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        <title>Scientific Discovery as Link Prediction in Influence and Citation Graphs</title>
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    <abstract>We introduce a machine learning approach for the identification of “white spaces” in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as “CTCF activates FOXA1”, which were automatically extracted using open-domain machine reading. We model this prediction task using graph-based features extracted from the above influence graph, as well as from a citation graph that captures scientific communities. We evaluated the proposed approach through backtesting. Although the data is heavily unbalanced (50 times more negative examples than positives), our approach predicts which influence links will be discovered in the “near future” with a F1 score of 27 points, and a mean average precision of 68%.</abstract>
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%0 Conference Proceedings
%T Scientific Discovery as Link Prediction in Influence and Citation Graphs
%A Luo, Fan
%A Valenzuela-Escárcega, Marco A.
%A Hahn-Powell, Gus
%A Surdeanu, Mihai
%Y Glavaš, Goran
%Y Somasundaran, Swapna
%Y Riedl, Martin
%Y Hovy, Eduard
%S Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F luo-etal-2018-scientific
%X We introduce a machine learning approach for the identification of “white spaces” in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as “CTCF activates FOXA1”, which were automatically extracted using open-domain machine reading. We model this prediction task using graph-based features extracted from the above influence graph, as well as from a citation graph that captures scientific communities. We evaluated the proposed approach through backtesting. Although the data is heavily unbalanced (50 times more negative examples than positives), our approach predicts which influence links will be discovered in the “near future” with a F1 score of 27 points, and a mean average precision of 68%.
%R 10.18653/v1/W18-1701
%U https://aclanthology.org/W18-1701
%U https://doi.org/10.18653/v1/W18-1701
%P 1-6

Markdown (Informal)

[Scientific Discovery as Link Prediction in Influence and Citation Graphs](https://aclanthology.org/W18-1701) (Luo et al., TextGraphs 2018)

• Scientific Discovery as Link Prediction in Influence and Citation Graphs (Luo et al., TextGraphs 2018)

ACL

• Fan Luo, Marco A. Valenzuela-Escárcega, Gus Hahn-Powell, and Mihai Surdeanu. 2018. Scientific Discovery as Link Prediction in Influence and Citation Graphs. In Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12), pages 1–6, New Orleans, Louisiana, USA. Association for Computational Linguistics.