Dhanya Sridhar

Assistant Professor

Curriculum vitae

dhanya.sridhar <at> mila.quebec


University of Montreal, Mila

F.04, 6666 Rue St. Urbain

Using Noisy Extractions to Discover Causal Knowledge

Journal article

Dhanya Sridhar, J. Pujara, L. Getoor

Semantic Scholar ArXiv DBLP


APA   Click to copy
Sridhar, D., Pujara, J., & Getoor, L. (2017). Using Noisy Extractions to Discover Causal Knowledge. AKBC@NIPS.

Chicago/Turabian   Click to copy
Sridhar, Dhanya, J. Pujara, and L. Getoor. “Using Noisy Extractions to Discover Causal Knowledge.” AKBC@NIPS (2017).

MLA   Click to copy
Sridhar, Dhanya, et al. “Using Noisy Extractions to Discover Causal Knowledge.” AKBC@NIPS, 2017.

BibTeX   Click to copy

  title = {Using Noisy Extractions to Discover Causal Knowledge},
  year = {2017},
  journal = {AKBC@NIPS},
  author = {Sridhar, Dhanya and Pujara, J. and Getoor, L.}


Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning. In this work, we study a particular reasoning task, the problem of discovering causal relationships between entities, known as causal discovery. There are two contrasting types of approaches to discovering causal knowledge. One approach attempts to identify causal relationships from text using automatic extraction techniques, while the other approach infers causation from observational data. However, extractions alone are often insufficient to capture complex patterns and full observational data is expensive to obtain. We introduce a probabilistic method for fusing noisy extractions with observational data to discover causal knowledge. We propose a principled approach that uses the probabilistic soft logic (PSL) framework to encode well-studied constraints to recover long-range patterns and consistent predictions, while cheaply acquired extractions provide a proxy for unseen observations. We apply our method gene regulatory networks and show the promise of exploiting KB signals in causal discovery, suggesting a critical, new area of research.


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