68,079 research outputs found
ServeNet: A Deep Neural Network for Web Services Classification
Automated service classification plays a crucial role in service discovery,
selection, and composition. Machine learning has been widely used for service
classification in recent years. However, the performance of conventional
machine learning methods highly depends on the quality of manual feature
engineering. In this paper, we present a novel deep neural network to
automatically abstract low-level representation of both service name and
service description to high-level merged features without feature engineering
and the length limitation, and then predict service classification on 50
service categories. To demonstrate the effectiveness of our approach, we
conduct a comprehensive experimental study by comparing 10 machine learning
methods on 10,000 real-world web services. The result shows that the proposed
deep neural network can achieve higher accuracy in classification and more
robust than other machine learning methods.Comment: Accepted by ICWS'2
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Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.
The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24
A Framework for Semi-automated Web Service Composition in Semantic Web
Number of web services available on Internet and its usage are increasing
very fast. In many cases, one service is not enough to complete the business
requirement; composition of web services is carried out. Autonomous composition
of web services to achieve new functionality is generating considerable
attention in semantic web domain. Development time and effort for new
applications can be reduced with service composition. Various approaches to
carry out automated composition of web services are discussed in literature.
Web service composition using ontologies is one of the effective approaches. In
this paper we demonstrate how the ontology based composition can be made faster
for each customer. We propose a framework to provide precomposed web services
to fulfil user requirements. We detail how ontology merging can be used for
composition which expedites the whole process. We discuss how framework
provides customer specific ontology merging and repository. We also elaborate
on how merging of ontologies is carried out.Comment: 6 pages, 9 figures; CUBE 2013 International Conferenc
Acquisition and management of semantic web service descriptions
Abstract. The increasing importance and use of Web services have resulted in a number of efforts targeted at automating Web service discovery and composition based on semantic descriptions of their properties. However, the progress in the automation of Web service discovery is still held back by the fact that the description of Web services in terms of semantic metadata is still mainly manually. This Ph.D. thesis addresses this problem by developing an approach for the acquisition and management of semantic Web service descriptions in order to facilitate efficient service discovery and composition. Specifically, this involves the collection of information about a Web service, the acquisition of semantic descriptions based on the collected information, and the structured storage of the generated semantic descriptions.
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
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