This paper explores visualizations of document collections, which we call topic maps. Our topic maps are based on a topic model of the document collection, where the topic model is used to determine the semantic content of each document. Using two collections of search results, we show how topic maps reveal the semantic structure of a collection and visually communicate the diversity of content in the collection. We describe techniques for assessing the validity and accuracy of topic maps, and discuss the challenge of producing useful two-dimensional maps of documents.
Description
Visualizing search results and document collections using topic maps
Bridging the Gap—Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences
%0 Journal Article
%1 NewmanD2010b
%A Newman, David
%A Baldwin, Timothy
%A Cavedon, Lawrence
%A Huang, Eric
%A Karimi, Sarvnaz
%A Martinez, David
%A Scholer, Falk
%A Zobel, Justin
%D 2010
%J Web Semantics: Science, Services and Agents on the World Wide Web
%K dimensionality_reduction general_text_mining visualization
%N 2–3
%P 169 - 175
%R http://dx.doi.org/10.1016/j.websem.2010.03.005
%T Visualizing search results and document collections using topic maps
%U http://www.sciencedirect.com/science/article/pii/S1570826810000211
%V 8
%X This paper explores visualizations of document collections, which we call topic maps. Our topic maps are based on a topic model of the document collection, where the topic model is used to determine the semantic content of each document. Using two collections of search results, we show how topic maps reveal the semantic structure of a collection and visually communicate the diversity of content in the collection. We describe techniques for assessing the validity and accuracy of topic maps, and discuss the challenge of producing useful two-dimensional maps of documents.
@article{NewmanD2010b,
abstract = {This paper explores visualizations of document collections, which we call topic maps. Our topic maps are based on a topic model of the document collection, where the topic model is used to determine the semantic content of each document. Using two collections of search results, we show how topic maps reveal the semantic structure of a collection and visually communicate the diversity of content in the collection. We describe techniques for assessing the validity and accuracy of topic maps, and discuss the challenge of producing useful two-dimensional maps of documents. },
added-at = {2015-08-19T15:18:51.000+0200},
author = {Newman, David and Baldwin, Timothy and Cavedon, Lawrence and Huang, Eric and Karimi, Sarvnaz and Martinez, David and Scholer, Falk and Zobel, Justin},
biburl = {https://www.bibsonomy.org/bibtex/2c33dacc831d467b224c373ee012a1d74/lopusz_kdd},
description = {Visualizing search results and document collections using topic maps},
doi = {http://dx.doi.org/10.1016/j.websem.2010.03.005},
interhash = {8f8a601bc974ea75372855c28d6a40ac},
intrahash = {c33dacc831d467b224c373ee012a1d74},
issn = {1570-8268},
journal = {Web Semantics: Science, Services and Agents on the World Wide Web },
keywords = {dimensionality_reduction general_text_mining visualization},
note = {Bridging the Gap—Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences },
number = {2–3},
pages = {169 - 175},
timestamp = {2015-08-19T16:51:00.000+0200},
title = {Visualizing search results and document collections using topic maps },
url = {http://www.sciencedirect.com/science/article/pii/S1570826810000211},
volume = 8,
year = 2010
}