Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.

BackgroundUsing knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?ResultsOur existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.ConclusionsWith some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels

Naturalness vs. Predictability: A Key Debate in Controlled Languages

Abstract. In this paper we describe two quite different philosophies used in developing controlled languages (CLs): A "naturalist " approach, in which CL interpretation is treated as a simpler form of full natural language processing; and a "formalist " approach, in which the CL interpretation is “deterministic” (context insensitive) and the CL is viewed more as an English-like formal specification language. Despite the philosophical and practical differences, we suggest that a synthesis can be made in which a deterministic core is embedded in a naturalist CL, and illustrate this with our own controlled language CPL. In the second part of this paper we present a fictitious debate between an ardent “naturalist ” and an ardent “formalist”, each arguing their respective positions, to illustrate the benefits and tradeoffs of these different philosophies in an accessible way. Part I: The Naturalist vs. Formalist Debate

Answering Comparison Questions in SHAKEN: A Progress Report

Abstract An important class of questions for knowledge based systems concern comparisons, such as "How is X like r." and "How are X and Y different?" This paper describes how we have used a cognitive simulation of analogical processing to answer such questions, to support domain experts in entering new knowledge. We outline techniques for case construction and summarization of comparison results that have been developed and refined based on an independent formative evaluation. In addition to these techniques, we discuss the role of the comparison system in SHAKEN, the larger system in which they are embedded, and our plans for further improvements

Mapping between the OBO and OWL ontology languages

Syed Hamid Tirmizi, Juan Sequeda, and Daniel P. Miranker are with the Department of Computer Science, The University of Texas at Austin, Austin, Texas 78701, USA -- Stuart Aitken is with the Artificial Intelligence Applications Institute, The University of Edinburgh, Edinburgh EH8 9LE, UK and the Informatics Life-Sciences Institute, The University of Edinburgh, Edinburgh EH8 9LE, UK -- Dilvan A. Moreira is with the Department of Computer Science, Mathematics and Computing Institute, University of São Paulo, São Carlos, São Paulo, Brazil -- Chris Mungall is with the Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA -- NIgam H. Shah is with the Center for Biomedical Informatics Research, School of Medicine, Stanford University, Stanford, California 94305, USA -- Daniel P. Miranker is with the Institute for Cell and Molecular Biology, The University of Texas at Austin, Austin, Texas 78701, USABackground: Ontologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL. Results: We have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source. Conclusions: Our transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner.Computer SciencesInstitute for Cellular and Molecular [email protected]

Processing Narratives by Means of Action Languages

In this work we design a narrative understanding system Text2ALM that can be used in Question Answering domains. System Text2ALM utilizes an action language ℒℳ to perform inferences on complex interactions of events described in narratives. The methodology that Text2ALM follows in its implementation was originally outlined by Yuliya Lierler, Daniela Inclezan, and Michael Gelfond in 2017 via a manual process, and this work serves as a proof of concept in a large-scale environment. Our system automates the conversion of a narrative to an ℒℳ model containing facts about the narrative. We make use of the VerbNet lexicon that we annotated with interpretable semantics in ℒℳ. Text2ALM also utilizes Text2DRS system developed by Gang Ling at UNO in 2018. These resources are used to produce an ℒℳ program with a system description containing information on the narrative’s entities, events, and their relations, as well as a history of the narrative’s events. The ℒℳ logic is used in tandem with a basic commonsense library of ℒℳ modules to generate a formal structure capturing the narrative’s properties. The CALM system designed by researchers at Texas Tech in 2018 and is used by Text2ALM to process the ℒℳ program. The effectiveness of this approach is measured by the system’s ability to correctly answer questions from the QA bAbI tasks published by Facebook Research in 2015. The Text2ALM system matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks