Trend graphs: Visualizing the evolution of concept relationships in large document collections

Ronen Feldman; Yonatan Aumann; Amir Zilberstein; Yaron Ben-Yehuda

doi:10.1007/bfb0094803

Trend graphs: Visualizing the evolution of concept relationships in large document collections

Ronen Feldman, Yonatan Aumann, Amir Zilberstein, Yaron Ben-Yehuda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

The proliferation of digitally available textual data necessitates automatic tools for analyzing large textual collections. Thus, in analogy to data mining for structured databases, text mining is defined for textual collections. A central tool in text mining is the analysis of concept relationship, which discovers connections between different concepts, as reflected in the corpus. Most previous work on text mining in general, and concept relationship in particular, viewed the entire corpus as one monolithic entity. However, large corpuses are often composed of documents with different characteristics. Most importantly, documents are often tagged with timestamps (e.g. news articles), and thus represent the state of the domain in different time periods. In this paper we introduce a new technique for analyzing and visualizing differences and similarities in the concept relationships, as they are reflected in different segments of the corpus. Focusing on the case of timestamped documents, we introduce Trend Graphs, which provide a graphical tool for analyzing and visualizing the dynamic changes in concept relationships over time. Trend Graphs thus provide a tool for tracking the evaluation of the corpus over time, highlighting trends and discontinuities.

Original language	English
Title of host publication	Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings
Editors	Jan M. Zytkow, Mohamed Quafafou
Publisher	Springer Verlag
Pages	38-46
Number of pages	9
ISBN (Print)	3540650687, 9783540650683
DOIs	https://doi.org/10.1007/bfb0094803
State	Published - 1998
Externally published	Yes
Event	2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998 - Nantes, France Duration: 23 Sep 1998 → 26 Sep 1998

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1510
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998
Country/Territory	France
City	Nantes
Period	23/09/98 → 26/09/98

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.

Access to Document

10.1007/bfb0094803

Cite this

Feldman, R., Aumann, Y., Zilberstein, A., & Ben-Yehuda, Y. (1998). Trend graphs: Visualizing the evolution of concept relationships in large document collections. In J. M. Zytkow, & M. Quafafou (Eds.), Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings (pp. 38-46). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1510). Springer Verlag. https://doi.org/10.1007/bfb0094803

Feldman, Ronen ; Aumann, Yonatan ; Zilberstein, Amir et al. / Trend graphs : Visualizing the evolution of concept relationships in large document collections. Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. editor / Jan M. Zytkow ; Mohamed Quafafou. Springer Verlag, 1998. pp. 38-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The proliferation of digitally available textual data necessitates automatic tools for analyzing large textual collections. Thus, in analogy to data mining for structured databases, text mining is defined for textual collections. A central tool in text mining is the analysis of concept relationship, which discovers connections between different concepts, as reflected in the corpus. Most previous work on text mining in general, and concept relationship in particular, viewed the entire corpus as one monolithic entity. However, large corpuses are often composed of documents with different characteristics. Most importantly, documents are often tagged with timestamps (e.g. news articles), and thus represent the state of the domain in different time periods. In this paper we introduce a new technique for analyzing and visualizing differences and similarities in the concept relationships, as they are reflected in different segments of the corpus. Focusing on the case of timestamped documents, we introduce Trend Graphs, which provide a graphical tool for analyzing and visualizing the dynamic changes in concept relationships over time. Trend Graphs thus provide a tool for tracking the evaluation of the corpus over time, highlighting trends and discontinuities.",

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Feldman, R, Aumann, Y, Zilberstein, A & Ben-Yehuda, Y 1998, Trend graphs: Visualizing the evolution of concept relationships in large document collections. in JM Zytkow & M Quafafou (eds), Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1510, Springer Verlag, pp. 38-46, 2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998, Nantes, France, 23/09/98. https://doi.org/10.1007/bfb0094803

Trend graphs: Visualizing the evolution of concept relationships in large document collections. / Feldman, Ronen; Aumann, Yonatan; Zilberstein, Amir et al.
Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. ed. / Jan M. Zytkow; Mohamed Quafafou. Springer Verlag, 1998. p. 38-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1510).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Feldman R, Aumann Y, Zilberstein A, Ben-Yehuda Y. Trend graphs: Visualizing the evolution of concept relationships in large document collections. In Zytkow JM, Quafafou M, editors, Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. Springer Verlag. 1998. p. 38-46. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/bfb0094803

Trend graphs: Visualizing the evolution of concept relationships in large document collections

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