English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

Using Noisy Extractions to Discover Causal Knowledge