Mark Jensen

The Ontology for Biomedical Investigations

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

The neurological disease ontology

BackgroundWe 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.DescriptionND 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 athttp://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker.ConclusionND 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.

Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data.

Representing vision and blindness

BackgroundThere have been relatively few attempts to represent vision or blindness ontologically. This is unsurprising as the related phenomena of sight and blindness are difficult to represent ontologically for a variety of reasons. Blindness has escaped ontological capture at least in part because: blindness or the employment of the term ‘blindness’ seems to vary from context to context, blindness can present in a myriad of types and degrees, and there is no precedent for representing complex phenomena such as blindness.MethodsWe explore current attempts to represent vision or blindness, and show how these attempts fail at representing subtypes of blindness (viz., color blindness, flash blindness, and inattentional blindness). We examine the results found through a review of current attempts and identify where they have failed.ResultsBy analyzing our test cases of different types of blindness along with the strengths and weaknesses of previous attempts, we have identified the general features of blindness and vision. We propose an ontological solution to represent vision and blindness, which capitalizes on resources afforded to one who utilizes the Basic Formal Ontology as an upper-level ontology.ConclusionsThe solution we propose here involves specifying the trigger conditions of a disposition as well as the processes that realize that disposition. Once these are specified we can characterize vision as a function that is realized by certain (in this case) biological processes under a range of triggering conditions. When the range of conditions under which the processes can be realized are reduced beyond a certain threshold, we are able to say that blindness is present. We characterize vision as a function that is realized as a seeing process and blindness as a reduction in the conditions under which the sight function is realized. This solution is desirable because it leverages current features of a major upper-level ontology, accurately captures the phenomenon of blindness, and can be implemented in many domain-specific ontologies.

Towards a methodology for lossless data exchange between NoSQL data structures

The variety of data structures used by NoSQL databases (e.g. key-value, document, triple store, graph) is evidence of the variety of ways in which data is used within an enterprise. Data in triple-stores that are aligned to semantically rich ontologies are useful for discovery and all-source analysis while data in key-value stores are useful for massive scale high-speed computing. Within an enterprise that utilizes multiple types of NoSQL databases the exchange of data from one to another, if accomplished, usually comes at the cost of losing some amount of content. A methodology of lossless data exchange that utilizes semantic metadata libraries is described.

Problems with prescriptions: disentangling data about actual versus prescribed entities

Integrating data about plans and artifact specifications with data about the actual instances of the entities prescribed by these provides numerous benefits for tasks such as mission planning, sensor assignment, and asset tasking. However, doing so raises several issues for data ingest, storage and analytics if a consistent semantics is to be maintained to enable extensible and unanticipated querying. In this paper, we examine strategies for overcoming these challenges and describe a method for using the Common Core Ontologies and Modal Relation Ontology to map and integrate data about planned and existing entities. We demonstrate a solution for ensuring reliable, dynamic and extensible data queries suitable for highly heterogeneous data sources that is agnostic to implementation requirements. We focus on examples relevant to sensor capabilities, selection and tasking.

A common core-based cyber ontology in support of cross-domain situational awareness

Awareness of mission environment and events is impeded by data heterogeneity and lack of integration among data sources across diverse domains. In this paper, we present C3O, an RDF/OWL-based cyber ontology which provides a representation of cyber assets and events, to which existing XML-based cyber models (STIX, CybOX) can be mapped. C3O is unique in that it is designed as an extension of Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO), which renders it automatically interoperable with a host of existing BFO- and CCO-based domain ontologies for land, sea, air, planning, operations, and sensor data.

Interdyscyplinarne perspektywy rozwoju, integracji i zastosowań ontologii poznawczych

Przedstawiamy ostatnie postepy w rozwoju ontologii poznawczych i omawiamy trzy wyzwania dla skoordynowanego rozwoju i zastosowan tych zasobow.xa0 Wyzwaniem_1xa0 jest przyjecie zestandaryzowanej definicji procesow poznawczych. Opisujemy trzy mozliwości i rekomendujemy jedną, spojną z tradycyjnym ujeciem w naukach poznawczych i biomedycznych.xa0 Wyzwaniem_2xa0 jest harmonizacja [harmonization]. Luki i niezgodności w reprezentacjach muszą byc usuniete, tak zeby zasobyxa0 mogly byc polączone w celu oznaczania i interpretacji danych multimodalnych. Na koniec,xa0wyzwaniem_3xa0jest przetestowanie uzyteczności tych zasobow dla szeroko zakrojonej anotacji danych, zapytan i przeszukiwania oraz integracji i odkrywania wiedzy. Podczas gdy definicje terminow są tes- towane i poprawiane, harmonizacja powinna umozliwiac skoordynowane aktualizacje pomiedzy ontologiami. Jednak prawdziwym testem dla tych definicji bedzie przyjecie ich przez calą wspolnote, ktora sprawdzi, czy wspierają one poprawne wnioskowania o danych psychologicznych i neu- ronaukowych.