2007 InformationGenealogy

Subject Headings: citation inference, flow of ideas, information genealogy, language models, temporal data, text mining

Notes

We now have incrementally-grown databases of text documents ranging back for over a decade in areas ranging from personal email, to news-articles and conference proceedings. While accessing individual documents is easy, methods for overviewing and understanding these collections as a whole are lacking in number and in scope. In this paper, we address one such global analysis task, namely the problem of automatically uncovering how ideas spread through the collection over time. We refer to this problem as Information Genealogy. In contrast to bibliometric methods that are limited to collections with explicit citation structure, we investigate content-based methods requiring only the text and timestamps of the documents. In particular, we propose a language-modeling approach and a likelihood ratio test to detect influence between documents in a statistically well-founded way. Furthermore, we show how this method can be used to infer citation graphs and to identify the most influential documents in the collection. Experiments on the NIPS conference proceedings and the Physics ArXiv show that our method is more effective than methods based on document similarity.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 InformationGenealogy	Thorsten Joachims Benyah Shaparenko			Information Geneology: Uncovering the flow of ideas in non-hyperlinked document databases			http://www.cs.cornell.edu/People/tj/publications/shaparenko joachims 07a.pdf	10.1145/1281192.1281259