKDD
  Conference on Knowledge Discovery and Data Mining
  2002613-619.

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Further reading on Ontology Mapping

• 22 Richard Benjamins, Jes?s Contreras. White
  Paper Six Challenges for the Semantic Web.
  Intelligent Software Components. Intelligent
  software for the networked economy (isoco).
  April, 2002.
• 23 E. Rahm and P. A. Bernstein. A survey of
  approaches to automatic schema matching. The VLDB
  Journal, 10334-350, 2001.
• 24 Tim Berners-Lee, Mark Fischetti
  (Contributor), Michael L. Dertouzos "Weaving the
  Web The Original Design and Ultimate Destiny of
  the World Wide Web" 1999.
• 25 K. M. Ting and I. H. Witten. Issues in
  stacked generalization. Journal of Artificial
  Intelligence Research, 10271-289, 1999.
• 26 Wache, H. Voegele, T. Visser, U.
  Stuckenschmidt, H.Schuster, G. Neumann, H. and
  Huebner, S. 2001. Ontology-based integration of
  information - a survey of existing approaches. In
  Proc. of IJCAI 2001 Workshop on Ontologies and
  Information Sharing.
• 27 Wiesman, F., Roos, N., and Vogt, P. (2001).
  Automatic ontology mapping for agent
  communication. Technical report.
• 28 L. Xu and D. Embley. Using domain ontologies
  to discover direct and indirect matches for
  schema elements. In Proc. of the Semantic
  Integration Workshop at ISWC-2003.

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Further Reading on Machine Learning

• Muslea. Multi-view plus active learning. (thesis)
• Tom M. Mitchell. Machine Learning.
• Richard O. Duda. Pattern Classification. (Second
  Edition)
• Zhai-Xiang Chen. Risk Minimization based
  Information Retrieval. (thesis)
• Wrapper Induction. Several thesis rapier, etc
• Data Mining. Han,