iASA: Learning to Annotate the Semantic Web

Tang, Jie; Li, Juanzi; Lü, Hongjun; Liang, Bin; Huang, Xiaotong; Wang, Kehong

doi:10.1007/11603412_4

Cited by 20 publications

(10 citation statements)

References 14 publications

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“…Many desktop systems for structural extraction have been developed in recent years [9,10,11]. However, Web browsers are used much more than desktop applications in today's IT environment.…”

Section: Related Workmentioning

confidence: 99%

Page query language generation for structural extraction

2014

2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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The information on the Web is usually fabricated to be understandable by human users rather than machines. It's not easy to automatically catalogue and extract the Web information solely with a software agent. Based on these observations, we present an approach that uses human guided operations to automatically generate a PQL query, a SQL like query language focusing on Web pages, to extract the interested information fragments on Web pages. The PQL query uses XPath expressions to locating the target HTML nodes. We develop a K-Medoid clustering algorithm to process PQL queries to generate the structural extractions. The extracted information is structured as a relational table (in CSV format) which can be manipulated smoothly with spreadsheet software or a relational DBMS system.

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“…Many desktop systems for structural extraction have been developed in recent years [9,10,11]. However, Web browsers are used much more than desktop applications in today's IT environment.…”

Section: Related Workmentioning

confidence: 99%

Page query language generation for structural extraction

2014

2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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“…The past few years have also seen a growing interest iASA [12] is a semantic annotation tool based on information extraction and machine learning techniques. It uses annotated documents to construct initial rule set.…”

Section: Related Workmentioning

confidence: 99%

BNOSA: A Bayesian network and ontology based semantic annotation framework

Rajput¹,

Haider²

2011

Journal of Web Semantics

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“…Because different kinds of information have different features in Web page, we use different semantic annotation methods for extraction [35,36]. We do not extract publication information from Web pages; instead, we use the publication information in DBLP.…”

Section: Web Page Annotationmentioning

confidence: 99%

Arnetminer: expertise oriented search using social networks

Tang

Zhang

et al. 2008

Front. Comput. Sci. China

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Expertise Oriented Search (EOS) aims at providing comprehensive expertise analysis on data from distributed sources. It is useful in many application domains, for example, finding experts on a given topic, detecting the confliction of interest between researchers, and assigning reviewers to proposals. In this paper, we present the design and implementation of our expertise oriented search system, Arnetminer (http://www.arnetminer.net). Arnetminer has gathered and integrated information about a half-million computer science researchers from the Web, including their profiles and publications. Moreover, Arnetminer constructs a social network among these researchers through their co-authorship, and utilizes this network information as well as the individual profiles to facilitate expertise oriented search tasks. In particular, the co-authorship information is used both in ranking the expertise of individual researchers for a given topic and in searching for associations between researchers. We have conducted initial experiments on Arnetminer. Our results demonstrate that the proposed relevancy propagation expert finding method outperforms the method that only uses person local information, and the proposed twostage association search on a large-scale social network is order of magnitude faster than the baseline method.

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iASA: Learning to Annotate the Semantic Web

Cited by 20 publications

References 14 publications

Page query language generation for structural extraction

Page query language generation for structural extraction

BNOSA: A Bayesian network and ontology based semantic annotation framework

Arnetminer: expertise oriented search using social networks

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