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CauseNet: Towards a causality graph extracted from the web

(causenet.org)
229 points geetee | 2 comments | 02 Sep 25 05:26 UTC | HN request time: 0.001s | source
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tgv ◴[02 Sep 25 07:42 UTC] No.45100192[source]▶
This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way to express that myxomatosis is lethal for only European rabbits, not humans, nor gold fish.
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koliber ◴[02 Sep 25 07:55 UTC] No.45100256[source]▶
Exactly. In some cases disease causes death. In others it causes immunity which in turn causes “good health” and postpones death.
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1. Nevermark ◴[02 Sep 25 09:38 UTC] No.45100838[source]▶
Contradictory cause-effect examples, each backed up with data, are a reliable indicator of a class of situations that need a higher chain-effect resolution.

Which is directly usable knowledge if you are building out a causal graph.

In the meantime, a cause and effect representation isn't limited to only listing one possible effect. A list of alternate disjoint effects, linked to a cause, is also directly usable.

Just as an effect may be linked to different causes. Which if you only know the effect, in a given situation, and are trying to identify cause, is the same problem in reverse time.

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2. koliber ◴[02 Sep 25 17:22 UTC] No.45106148[source]▶
It is my opinion that if we examine any factor closely, it will have multiple disjoint effects. As in nothing is absolutely unilateral in its effects. Some of those effects will depend on certain conditions. If it is possible to specify condition, annotations, and other nuances such as levels of confidence or source of the opinion, such a database might be pretty useful.