Chapter 20 Knowledge Representation and Question Answering

Abstract

Publisher Summary A search engine or a typical information retrieval (IR) system, such as Google, does not go far enough as it takes keywords and only gives a ranked list of documents that may contain those keywords. Often this list is very long, and an analyst might have to read the documents in the list. Other reasons behind the unsuitability of an IR system (for an analyst) are that the nuances of a question in a natural language cannot be adequately expressed through keywords, most IR systems ignore synonyms, and most IR systems cannot reason. There should be a system that can take the documents and the analyst's question as input, access the data in fact books, and do commonsense reasoning based on them to provide answers to questions. Such a system is referred to as a “question answering system” or a “QA system.” A precursor to question answering is database querying where one queries a database using a database query language. Question answering takes this to a whole new dimension where the system has a huge body of documents (in natural languages, possibly including multimedia objects, situated in the Web and described in a Web language), and it is asked a query in a natural language. It is expected to give an answer to the question not only using the documents but also using appropriate commonsense knowledge. The system needs to be able to accommodate new additions to the body of documents.