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Highlights • A computational system was developed according to software engineering prototyping. • An original ontology-based Medical Report Mapping Process was automated in this work. • The system supports automatic mapping of unstructured texts into structured format. • An experimental evaluation was performed in a set of 100 artificial textual reports. • Experts found that the system is valuable to extract and analyze textual patterns. Abstract We have developed, in collaboration with medical and computer experts, the ontology-based Medical Report Mapping Process to support the transformation of unstructured reports into a structured representation. Nevertheless, the techniques employed in this two-phase process must be performed individually and manually by computer instructions, which hinder their use by users not familiar with such language. Thereby, this work proposes a tool to automate and optimize this process by integrating its techniques in a computational system, which was built using a software engineering prototyping approach. This system was experimentally evaluated by applying it to a set of 100 textual reports. The first phase decreased the total number of phrases (853) and words (2520) by 82.25% (48) and 92.70% (184), respectively. In the second phase, 100% of the relevant pieces of information (previously established) present in the reports were transcribed. Also, the second phase was applied, using the same configuration as the first study, in another set with 250 textual reports, resulting in a mapping rate of 99.74%. The unprocessed and unmapped words, regarding both experimental evaluations, were recorded for later inclusion into the ontology. By using this system, efficient and scalable investigations can be performed from medical reports, contributing to generate new knowledge. Also, the system facilitates the definition of these structures due to the feasibility to analyze different sentences in unique phrase sets. [ABSTRACT FROM AUTHOR]

This paper presents a workflow for the design of transformation processes using different kinds of expert's knowledge. It introduces POND (Process and observation ONtology Discovery), a workflow dedicated to answer expert's questions about processes. It addresses two main issues: (1) how to represent the processes inner complexity, and (2) how to reason about processes taking into account uncertainty and causality. First, we show how to use a semantic model, an ontology, and its associated data to answer some of the expert's questions concerning the processes, using semantic web languages and technologies. Then, we describe how to learn a predictive model, to discover new knowledge and provide explicative models by integrating the semantic model into a probabilistic relational model. The result is a complete workflow able to extensively analyze transformation processes through all their granularity levels and answer expert's questions about their domains. An example of this workflow is given on biocomposites manufacturing for food packaging. • Due to their complexity, reasoning with transformation processes is challenging. • POND is a workflow aiming to represent and reason with transformation processes. • Representation is achieved with the ontology PO² and its associated softwares. • Reasoning under uncertainty is achieved with probabilistic graphical models. • Expert knowledge integration allows knowledge and causal discovery. [ABSTRACT FROM AUTHOR]

The paper presents the main results of the KOMET (Knowledge and cOntent structuring via METhods of collaborative ontology design) project, which aims to develop a novel paradigm for knowledge structuring based on the interplay between cognitive psychology and ontology engineering. By the knowledge structure (a conceptual model) we define the main domain concepts and relations between them in the form of a graph, map or diagram. This approach considers individual cognitive styles and uses recent advances in knowledge engineering and conceptual structuring; it aims to create new, consistent and structurally holistic knowledge bases for various areas of science and technology. Two stages of research have been completed: research into correlations between the expert’s individual cognitive style and the peculiarities of the expert’s subject domain ontology development; and research into correlations between the expert’s individual cognitive style and the group ontology design (including design accomplished by groups of experts with either similar or different cognitive styles). The results of these research stages can be applied to organizing collaborative ontology design (especially for research and learning purposes), data structuring and other group analytical work. Implications for practice are briefly delineated. [ABSTRACT FROM AUTHOR]

Highlights: [•] An ontology-based system is proposed to model items included in a hospital health catalog. [•] The proposed ontology-based system can be used for sharing and acquiring knowledge by clinical and administrative personnel. [•] Correct usage of medical supplies has a positive impact both on hospital economics and personnel safety. [Copyright &y& Elsevier]

• Handling compound sentences increases precision of a SPARQL query. • Handling opposing scalar adjectives increases precision and recall of a SPARQL query. • Handling Negation increases precision and recall of a SPARQL query. This paper provides a dependency based framework for converting natural language to SPARQL. We present a tool known as PAROT (which echos answers from ontologies) which is able to handle user's queries that contain compound sentences, negation, scalar adjectives and numbered list. PAROT employs a number of dependency based heuristics to convert user's queries to user's triples. The user's triples are then processed by the lexicon into ontology triples. It is these ontology triples that are used to construct SPARQL queries. From the experiments conducted, PAROT provides state of the art results. [ABSTRACT FROM AUTHOR]