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1. Knowledge authoring for rule-based reasoning

   Gao, Tiantian ; Fodor, Paul ; Kifer, Michael ( October 2018 , Ontologies, DataBases, and Applications of Semantics)

   Modern knowledge bases have matured to the extent of being capable of complex reasoning at scale. Unfortunately, wide deployment of this technology is still hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and skilled knowledge engineers are in short supply. A way around this problem could be to acquire knowledge from text. However, the current knowledge acquisition technologies for information extraction are not up to the task because logic reasoning systems are extremely sensitive to errors in the acquired knowledge, and existing techniques lack the required accuracy by too large of a margin. Because of the enormous complexity of the problem, controlled natural languages (CNLs) were proposed in the past, but even they lack high enough accuracy. Instead of tackling the general problem of text understanding, our interest is in a related, but different, area of knowledge authoring—a technology designed to enable domain experts to manually create formalized knowledge using CNL. Our approach adopts and formalizes the FrameNet methodology for representing the meaning, enables incrementally-learnable and explainable semantic parsing, and harnesses rich knowledge graphs like BabelNet in the quest to obtain unique, disambiguated meaning of CNL sentences. Our experiments show that this approach is 95.6% accurate in standardizing the semantic relations extracted from CNL sentences—far superior to alternative systems. 

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2. High accuracy question answering via hybrid controlled natural language

   Gao, Tiantian ; Fodor, Paul ; Kifer, Michael ( December 2018 , IEEE/WIC/ACM International Conference on Web Intelligence (WI2018))

   Knowledge representation and reasoning (KRR) is key to the vision of the intelligent Web. Unfortunately, wide deployment of KRR is hindered by the difficulty in specifying the requisite knowledge, which requires skills that most domain experts lack. A way around this problem could be to acquire knowledge automatically from documents. The difficulty is that, KRR requires high-precision knowledge and is sensitive even to small amounts of errors. Although most automatic information extraction systems developed for general text understandings have achieved remarkable results, their accuracy is still woefully inadequate for logical reasoning. A promising alternative is to ask the domain experts to author knowledge in Controlled Natural Language (CNL). Nonetheless, the quality of knowledge construc- tion even through CNL is still grossly inadequate, the main obstacle being the multiplicity of ways the same information can be described even in a controlled language. Our previous work addressed the problem of high accuracy knowledge authoring for KRR from CNL documents by introducing the Knowledge Au- thoring Logic Machine (KALM). This paper develops the query aspect of KALM with the aim of getting high precision answers to CNL questions against previously authored knowledge and is tolerant to linguistic variations in the queries. To make queries more expressive and easier to formulate, we propose a hybrid CNL, i.e., a CNL with elements borrowed from formal query languages. We show that KALM achieves superior accuracy in semantic parsing of such queries. 

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3. Knowledge Authoring with Factual English

   https://doi.org/10.4204/EPTCS.364.10  

   Wang, Yuheng ; Borca-Tasciuc, Giorgian ; Goel, Nikhil ; Fodor, Paul ; Kifer, Michael ( August 2022 , Electronic proceedings in theoretical computer science)

   Lierler, Yuliya ; Morales, Jose F ; Dodaro, Carmine ; Dahl, Veroniica ; Gebser, Martin ; Tekle, Tuncay (Ed.)

   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%. 

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4. Knowledge Authoring for Rules and Actions

   https://doi.org/10.1017/S1471068423000169  

   WANG, YUHENG ; FODOR, PAUL ; KIFER, MICHAEL ( July 2023 , Theory and Practice of Logic Programming)

   Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALM^FL, a successor of KALM, replaced CNL withfactualEnglish, which is much less restrictive and requires very little training from users. However, KALM^FLhas limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALM^RAto enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALM^RAachieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALM^RAachieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALM^RAby drawing attention to the problems faced by the recently made famous AI, ChatGPT.

    

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5. Knowledge-driven Natural Language Understanding of English Text and its Applications

   Basu, Kinjal ; Varanasi, Sarat C. ; Shakerin, Farhad ; Arias, Joaquin ; Gupta, Gopal ( May 2021 , Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by ``truly understanding'' the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy. 

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