Towards a Semantic Portal for Oncology using a Description Logic with Fuzzy Concrete Domains

This paper presents three systems that are fully implemented and a proposal for a fourth one. KASIMIR is a knowledge based-system using an ad hoc formalism similar to a simple description logic with concrete domains which is used for representing decision protocols in oncology. FUZZY-KASIMIR is an extension of KASIMIR with fuzzy concrete domains taking into account discontinuities in the decision that are due to numerical thresholds. Another extension of KASIMIR has led to embed it into a semantic portal for oncology, which has been motivated by the need to share knowledge for geographically distributed physicians and has led to change the ad hoc formalism to the standard OWL DL. A combination of these two extensions of KASIMIR is currently under implementation and will lead to a semantic portal for oncology with fuzzy datatypes

An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics

Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained resultsThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1A2B5B02002599)S

Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology

International audienceThis article presents the Kasimir system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL. The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, e.g., because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning in the framework of description logics have been used. In addition, the domain of oncology is complex and involves several specialties, e.g. surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed description logic has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized case-based reasoning

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

First Elements on Knowledge Discovery guided by Domain Knowledge (KDDK)

International audienceIn this paper, we present research trends carried out in the Orpailleur team at Loria, showing how knowledge discovery and knowledge processing may be combined. The knowledge discovery in databases process (KDD) consists in processing a huge volume of data for extracting significant and reusable knowledge units. From a knowledge representation perspective, the KDD process may take advantage of domain knowledge embedded in ontologies relative to the domain of data, leading to the notion of ''knowledge discovery guided by domain knowledge'' or KDDK. The KDDK process is based on the classification process (and its multiple forms), e.g. for modeling, representing, reasoning, and discovering. Some applications are detailed, showing how KDDK can be instantiated in an application domain. Finally, an architecture of an integrated KDDK system is proposed and discussed

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse