Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
The STARLIGHT information visualization system
IV '97 Proceedings of the IEEE Conference on Information Visualisation
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Semiology of graphics
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Measures of semantic similarity and relatedness in the biomedical domain
Journal of Biomedical Informatics
Towards a general theory of geographic representation in GIS
International Journal of Geographical Information Science
Modelling vague places with knowledge from the Web
International Journal of Geographical Information Science - Digital Gazetteer Research
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Conversation clusters: grouping conversation topics through human-computer dialog
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploratory Search
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Knowledge derived from wikipedia for computing semantic relatedness
Journal of Artificial Intelligence Research
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Search User Interfaces
Automatically creating datasets for measures of semantic relatedness
LD '06 Proceedings of the Workshop on Linguistic Distances
Kosmix: high-performance topic exploration using the deep web
Proceedings of the VLDB Endowment
Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words
Natural Language Engineering
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Liquid query: multi-domain exploratory search on the web
Proceedings of the 19th international conference on World wide web
A Wikipedia-based multilingual retrieval model
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Spatiotemporal mapping of Wikipedia concepts
Proceedings of the 10th annual joint conference on Digital libraries
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A word at a time: computing word relatedness using temporal semantic analysis
Proceedings of the 20th international conference on World wide web
Methods for exploring and mining tables on Wikipedia
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
Proceedings of the 2013 workshop on Automated knowledge base construction
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Hi-index | 0.00 |
Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram -- from a periodic table, to a parliamentary seating chart, to a world map -- as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.