Summary of the paper
| Title | Ontology-Based Categorization of Web Services with Machine Learning |
|---|---|
| Authors | Adam Funk and Kalina Bontcheva |
| Abstract | We present the problem of categorizing web services according to a shallowontology for presentation on a specialist portal, using their WSDL andassociated textual documents found by a crawler. We treat this as a textclassification problem and apply first information extraction (IE) techniques(voting using keywords weight according to their context), then machinelearning (ML), and finally a combined approach in which ML has priority overweighted keywords, but the latter can still make up categorizations forservices for which ML does not produce enough. We evaluate the techniques(using data manually annotated through the portal, which we also use as thetraining data for ML) according to standard IE measures for flat categorizationas well as the Balanced Distance Metric (more suitable for ontologicalclassification) and compare them with related work in web servicecategorization. The ML and combined categorization results are good and thesystem is designed to take users' contributions through the portal's Web 2.0features as additional training data. |
| Language | Document Classification, Text categorisation |
| Topics | Semantic Web, Web Services, Document Classification, Text categorisation |
Full paper  |
Ontology-Based Categorization of Web Services with Machine Learning |
| Bibtex | @InProceedings{FUNK10.170, author = {Adam Funk and Kalina Bontcheva}, title = {Ontology-Based Categorization of Web Services with Machine Learning}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |