Tag Ontology Automatic Building for Semantic Searching of Services: a Case Study on Mashup Services

Pan, Weifeng; Li, Shan

doi:10.4304/jcp.7.12.2979-2986

Cited by 4 publications

(3 citation statements)

References 20 publications

(21 reference statements)

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“…W P. Pan focused on the building of a tag ontology to enhance the semantic searching of services, and final to tackle this problem. By this method, we improved the efficiency of searching the corresponding service [21]. The framework of PPHIIS is based on MDA (Model Driver Architecture), and is designed in view of the framework needs and the advantage of characteristics of the organization agent.…”

Section: Methodsmentioning

confidence: 99%

Practical Applications of Ontology-based multi-agent systems for PPHIIS

Li¹,

Liu²,

Fu³

et al. 2014

JSW

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This study presents an ontology-based and multi-agent system for PPHIIS assessment, an integrated information system for the health of People's Police (PPHIIS) was investigated, and an ontology model was designed and implemented to represent the health domain knowledge combined with the agents' cooperation, the SO s RCE-ontology model not only solved semantic problems about heterogeneous data but also enabled medical staff to extract detailed medical information more efficiently. Meanwhile, a level of abstraction at which we envisage integrated systems carrying out cooperative work by inter-operating globally across networks connecting police, hospitals and terminals was offered by the multi-agent view.Index terms-multi-agent, heterogeneous, shared, PPHIIS, SO s RCE-ontology.

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Section: Methodsmentioning

confidence: 99%

Practical Applications of Ontology-based multi-agent systems for PPHIIS

Li¹,

Liu²,

Fu³

et al. 2014

JSW

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“…Blitzer et al [8] investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. Prabowo and Thelwall [9] conducted experiments on movie reviews, product reviews and Myspace comments by combining a rule-based classifier and supervised learning algorithm, and found that the hybrid classification showed an improvement in accuracy. Machine learning approaches work well in situations where large labeled corpora are available for training and validation [10].…”

Section: Related Workmentioning

confidence: 99%

Feature-based Sentiment Analysis Approach for Product Reviews

Wang¹,

Liu²,

Song³

et al. 2014

JSW

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The researches and applications of sentiment analysis become increasingly important with the rapid growth of online reviews. But traditional sentiment analysis models have been lacking in concern on the modifying relationship between words for sentiment analysis of Chinese reviews, and limit the development of opinion mining. This paper proposes a feature-based vector model and a novel weighting algorithm for sentiment analysis of Chinese product reviews. It considers both modifying relationships between words and punctuations in review texts. Specifically, it can classify reviews into two categories, i.e., positive and negative, and can also represent the sentiment strength by adverb of degree. Moreover, a novel feature extraction method based on dependency parsing is presented to identify the corresponding aspects that opinions words modify. We conduct some experiments to evaluate our algorithms, and demonstrate that the proposed approaches are efficient and promising. Index Terms-sentiment analysis, dependency parsing, polarity classification * Supported by the Beijing Natural Science Foundation (No. 4133084) and the Beijing Key Disciplines of Computer Application Technology.

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“…The ontology is a formal knowledge representation method to facilitate human and computer interactions and it can be expressed by using formal semantic markup languages such as RDF and OWL [1]. And in particular, domain ontology constructs the relationship among domain concepts and helps in determining the structure of knowledge [2].…”

Section: Introductionmentioning

confidence: 99%

The Opinion Mining Based on Fuzzy Domain Sentiment Ontology Tree for Product Reviews

Nie¹,

Liu²,

Wang³

et al. 2013

JSW

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In this paper, we present a novel method that integrates domain sentiment knowledge into the analysis approach to deal with feature-level opinion mining By constructing a domain ontology called Fuzzy Domain Sentiment Ontology Tree (FDSOT), we then utilize the prior sentiment knowledge of our ontology to achieve significantly accuracy in sentiment classification. Particularly, the FDSOT is the conceptual model which represents the semantic relation between features and sentiment words. The evaluation is based on the Chinese product reviews collected from 360buy.com 1. The experimental results demonstrate that our approach is able to automatically identify the domain-dependent polarity for a large subset of sentiment expression and effectively improve the performance of opinion mining. Index Terms-feature-sentiment pairs, product reviews ontology-based, sentiment classification * Supported by the Beijing Natural Science Foundation (No. 4133084) and the Beijing Key Disciplines of Computer Application Technology.

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Tag Ontology Automatic Building for Semantic Searching of Services: a Case Study on Mashup Services

Cited by 4 publications

References 20 publications

Practical Applications of Ontology-based multi-agent systems for PPHIIS

Practical Applications of Ontology-based multi-agent systems for PPHIIS

Feature-based Sentiment Analysis Approach for Product Reviews

The Opinion Mining Based on Fuzzy Domain Sentiment Ontology Tree for Product Reviews

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