  Knowledge representation and reasoning (KRR) is one of the key areas in
artificial intelligence (AI) field. It is intended to represent the world
knowledge in formal languages (e.g., Prolog, SPARQL) and then enhance the
expert systems to perform querying and inference tasks. Currently, constructing
large scale knowledge bases (KBs) with high quality is prohibited by the fact
that the construction process requires many qualified knowledge engineers who
not only understand the domain-specific knowledge but also have sufficient
skills in knowledge representation. Unfortunately, qualified knowledge
engineers are in short supply. Therefore, it would be very useful to build a
tool that allows the user to construct and query the KB simply via text.
Although there is a number of systems developed for knowledge extraction and
question answering, they mainly fail in that these system don't achieve high
enough accuracy whereas KRR is highly sensitive to erroneous data. In this
thesis proposal, I will present Knowledge Authoring Logic Machine (KALM), a
rule-based system which allows the user to author knowledge and query the KB in
text. The experimental results show that KALM achieved superior accuracy in
knowledge authoring and question answering as compared to the state-of-the-art
systems.
