AIML for Intelligent Answering Engine

Question

I have heard about a programming language called AIML which can be used for programming Intelligent Robots. I am a web developer and have a web crawler build using Python 2.7 and have indexed Wikipedia...

So, I wanted to build an answering engine using python which would use a string variable (It is a HUGE variable containing the whole of Wikipedia) as a source of information and use AI to answer...

Finally, I wanted to put this up on my school website...

So can I do that in AIML?

Later on, I also want to modify it so as to give my live scores answers to questions like:

"What is the age of ~someperson~?" etc. For that, I'll send my web crawler to index some score pages, etc.

Can I program this sort of answering agent in AIML?

If yes please provide links to tutorials that tell me how to do that? (using string variables as a source of information to parse queries and answer like a human)

moreover, AIML uses syntax like:

<category>

    <pattern>WHAT ARE YOU</pattern>

    <template>

        <think><set name="topic">Me</set></think> 

        I am the latest result in artificial intelligence,

        which can reproduce the capabilities of the human brain

        with greater speed and accuracy.

    </template>

</category>

Where a pattern is a query and template is the answer, so does that mean I have to sit and write these tags for all possible queries?

Or can I make it use its brains to figure out what the person wants and give them answers using the string variable as its source of information?

Thank you.

Answer 1

It seems like AIML is a form of pattern matching. Further, it looks like this is mainly meant for dialog-based agents. Hence, to use AIML, you would likely need to manually generate every question and the correct response (answer).

Question answering

What it looks like you are literally after is what we call a question answering system. Very shortly, a QA system generally has these components:

• Question analysis.

• Extract keywords.

• (Sometimes) determine the expected answer type (location, person, color, number, etc.).

• Candidate document selection: performing a search on your knowledge base using an information retrieval system.

• Candidate document analysis.

• Answer extraction: select some part of the data(sentence(s), paragraph(s)).

• Response generation: 

1. Scores and ranks each answer.

2. Displays the most confident answer(s).

For more information regarding AIML Based Voice-Enabled Artificial Intelligent Chatterbot, refer to the following link: https://www.researchgate.net/publication/273643970_AIML_Based_Voice_Enabled_Artificial_Intelligent_Chatterbot

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