Abstract. Our work is driven by the hypothesis that for a program to answer questions, explain the answers, and engage in a dialog just like a human does, it must have an explicit representation of knowledge. Such explicit representations occur naturally in many situations such as engineering designs created by engineers, a software requirement created in unified modeling language or a process flow diagram for a manufacturing process. Automated approaches based on natural language processing have progressed on tasks such as named entity recognition, fact extraction and relation learning. Use of automated methods can be problematic in situations where the conceptual distinctions used by humans for reasoning are not directly expressed in natural language or when the representation must be used to drive a high fidelity simulation. In this paper, we report on our effort to systematically curate a knowledge base for substantial fraction of text in a biology textbook [26]. While this experience and the process is interesting on its own, three aspects can be especially instructive for future development of knowledge bases by both manual and automatic methods: (1) Consider imposing a simplifying abstract structure on natural language sentences so that the surface form is closer to the target logical form to be extracted. (2) Adopt an upper ontology that is strongly motivated and influenced by natural language. (3) Develop a set of guidelines that captures how the conceptual distinctions in the ontology may be realized in natural language. Since the representation created by this process has been quite effective for answering questions and producing explanations, it gives a concrete target for what information should be extracted by the automated methods.

( † = corresponding author) We address two problems in underspecified graph-structured knowledge bases (GSKBs): the co-reference and the provenance problem. Both prob-lems are important for a variety of reasons. The for-mer asks “Which existentially quantified variables in different but related axioms of a GSKB possibly denote identical domain individuals?”, and the lat-ter “From which axioms in a GSKB is a piece of knowledge getting derived? ” To decide the former, we need to be able to prove equality between dif-ferent variables – a GSKB in which this is possible is called a strenghtened GSKB, and an underspec-ified GSKB otherwise. The latter occur naturally in many knowledge acquisition contexts, and are also easier to author. We hence present an algo-rithm which rewrites an underspecified GSKB into a strengthened GSKB, by virtue of Skolemization and addition of equality atoms such that the co-reference information can be drawn from it. This enlarges the logical theory (the deductive closure) of the GSKB and strengthens its inferential power, hence affecting the provenance information. Our algorithm is model-theoretic in nature and exploits a novel class of desirable, preferred models, which capture the desired co-references. The algorithm is a logical reconstruction of an implemented algo-rithm that we successfully applied to a large-scale biological knowledge base, in which it identified more that 22,000 equality atoms. 1

Our work is driven by the hypothesis that, for a program to answer questions, explain the answers, and engage in a dialog just as a human does, it must have an explicit representation of knowledge. Such explicit representations naturally occur in many situations such as in designs created by en-gineers, software requirements created in a unified modeling language or process flow diagrams created for a manufacturing process. Automated approaches based on natural language processing have progressed on tasks such as named entity recognition, fact extraction and relation learning, but they cannot generate expressive representations with high accuracy. In this paper, we report on our effort to systematically curate a knowledge base for a substantial fraction of a biology textbook. Although this experience and the process inherently offer insights, three aspects are especially in-structive for the future development of knowledge bases both by manual and by automatic methods: (1) Consider imposing a simplifying abstract structure on natural language sentences so that the sur-face form is closer to the target logical form to be extracted; (2) Adopt an upper ontology that is strongly motivated and influenced by natural language; (3) Develop a set of syntactic and semantic guidelines that captures how the conceptual distinctions in the ontology may be realized in natural language. Because this representation has effectively enabled reasoning, explanation and dialog, it gives a concrete target for what should be learned by automated methods. 1.

Abstract. Continuity and change is a core theme in biology that refers to how genetic information is carried forward. This paper reports on our initial steps to-ward representing this core theme and describes the methodological background and open challenges. We define continuity and change from a conceptual mod-eling perspective, identify its facets that require further ontological work, and present competency questions designed to check the adequacy of its represen-tation. Moreover, we explore whether continuity and change must be explicitly represented as primitives in the representation of biological processes or whether it can be inferred from the process structure.