Latent semantic analysis - The dynamics of semantics web services discovery
Semantic Web Services (SWS) have currently drawn much momentum in both academia and industry. Most of the solutions and specifications for SWS rely on ontology building, a task needs much human (e.g. domain experts) involvement, and hence cannot scale very well in face of vast amount of web informat...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Book Chapter |
| Published: |
Springer
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/6357 |
| _version_ | 1848745052862414848 |
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| author | Wu, Chen Potdar, Vidyasagar Chang, Elizabeth |
| author2 | E. Chang |
| author_facet | E. Chang Wu, Chen Potdar, Vidyasagar Chang, Elizabeth |
| author_sort | Wu, Chen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Semantic Web Services (SWS) have currently drawn much momentum in both academia and industry. Most of the solutions and specifications for SWS rely on ontology building, a task needs much human (e.g. domain experts) involvement, and hence cannot scale very well in face of vast amount of web information and myriad of services providers. The recent proliferation of SOA applications exacerbates this issue by allowing loosely-coupled services to dynamically collaborate with each other, each of which might maintain a different set of ontology. This chapter presents the fundamental mechanism of Latent Semantic Analysis (LSA), an extended vector space model for Information Retrieval (IR), and its application in semantic web services discovery, selection, and aggregation for digital ecosystems. First, we explore the nature of current semantic web services within the principle of ubiquity and simplicity. This is followed by a succinct literature overview of current approaches for semanticservices/software component (e.g. ontology-based OWL-s) discovery and the motivation for introducing LSA into the user-driven scenarios for service discovery and aggregation. We then direct the readers to the mathematical foundation of LSA ? SVD of data matrices for calculating statistics distribution andthus capturing the ?hidden? semantics of web services concepts. Some existing applications of LSA in various research fields are briefly presented, which gives rise to the analysis of the uniqueness (i.e. strength, limitations, parameter settings) of LSA application in semantic web services. We provide a conceptual level solution with a proof-of-concept prototype to address such uniqueness. Finallywe propose an LSA-enabled semantic web services architecture fostering service discovery, selection, and aggregation in a digital ecosystem. |
| first_indexed | 2025-11-14T06:11:14Z |
| format | Book Chapter |
| id | curtin-20.500.11937-6357 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:11:14Z |
| publishDate | 2008 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-63572022-11-21T05:19:39Z Latent semantic analysis - The dynamics of semantics web services discovery Wu, Chen Potdar, Vidyasagar Chang, Elizabeth E. Chang T. Dillon R. Meersman K. Sycara Semantic Web Services (SWS) have currently drawn much momentum in both academia and industry. Most of the solutions and specifications for SWS rely on ontology building, a task needs much human (e.g. domain experts) involvement, and hence cannot scale very well in face of vast amount of web information and myriad of services providers. The recent proliferation of SOA applications exacerbates this issue by allowing loosely-coupled services to dynamically collaborate with each other, each of which might maintain a different set of ontology. This chapter presents the fundamental mechanism of Latent Semantic Analysis (LSA), an extended vector space model for Information Retrieval (IR), and its application in semantic web services discovery, selection, and aggregation for digital ecosystems. First, we explore the nature of current semantic web services within the principle of ubiquity and simplicity. This is followed by a succinct literature overview of current approaches for semanticservices/software component (e.g. ontology-based OWL-s) discovery and the motivation for introducing LSA into the user-driven scenarios for service discovery and aggregation. We then direct the readers to the mathematical foundation of LSA ? SVD of data matrices for calculating statistics distribution andthus capturing the ?hidden? semantics of web services concepts. Some existing applications of LSA in various research fields are briefly presented, which gives rise to the analysis of the uniqueness (i.e. strength, limitations, parameter settings) of LSA application in semantic web services. We provide a conceptual level solution with a proof-of-concept prototype to address such uniqueness. Finallywe propose an LSA-enabled semantic web services architecture fostering service discovery, selection, and aggregation in a digital ecosystem. 2008 Book Chapter http://hdl.handle.net/20.500.11937/6357 10.1007/978-3-540-89784-2_14 Springer fulltext |
| spellingShingle | Wu, Chen Potdar, Vidyasagar Chang, Elizabeth Latent semantic analysis - The dynamics of semantics web services discovery |
| title | Latent semantic analysis - The dynamics of semantics web services discovery |
| title_full | Latent semantic analysis - The dynamics of semantics web services discovery |
| title_fullStr | Latent semantic analysis - The dynamics of semantics web services discovery |
| title_full_unstemmed | Latent semantic analysis - The dynamics of semantics web services discovery |
| title_short | Latent semantic analysis - The dynamics of semantics web services discovery |
| title_sort | latent semantic analysis - the dynamics of semantics web services discovery |
| url | http://hdl.handle.net/20.500.11937/6357 |