Trying to learn more about Answer / Inference engines [closed]

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I'm trying to learn more about how answer / inference engines work, the code behind it.

Are there any famous or well done algorithms, good books, or papers on this topic?

How do systems like Google Now ( The answer not predictive part ), Siri, and Wolfram | Alpha work?

I know they use Natural Language Processing and Machine Leaning, but how do they answer questions based from a collection of knowledge / facts?

Answer 1

Question/answering is not done with one algorithm. It can be a combination of NLP algorithms such as part of speech tagging, semantic analysis, semantic and/or lexical parsing etc. Then many approaches can be used like supervised learning, clustering or just storing the information and indexing them.

Can you maybe explain what you are trying to do?

Answer 2

You ask a very broad question. There are many implementations of inference engines, but they would all rely on natural language processing and searching algorithms at their core so I would focus on that.

Try the book Artifical Intelligence : A Modern Approach. It has sections on both NLP and Search and is very good.

Answer 3

This task is called Question Answering. A few years ago there was an annual competition for it: the data are still available, and widely used in research papers.