Background: We are developing the Neurological Disease Ontology (ND) to provide a framework to en... more Background: We are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer’s disease, multiple sclerosis, and stroke. Description: ND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2. Term development in ND has primarily extended the OGMS terms ‘disease’, ‘diagnosis’, ‘disease course’, and ‘disorder’. We have imported and utilize over 700 classes from related ontology efforts including the Foundational Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status, and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the representation of Alzheimer’s disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled file at http://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker. Conclusion: ND seeks to provide a formal foundation for the representation of clinical and research data pertaining to neurological diseases. ND will enable its users to connect data in a robust way with related data that is annotated using other terminologies and ontologies in the biomedical domain.
A number of researchers have begun to demonstrate that the widely discussed ‘Knobe effect’ (where... more A number of researchers have begun to demonstrate that the widely discussed ‘Knobe effect’ (wherein participants are more likely to think that actions with bad side-effects are brought about intentionally than actions with good or neutral side-effects) can be found in theory of mind judgments that do not involve the concept of intentional action. In this article we report experimental results that show that attributions of knowledge can be influenced by the kinds of (non-epistemic) concerns that drive the Knobe effect. Our findings suggest there is good reason to think that the epistemic version of the Knobe effect is a theoretically significant and robust effect and that the goodness or badness of side-effects can often have greater influence on participant knowledge attributions than explicit information about objective probabilities. Thus, our work sheds light on important ways in which participant assessments of actions can affect the epistemic assessments participants make of agents’ beliefs.
Background: We are developing the Neurological Disease Ontology (ND) to provide a framework to en... more Background: We are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer’s disease, multiple sclerosis, and stroke. Description: ND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2. Term development in ND has primarily extended the OGMS terms ‘disease’, ‘diagnosis’, ‘disease course’, and ‘disorder’. We have imported and utilize over 700 classes from related ontology efforts including the Foundational Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status, and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the representation of Alzheimer’s disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled file at http://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker. Conclusion: ND seeks to provide a formal foundation for the representation of clinical and research data pertaining to neurological diseases. ND will enable its users to connect data in a robust way with related data that is annotated using other terminologies and ontologies in the biomedical domain.
A number of researchers have begun to demonstrate that the widely discussed ‘Knobe effect’ (where... more A number of researchers have begun to demonstrate that the widely discussed ‘Knobe effect’ (wherein participants are more likely to think that actions with bad side-effects are brought about intentionally than actions with good or neutral side-effects) can be found in theory of mind judgments that do not involve the concept of intentional action. In this article we report experimental results that show that attributions of knowledge can be influenced by the kinds of (non-epistemic) concerns that drive the Knobe effect. Our findings suggest there is good reason to think that the epistemic version of the Knobe effect is a theoretically significant and robust effect and that the goodness or badness of side-effects can often have greater influence on participant knowledge attributions than explicit information about objective probabilities. Thus, our work sheds light on important ways in which participant assessments of actions can affect the epistemic assessments participants make of agents’ beliefs.
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Papers by Mark Jensen
representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a
representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated
with the treatment and study of neurological diseases. ND is being developed in compliance with the Open
Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General
Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial
applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer’s
disease, multiple sclerosis, and stroke.
Description: ND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe
various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2.
Term development in ND has primarily extended the OGMS terms ‘disease’, ‘diagnosis’, ‘disease course’, and
‘disorder’. We have imported and utilize over 700 classes from related ontology efforts including the Foundational
Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with
ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status,
and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current
development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the
representation of Alzheimer’s disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled
file at http://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker.
Conclusion: ND seeks to provide a formal foundation for the representation of clinical and research data pertaining
to neurological diseases. ND will enable its users to connect data in a robust way with related data that is
annotated using other terminologies and ontologies in the biomedical domain.
representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a
representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated
with the treatment and study of neurological diseases. ND is being developed in compliance with the Open
Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General
Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial
applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer’s
disease, multiple sclerosis, and stroke.
Description: ND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe
various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2.
Term development in ND has primarily extended the OGMS terms ‘disease’, ‘diagnosis’, ‘disease course’, and
‘disorder’. We have imported and utilize over 700 classes from related ontology efforts including the Foundational
Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with
ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status,
and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current
development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the
representation of Alzheimer’s disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled
file at http://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker.
Conclusion: ND seeks to provide a formal foundation for the representation of clinical and research data pertaining
to neurological diseases. ND will enable its users to connect data in a robust way with related data that is
annotated using other terminologies and ontologies in the biomedical domain.