ACM Transactions on Mathematical Software (TOMS)
Latent semantic indexing is an optimal special case of multidimensional scaling
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
A snapshot of public web services
ACM SIGMOD Record
A Java Reuse Repository for Eclipse using LSI
ASWEC '06 Proceedings of the Australian Software Engineering Conference
Semantic Web Services, Processes and Applications (Semantic Web and Beyond: Computing for Human Experience)
How latent is latent semantic analysis?
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Collaborative filtering technique for web service recommendation based on user-operation combination
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Semantics-based web service discovery using information retrieval techniques
INEX'10 Proceedings of the 9th international conference on Initiative for the evaluation of XML retrieval: comparative evaluation of focused retrieval
A recommender system based on historical usage data for web service discovery
Service Oriented Computing and Applications
A ranking algorithm integrating vector space model with semantic metadata
Proceedings of the CUBE International Information Technology Conference
A hybrid graph based framework for integrating information from RDF and topic map: a proposal
Proceedings of the CUBE International Information Technology Conference
An approach to manage ontology dynamically based on web service composition requests
Proceedings of the CUBE International Information Technology Conference
| Hi-index | 0.00 |
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 semantic services/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 and thus 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. Finally we propose an LSA-enabled semantic web services architecture fostering service discovery, selection, and aggregation in a digital ecosystem.