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Andrew W. Hogue

dblp:58/8717 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none

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

Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 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.

Artificial intelligence
1 paper
Information extraction and text analysis · 100%
Databases, data mining, and information retrieval
1 paper
Knowledge graphs · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
web information extraction
0.112005
Thresher: automating the unwrapping of semantic content from the World Wide Web · WWW 2005
Natural language and speech › Information extraction and text analysis › web information extraction
wrapper induction
0.112005
Thresher: automating the unwrapping of semantic content from the World Wide Web · WWW 2005
Knowledge graphs
semantic web
0.112005
Thresher: automating the unwrapping of semantic content from the World Wide Web · WWW 2005

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

tree edit distance · 0.1DOM subtree matching · 0.1
YearPublicationVenuePosition
2005 Thresher: automating the unwrapping of semantic content from the World Wide Web
abstract
We describe Thresher, a system that lets non-technical users teach their browsers how to extract semantic web content from HTML documents on the World Wide Web. Users specify examples of semantic content by highlighting them in a web browser and describing their meaning. We then use the tree edit distance between the DOM subtrees of these examples to create a general pattern, or wrapper, for the content, and allow the user to bind RDF classes and predicates to the nodes of these wrappers. By overlaying matches to these patterns on standard documents inside the Haystack semantic web browser, we enable a rich semantic interaction with existing web pages, "unwrapping" semantic data buried in the pages' HTML. By allowing end-users to create, modify, and utilize their own patterns, we hope to speed adoption and use of the Semantic Web and its applications.
Andrew W. Hogue, David R. Karger
WWW1