scholarly journals Adding Cognition to the Semanticscience Integrated Ontology

2019 ◽  
pp. 4-13 ◽  
Author(s):  
Stephen K Reed ◽  
Michel Dumontier
Keyword(s):  
Mental Illness ◽  
Cognitive Psychology ◽  
Molecular Biology ◽  
Cognitive Neuroscience ◽  
Domain Knowledge ◽  
Computational Analysis ◽  
Deductive Reasoning ◽  
Multi Scale ◽  
Units Of Analysis ◽  
Formal Ontologies

The goal of the NIMH RDoC initiative is to establish a biological basis for mental illness that includes linking cognition to molecular biology. A key challenge lies in how to represent such large, complex, and multi-scale knowledge in a manner that can support computational analysis, including query answering. Formal ontologies, such as the Semanticscience Integrated Ontology (SIO), offer a scaffold in which complex domain knowledge such as neurological and cognitive functions can be represented and linked to knowledge of molecular biology. In this article, we explore the use of SIO to represent concepts in molecular biology and in cognition. We extend SIO to traditional cognitive topics by illustrating axioms for both an information-processing and a neuroscience perspective on reading. We next discuss the NIMH RDoC taxonomy and include SIO axioms for the units-of-analysis and functions-of-behavior dimensions. An example demonstrates its use of deductive reasoning to establish causal relations across RDoC dimensions. From a broader perspective this article demonstrates how informatics can assist in integrating work in clinical psychology, cognitive psychology, cognitive neuroscience, computer science, molecular biology, and philosophy.

scholarly journals Adding Cognition to the Semanticscience Integrated Ontology

2019 ◽  
pp. 4-13
Author(s):  
Stephen k Reed ◽  
Michel Dumontier
Keyword(s):  
Mental Illness ◽  
Cognitive Psychology ◽  
Molecular Biology ◽  
Cognitive Neuroscience ◽  
Domain Knowledge ◽  
Computational Analysis ◽  
Deductive Reasoning ◽  
Multi Scale ◽  
Units Of Analysis ◽  
Formal Ontologies

The goal of the NIMH RDoC initiative is to establish a biological basis for mental illness that includes linking cognition to molecular biology. A key challenge lies in how to represent such large, complex, and multi-scale knowledge in a manner that can support computational analysis, including query answering. Formal ontologies, such as the Semanticscience Integrated Ontology (SIO), offer a scaffold in which complex domain knowledge such as neurological and cognitive functions can be represented and linked to knowledge of molecular biology. In this article, we explore the use of SIO to represent concepts in molecular biology and in cognition. We extend SIO to traditional cognitive topics by illustrating axioms for both an information-processing and a neuroscience perspective on reading. We next discuss the NIMH RDoC taxonomy and include SIO axioms for the units-of-analysis and functions-of-behavior dimensions. An example demonstrates its use of deductive reasoning to establish causal relations across RDoC dimensions. From a broader perspective this article demonstrates how informatics can assist in integrating work in clinical psychology, cognitive psychology, cognitive neuroscience, computer science, molecular biology, and philosophy.

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.

Verification of Uncurated Protein Annotations

2010 ◽  
pp. 301-314
Author(s):  
Francisco M. Couto ◽  
Mário J. Silva ◽  
Vivian Lee ◽  
Emily Dimmer ◽  
Evelyn Camon ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Molecular Biology ◽  
Domain Knowledge ◽  
Text Processing ◽  
The Other ◽  
Research Projects ◽  
Evidence Text ◽  
Biological Sources ◽  
Biology Research ◽  
Automatic Text

Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a large portion of the data contains a significant number of errors and therefore requires careful verification by curators, a painful and costly task, before being reliable enough to derive valid conclusions from it. On the other hand, research in biomedical information retrieval and information extraction are nowadays delivering Text Mining solutions that can support curators to improve the efficiency of their work to deliver better data resources. Over the past decades, automatic text processing systems have successfully exploited biomedical scientific literature to reduce the researchers’ efforts to keep up to date, but many of these systems still rely on domain knowledge that is integrated manually leading to unnecessary overheads and restrictions in its use. A more efficient approach would acquire the domain knowledge automatically from publicly available biological sources, such as BioOntologies, rather than using manually inserted domain knowledge. An example of this approach is GOAnnotator, a tool that assists the verification of uncurated protein annotations. It provided correct evidence text at 93% precision to the curators and thus achieved promising results. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.

How to interface cognitive psychology with cognitive neuroscience?

2001 ◽  
Vol 24 (1) ◽  
pp. 148-149
Author(s):  
Hannu Tiitinen
Keyword(s):  
Cognitive Psychology ◽  
Cognitive Neuroscience ◽  
Short Term Memory ◽  
Point Of View ◽  
Short Term ◽  
Term Memory

Cowan's analysis of human short-term memory (STM) and attention in terms of processing limits in the range of 4 items (or “chunks”) is discussed from the point of view of cognitive neuroscience. Although, Cowan already provides many important theoretical insights, we need to learn more about how to build further bridges between cognitive psychology and cognitive neuroscience.

scholarly journals Joint action with iCub: a successful adaptation of a paradigm of cognitive neuroscience in HRI

2019 ◽  
Author(s):  
Jairo Pérez-Osorio ◽  
Davide De Tommaso ◽  
Ebru Baykara ◽  
Agnieszka Wykowska
Keyword(s):  
Cognitive Psychology ◽  
Human Brain ◽  
Cognitive Neuroscience ◽  
Human Robot Interaction ◽  
Human Cognition ◽  
Social Environments ◽  
Social Signals ◽  
Robot Interaction ◽  
Robot Behavior ◽  
The Impact

Robots will soon enter social environments shared with humans. We need robots that are able to efficiently convey social signals during interactions. At the same time, we need to understand the impact of robots’ behavior on the human brain. For this purpose, human behavioral and neural responses to the robot behavior should be quantified offering feedback on how to improve and adjust robot behavior. Under this premise, our approach is to use methods of experimental psychology and cognitive neuroscience to assess the human’s reception of a robot in human-robot interaction protocols. As an example of this approach, we report an adaptation of a classical paradigm of experimental cognitive psychology to a naturalistic human- robot interaction scenario. We show the feasibility of such an approach with a validation pilot study, which demonstrated that our design yielded a similar pattern of data to what has been previously observed in experiments within the area of cognitive psychology. Our approach allows for addressing specific mechanisms of human cognition that are elicited during human-robot interaction, and thereby, in a longer-term perspective, it will allow for designing robots that are well- attuned to the workings of the human brain.

Verification of Uncurated Protein Annotations

2011 ◽  
pp. 1360-1373
Author(s):  
Francisco M. Couto ◽  
Mário J. Silva ◽  
Vivian Lee ◽  
Emily Dimmer ◽  
Evelyn Camon ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Molecular Biology ◽  
Domain Knowledge ◽  
Text Processing ◽  
The Other ◽  
Research Projects ◽  
Evidence Text ◽  
Biological Sources ◽  
Biology Research ◽  
Automatic Text

Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a large portion of the data contains a significant number of errors and therefore requires careful verification by curators, a painful and costly task, before being reliable enough to derive valid conclusions from it. On the other hand, research in biomedical information retrieval and information extraction are nowadays delivering Text Mining solutions that can support curators to improve the efficiency of their work to deliver better data resources. Over the past decades, automatic text processing systems have successfully exploited biomedical scientific literature to reduce the researchers’ efforts to keep up to date, but many of these systems still rely on domain knowledge that is integrated manually leading to unnecessary overheads and restrictions in its use. A more efficient approach would acquire the domain knowledge automatically from publicly available biological sources, such as BioOntologies, rather than using manually inserted domain knowledge. An example of this approach is GOAnnotator, a tool that assists the verification of uncurated protein annotations. It provided correct evidence text at 93% precision to the curators and thus achieved promising results. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.

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