Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader's background knowledge. One example is the task of interpreting regulations to answer "Can I...?" or "Do I have to...?" questions such as "I am working in Canada. Do I have to carry on paying UK National Insurance?" after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as "How long have you been working abroad?" when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 32k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.
The majority of knowledge on the Web is encoded in unstructured text and is not linked to formalized knowledge, such as ontologies and rules. A potential solution to this problem is to acquire this knowledge through natural language processing and text mining methods. Prior work has focused on automatically extracting RDF- or OWL-based ontologies from text; however, the type of knowledge acquired is generally restricted to simple term hierarchies. This paper presents a general-purpose framework for acquiring more complex relationships from text and then encoding this knowledge as rules. Our approach starts with existing domain knowledge in the form of OWL ontologies and Semantic Web Rule Language (SWRL) rules and applies natural language processing and text matching techniques to deduce classes and properties. It then captures deductive knowledge in the form of new rules. We have evaluated our framework by applying it to web-based text on car rental requirements. We show that our approach can automatically and accurately generate rules for requirements of car rental companies not in the knowledge base. Our framework thus rapidly acquires complex knowledge from free text sources. We are expanding it to handle richer domains, such as medical science. The paper describes a system that extracts knowledge from technical English texts. Our basic assumption is that in technical texts syntax is a reliable indication of meaning. Consequently, semantic interpretation of the text starts from surface syntax. The linguistic component of the system uses a broad-coverage, domainindependent parser of English, as well as a user-assisted semantic interpreter that memorizes its experience. The resulting semantic structures are translated into Horn clauses, a representation suitable for Explanation-based Learning (EBL). An EBL engine performs symbollevel learning on representations of both the domain theory and the example provided by the linguistic part of the system. Our approach has been applied to the Canadian Individual Income Tax Guide and examples from it are used in the presentation.  The Systeme d'Acquisition des Connaissances Deontique (SACD), part of the Acquisition des Connaissances et Analyse de Textes project, which can generate a knowledge base from the logical structure of regulatory texts, is described. SACD can be used by domain specialists who know the text, characteristics and the intricacies of the legislation, or by knowledge engineers who know SACD and can modify the grammars. SACD performs all syntactic text processing and automatically manipulates the knowledge structures. The specialist interprets all semantic knowledge and performs any tasks requiring domain experience. The knowledge engineer adapts SACD's knowledge structures when the system behaves inaccurately. The logical context of regulatory texts is reviewed, and the text analysis, knowledge base operation, and knowledge base adaptation procedures of SACD are discussed. >