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Thomas Kabisch

dblp:32/5626 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Data integration and cleaning · 67% Information retrieval · 29% Data models and query languages · 4%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning › web data integration
deep web integration
0.112010
Deep Web Integration with VisQI · Proc. VLDB Endow. 2010
Data integration and cleaning
schema matching
0.112010
Deep Web Integration with VisQI · Proc. VLDB Endow. 2010
Information retrieval › search engines › web crawling
deep web crawling
0.112009
A Hierarchical Approach to Model Web Query Interfaces for Web Source Integration · Proc. VLDB Endow. 2009
Data integration and cleaning › data extraction › web data extraction
query interface extraction
0.112009
A Hierarchical Approach to Model Web Query Interfaces for Web Source Integration · Proc. VLDB Endow. 2009
Data integration and cleaning
web data integration
0.112009
A Hierarchical Approach to Model Web Query Interfaces for Web Source Integration · Proc. VLDB Endow. 2009
Information retrieval › search interfaces
web query interface
0.112009
A Hierarchical Approach to Model Web Query Interfaces for Web Source Integration · Proc. VLDB Endow. 2009

Methods — techniques the papers use, named apart from their topics

tree merging · 0.1geometric layout analysis · 0.1
YearPublicationVenuePosition
2010 Deep Web Integration with VisQI
abstract
In this paper, we present VisQI (VISual Query interface Integration system), a Deep Web integration system. VisQI is capable of (1) transforming Web query interfaces into hierarchically structured representations, (2) of classifying them into application domains and (3) of matching the elements of different interfaces. Thus VisQI contains solutions for the major challenges in building Deep Web integration systems. The system comes along with a full-fledged evaluation system that automatically compares generated data structures against a gold standard. VisQI has a framework-like architecture such that other developers can reuse its components easily.
Thomas Kabisch, Eduard C. Dragut, Clement T. Yu, Ulf Leser
Proc. VLDB Endow.1
2009 A Hierarchical Approach to Model Web Query Interfaces for Web Source Integration
abstract
Much data in the Web is hidden behind Web query interfaces. In most cases the only means to "surface" the content of a Web database is by formulating complex queries on such interfaces. Applications such as Deep Web crawling and Web database integration require an automatic usage of these interfaces. Therefore, an important problem to be addressed is the automatic extraction of query interfaces into an appropriate model. We hypothesize the existence of a set of domain-independent "commonsense design rules" that guides the creation of Web query interfaces. These rules transform query interfaces into schema trees. In this paper we describe a Web query interface extraction algorithm, which combines HTML tokens and the geometric layout of these tokens within a Web page. Tokens are classified into several classes out of which the most significant ones are text tokens and field tokens. A tree structure is derived for text tokens using their geometric layout. Another tree structure is derived for the field tokens. The hierarchical representation of a query interface is obtained by iteratively merging these two trees. Thus, we convert the extraction problem into an integration problem. Our experiments show the promise of our algorithm: it outperforms the previous approaches on extracting query interfaces on about 6.5% in accuracy as evaluated over three corpora with more than 500 Deep Web interfaces from 15 different domains.
Thomas Kabisch, Eduard C. Dragut, Clement T. Yu, Ulf Leser
Proc. VLDB Endow.1