OntoBuilder: fully automatic extraction and consolidation of ontologies from Web sources

Gal, Avigdor; Modica, Giovanni A.; Jamil, Hasan M.

doi:10.1109/icde.2004.1320082

Cited by 27 publications

(18 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such information is quite important for precisely representing a search interface as well as for other purposes such as automatic interface integration and query mapping/submission. We also note that Ontobuilder [4] supports the automatic extraction of ontologies from Web search interfaces. However, we found out that the ontologies are only limited to the properties of labels and elements themselves, but other semantic/meta-information such as value type is not identified.…”

Section: Related Workmentioning

confidence: 99%

“…Among the 184 real Web search interfaces we collected and investigated, 50 interfaces contain exclusive attributes; in particular, more than 34% of the interfaces in books, movies and music domains have exclusive attributes. A significant flaw of existing approaches [4,10,12,17,21] for interface extraction is that they do not extract exclusive attributes.…”

Section: Identifying the Names Of Exclusive Attributesmentioning

confidence: 99%

See 1 more Smart Citation

Towards Deeper Understanding of the Search Interfaces of the Deep Web

Meng

et al. 2007

World Wide Web

View full text Add to dashboard Cite

Many databases have become Web-accessible through form-based search interfaces (i.e., HTML forms) that allow users to specify complex and precise queries to access the underlying databases. In general, such a Web search interface can be considered as containing an interface schema with multiple attributes and rich semantic/metainformation; however, the schema is not formally defined in HTML. Many Web applications, such as Web database integration and deep Web crawling, require the construction of the schemas. In this paper, we first propose a schema model for representing complex search interfaces, and then present a layout-expression based approach to automatically extract the logical attributes from search interfaces. We also rephrase the identification of different types of semantic information as a classification problem, and design several Bayesian classifiers to help derive semantic information from extracted attributes. A system, WISE-iExtractor, has been implemented to automatically construct the schema from any Web search interfaces. Our experimental results on real search interfaces indicate that this system is highly effective.World Wide Web (

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Identifying the Names Of Exclusive Attributesmentioning

confidence: 99%

Towards Deeper Understanding of the Search Interfaces of the Deep Web

Meng

et al. 2007

World Wide Web

View full text Add to dashboard Cite

show abstract

“…In general, there is an effort to recover as much of the semantics as possible from many different source genres. For example, researchers have investigated semantic recovery from relational databases [5,6], XML [1,38], human-readable tables [24,25,34], forms [22,31], and free-running text [10].…”

Section: Theorem 3 Let S Be a Relational Database With Its Schema Rementioning

confidence: 99%

Theoretical Foundations for Enabling a Web of Knowledge

Embley

Zitzelberger

2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The current web is a web of linked pages. Frustrated users search for facts by guessing which keywords or keyword phrases might lead them to pages where they can find facts. Can we make it possible for users to search directly for facts embedded in web pages? Instead of a web of human-readable pages containing machine-inaccessible facts, can the web be a web of machine-accessible facts superimposed over a web of human-readable pages? Ultimately, can the web be a web of knowledge that can provide direct answers to factual questions and support these answers by referencing and highlighting relevant base facts embedded in source pages? Answers to these questions call for distilling knowledge from the web's wealth of heterogeneous digital data into a web of knowledge. But how? Or, even more fundamentally, what, precisely, is this web of knowledge, and what is required to enable it? To answer these questions, we proffer a theoretical foundation for a web of knowledge: We formally define a computational view of knowledge in a way that enables practical construction and use of a web of knowledge.

show abstract

“…In our previous work [7,[16][17][18][19][20]28,32,69], we have implemented a number of prototype systems such as WebFusion, PickUp, FastWrap, OntoBuilder, OntoMatch, etc., that are much more scalable approaches toward Web data integration. .1 shows our overall envisioned system, resource capability discovery/description management system.…”

Section: Web Data Extraction and Labelingmentioning

confidence: 99%