scholarly journals Formal Ontologies in Biomedical Knowledge Representation

2013 ◽  
Vol 22 (01) ◽  
pp. 132-146 ◽  
Author(s):  
L. Jansen ◽  
S. Schulz
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Description Logics ◽  
Clinical Decision Support Systems ◽  
Knowledge Bases ◽  
Medical Decision ◽  
Knowledge Representation And Reasoning ◽  
Digital Information ◽  
Biomedical Knowledge ◽  
Formal Ontologies

Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.

scholarly journals Turning Informal Thesauri into Formal Ontologies: A Feasibility Study on Biomedical Knowledge Re-Use

2003 ◽  
Vol 4 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Udo Hahn
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Description Logics ◽  
Biomedical Domain ◽  
Biomedical Knowledge ◽  
Knowledge Conversion ◽  
Formal Knowledge ◽  
Knowledge Repositories ◽  
Formal Ontologies ◽  
Rigorous Description

This paper reports a large-scale knowledge conversion and curation experiment. Biomedical domain knowledge from a semantically weak and shallow terminological resource, the UMLS, is transformed into a rigorous description logics format. This way, the broad coverage of the UMLS is combined with inference mechanisms for consistency and cycle checking. They are the key to proper cleansing of the knowledge directly imported from the UMLS, as well as subsequent updating, maintenance and refinement of large knowledge repositories. The emerging biomedical knowledge base currently comprises more than 240 000 conceptual entities and hence constitutes one of the largest formal knowledge repositories ever built.

Clinical Decision Making by Emergency Room Physicians and Residents

2008 ◽  
pp. 217-223
Author(s):  
Ken J. Farion ◽  
Michael J. Hine ◽  
Wojtek Michalowski ◽  
Szymon Wilk
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Community ◽  
Medical Decision ◽  
Best Interest ◽  
Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

scholarly journals Extensible Knowledge Representation: the Case of Description Reasoners

1999 ◽  
Vol 10 ◽  
pp. 399-434 ◽  
Author(s):  
A. Borgida
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Software Architecture ◽  
Description Logics ◽  
Knowledge Representation And Reasoning ◽  
Heuristic Methodology

This paper offers an approach to extensible knowledge representation and reasoning for a family of formalisms known as Description Logics. The approach is based on the notion of adding new concept constructors, and includes a heuristic methodology for specifying the desired extensions, as well as a modularized software architecture that supports implementing extensions. The architecture detailed here falls in the normalize-compared paradigm, and supports both intentional reasoning (subsumption) involving concepts, and extensional reasoning involving individuals after incremental updates to the knowledge base. The resulting approach can be used to extend the reasoner with specialized notions that are motivated by specific problems or application areas, such as reasoning about dates, plans, etc. In addition, it provides an opportunity to implement constructors that are not currently yet sufficiently well understood theoretically, but are needed in practice. Also, for constructors that are provably hard to reason with (e.g., ones whose presence would lead to undecidability), it allows the implementation of incomplete reasoners where the incompleteness is tailored to be acceptable for the application at hand.

scholarly journals A Framework for Reasoning about Dynamic Axioms in Description Logics

2020 ◽  
Author(s):  
Bartosz Bednarczyk ◽  
Stephane Demri ◽  
Alessio Mansutti
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Data Structures ◽  
Description Logics ◽  
Knowledge Bases ◽  
Dynamic Data Structures ◽  
Dynamic Data ◽  
Logical Connectives ◽  
Consistency Problem ◽  
Natural Means

Description logics are well-known logical formalisms for knowledge representation. We propose to enrich knowledge bases (KBs) with dynamic axioms that specify how the satisfaction of statements from the KBs evolves when the interpretation is decomposed or recomposed, providing a natural means to predict the evolution of interpretations. Our dynamic axioms borrow logical connectives from separation logics, well-known specification languages to verify programs with dynamic data structures. In the paper, we focus on ALC and EL augmented with dynamic axioms, or to their subclass of positive dynamic axioms. The knowledge base consistency problem in the presence of dynamic axioms is investigated, leading to interesting complexity results, among which the problem for EL with positive dynamic axioms is tractable, whereas EL with dynamic axioms is undecidable.

Artificial Intelligence-Based Knowledge Representation and Reasoning

2021 ◽  
pp. 134-149
Author(s):  
Dimpal Tomar ◽  
Pradeep Tomar
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Knowledge Representation ◽  
Knowledge Bases ◽  
Knowledge Representation And Reasoning ◽  
Artificial Intelligent ◽  
Logical Interpretation ◽  
Quality Of Higher Education ◽  
The Right

The quality of higher education can be enhanced only by upgrading the content and skills towards knowledge. Hence, knowledge representation and reasoning play a chief role to represent the facts, beliefs, and information, and inferring the logical interpretation of represented knowledge stored in the knowledge bases. This chapter provide a broad overview of knowledge, representation, and reasoning along with the related art of study in the field of higher education. Various artificial intelligent-based knowledge representation and reasoning techniques and schemes are provided for better representation of facts, beliefs, and information. Various reasoning types are discussed in order to infer the right meaning of the knowledge followed by various issues of knowledge representation and reasoning. .

Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology

2015 ◽  
Vol 11 (2) ◽  
pp. e206-e211 ◽  
Author(s):  
Peter Paul Yu
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Rapid Learning ◽  
Types Of Knowledge ◽  
Different Types

This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into clinical decision support systems.

Partitioning knowledge bases between advanced notification and clinical decision support systems

2007 ◽  
Vol 43 (4) ◽  
pp. 1274-1286 ◽  
Author(s):  
Yves A. Lussier ◽  
Rose Williams ◽  
Jianrong Li ◽  
Srikant Jalan ◽  
Tara Borlawsky ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Knowledge Bases

scholarly journals A Knowledge Management and Decision Support Model for Enterprises

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Patrizia Ribino ◽  
Agnese Augello ◽  
Giuseppe Lo Re ◽  
Salvatore Gaglio
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Planning Process ◽  
Semantic Space ◽  
Knowledge Bases ◽  
Data Driven ◽  
Knowledge Representation And Reasoning ◽  
Decision Support Model ◽  
Intelligent Search ◽  
Decision Support Process

We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty.

Adjustment of Medical Observations Influenced by Emotional State

2018 ◽  
Vol 9 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Firas Zekri ◽  
Afef Samet Ellouze ◽  
Rafik Bouaziz
Keyword(s):  
Decision Support ◽  
Fuzzy Systems ◽  
Emotional State ◽  
Strong Dependence ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Observation ◽  
Using Data

Research in neurophysiology and neuropsychology have established a strong dependence between emotion, subjectivity and decision-making. Otherwise, medical observations are used as one of the main inputs of clinical decision support systems (CDSS) which are designed to support patients with chronic progressive diseases. However, these observations are influenced when confronted with a critical emotional state and they are likely to be subjective. To generate efficient results, CDSS must bring these subjective observations closer to the reality by using data describing the observer's emotional state. To solve this issue, the authors of this article propose to identify the dependency relationship between observations and emotions. Then they provide a solution that moderates the patient and caregivers' observations within a medical decision support system, so that it can generate efficient results. Finally, they propose two fuzzy systems to adjust the influence of emotional state on medical observation. These two systems make the medical observation closer to the current condition of the patient.

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