  Knowledge representation and reasoning (KRR) systems represent knowledge as
collections of facts and rules. Like databases, KRR systems contain information
about domains of human activities like industrial enterprises, science, and
business. KRRs can represent complex concepts and relations, and they can query
and manipulate information in sophisticated ways. Unfortunately, the KRR
technology has been hindered by the fact that specifying the requisite
knowledge requires skills that most domain experts do not have, and
professional knowledge engineers are hard to find. One solution could be to
extract knowledge from English text, and a number of works have attempted to do
so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of
logical facts from unrestricted natural language is still too inaccurate to be
used for reasoning, while restricting the grammar of the language (so-called
controlled natural language, or CNL) is hard for the users to learn and use.
Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring
Logic Machine (KALM), have shown to have very high accuracy compared to others,
and a natural question is to what extent the CNL restrictions can be lifted. In
this paper, we address this issue by transplanting the KALM framework to a
neural natural language parser, mStanza. Here we limit our attention to
authoring facts and queries and therefore our focus is what we call factual
English statements. Authoring other types of knowledge, such as rules, will be
considered in our followup work. As it turns out, neural network based parsers
have problems of their own and the mistakes they make range from part-of-speech
tagging to lemmatization to dependency errors. We present a number of
techniques for combating these problems and test the new system, KALMFL (i.e.,
KALM for factual language), on a number of benchmarks, which show KALMFL
achieves correctness in excess of 95%.
