NIFSTD is a comprehensive ontology for neuroscience developed by the Neuroscience Information Framework (NIF) project. It consists of several modular ontologies covering neuroscience domains like brain regions, cells, molecules, and diseases. NIFSTD aims to provide consistent descriptions of neuroscience resources to enable concept-based search across multiple data types. It imports and maps to existing community ontologies and seeks to avoid duplication of efforts.
The document discusses neuroscience ontologies created by the Neuroscience Information Framework (NIF). It describes how NIF incorporates existing ontologies and extends them for neuroscience as needed. NIF includes modular ontologies covering multiple scales including molecules, cells, anatomy, and functions. Key ontologies discussed include NIFSTD, Neurolex, and bridging files that link related concepts across ontologies. Examples are provided of how neuron classes are defined based on attributes such as brain region, molecular constituents, and roles.
The document introduces Knowledge Engineering from Experimental Design (KEfED), a new approach for representing biomedical scientific knowledge based on how scientists design experiments. KEfED uses standard experimental templates to represent parameters, measurements, calculations, and the dependencies between variables inherent in experimental protocols. It can handle complex experimental designs. KEfED forms the basis for developing a semantic web-compatible data repository and reasoning tools to represent and interpret observations from experiments studying neural connectivity. Future work involves linking KEfED to existing ontologies and developing domain-specific reasoning models.
The Neuroscience Information Framework (NIF) uses ontologies like the NIF Standard Ontology (NIFSTD) to enable concept-based search across multiple neuroscience resources. NIFSTD integrates over 60,000 concepts from various domains of neuroscience and reuses terms from existing ontologies. It allows for classification of neuroscience entities and logical inferences to find related concepts. The NIF framework aims to build a rich knowledgebase integrating neuroscience data from various sources.
This document discusses defined versus asserted classes in ontologies and provides examples from the Neuroscience Information Framework (NIF) Standard (NIFSTD) ontologies. It explains that NIFSTD uses single inheritance for asserted class hierarchies but allows multiple inheritance through logical definitions and automated reasoning. It provides examples of defined neuron classes based on neurotransmitter or brain region and discusses how bridge files link different ontology modules while allowing custom restrictions.
This document provides an overview of using soft computing techniques for DNA sequence classification. It discusses DNA and DNA sequencing. It then introduces common soft computing techniques used for classification, including neural networks, fuzzy logic, and genetic algorithms. The document proposes using these soft computing methods for DNA sequence classification and describes related studies. It outlines a methodology using neural networks and genetic algorithms and analyzes the advantages of soft computing for this application. In conclusion, it states that soft computing techniques are well-suited for DNA sequence classification problems.
The document describes an algorithm called "Hide" that identifies and removes redundant import of classes from a top-level ontology into a domain ontology. It was developed to improve quality assurance of ontologies by removing unused imported classes. The algorithm is demonstrated by applying it to the Drug Discovery Investigations (DDI) ontology, where it identified and hid 18 out of 32 imported Basic Formal Ontology (BFO) classes that were not used in DDI. Future work is needed to handle more complex ontology structures and relationships between classes.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
NIFSTD is a comprehensive ontology for neuroscience developed by the Neuroscience Information Framework (NIF) project. It consists of several modular ontologies covering neuroscience domains like brain regions, cells, molecules, and diseases. NIFSTD aims to provide consistent descriptions of neuroscience resources to enable concept-based search across multiple data types. It imports and maps to existing community ontologies and seeks to avoid duplication of efforts.
The document discusses neuroscience ontologies created by the Neuroscience Information Framework (NIF). It describes how NIF incorporates existing ontologies and extends them for neuroscience as needed. NIF includes modular ontologies covering multiple scales including molecules, cells, anatomy, and functions. Key ontologies discussed include NIFSTD, Neurolex, and bridging files that link related concepts across ontologies. Examples are provided of how neuron classes are defined based on attributes such as brain region, molecular constituents, and roles.
The document introduces Knowledge Engineering from Experimental Design (KEfED), a new approach for representing biomedical scientific knowledge based on how scientists design experiments. KEfED uses standard experimental templates to represent parameters, measurements, calculations, and the dependencies between variables inherent in experimental protocols. It can handle complex experimental designs. KEfED forms the basis for developing a semantic web-compatible data repository and reasoning tools to represent and interpret observations from experiments studying neural connectivity. Future work involves linking KEfED to existing ontologies and developing domain-specific reasoning models.
The Neuroscience Information Framework (NIF) uses ontologies like the NIF Standard Ontology (NIFSTD) to enable concept-based search across multiple neuroscience resources. NIFSTD integrates over 60,000 concepts from various domains of neuroscience and reuses terms from existing ontologies. It allows for classification of neuroscience entities and logical inferences to find related concepts. The NIF framework aims to build a rich knowledgebase integrating neuroscience data from various sources.
This document discusses defined versus asserted classes in ontologies and provides examples from the Neuroscience Information Framework (NIF) Standard (NIFSTD) ontologies. It explains that NIFSTD uses single inheritance for asserted class hierarchies but allows multiple inheritance through logical definitions and automated reasoning. It provides examples of defined neuron classes based on neurotransmitter or brain region and discusses how bridge files link different ontology modules while allowing custom restrictions.
This document provides an overview of using soft computing techniques for DNA sequence classification. It discusses DNA and DNA sequencing. It then introduces common soft computing techniques used for classification, including neural networks, fuzzy logic, and genetic algorithms. The document proposes using these soft computing methods for DNA sequence classification and describes related studies. It outlines a methodology using neural networks and genetic algorithms and analyzes the advantages of soft computing for this application. In conclusion, it states that soft computing techniques are well-suited for DNA sequence classification problems.
The document describes an algorithm called "Hide" that identifies and removes redundant import of classes from a top-level ontology into a domain ontology. It was developed to improve quality assurance of ontologies by removing unused imported classes. The algorithm is demonstrated by applying it to the Drug Discovery Investigations (DDI) ontology, where it identified and hid 18 out of 32 imported Basic Formal Ontology (BFO) classes that were not used in DDI. Future work is needed to handle more complex ontology structures and relationships between classes.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
The document discusses the Neuroscience Information Framework (NIF), which aims to provide a consistent framework and portal for discovering and utilizing web-based neuroscience resources. It summarizes the goals of NIF in indexing over 2000 databases and making their content searchable through an expansive neuroscience ontology. The document outlines the history and development of NIF, describes its search capabilities and use of ontologies, and provides examples of tools and resources that integrate NIF services like the Whole Brain Catalog.
Big data from small data: A survey of the neuroscience landscape through the...Maryann Martone
The document discusses the Neuroscience Information Framework (NIF), an initiative by the NIH Blueprint to provide a single access point for searching across multiple neuroscience databases and data types. NIF aims to maximize access to and utility of worldwide neuroscience resources by creating a consistent framework for describing resources and enabling simultaneous searches. It notes that neuroscience data exists in many forms, from raw data to processed data to claims, across multiple scales and data types. NIF is designed to rapidly integrate these diverse resources through a tiered system that has a low barrier for data providers to participate.
How do we know what we don’t know: Using the Neuroscience Information Framew...Maryann Martone
The document discusses using the Neuroscience Information Framework (NIF) to reveal knowledge gaps in neuroscience. It summarizes that NIF aims to maximize awareness, access, and utility of neuroscience research resources by uniting information from over 200 databases containing over 400 million records. However, it notes that certain domains may still be underrepresented due to biases in available data driven by factors like funding priorities. The framework uses ontologies to help integrate diverse data types and link them with defined concepts, but notes that neuroanatomical structures in particular pose challenges due to inconsistent naming conventions across studies.
The document describes Knowledge Engineering from Experimental Design (KEfED), a semantic framework for representing biomedical experimental data and knowledge. KEfED models experiments using logical elements like activities, experimental objects, parameters, measurements, and branches to represent experimental designs, observations, and interpretations. It aims to introduce formalism to heterogeneous biomedical statements in a way that is intuitive for scientists. KEfED can be used as the basis for data repositories and integrated with tools like the Open Biomedical Ontology. Future work includes developing domain-specific reasoning models and semantic links to other frameworks.
The document discusses navigating the neuroscience data landscape. It notes that a grand challenge in neuroscience is to understand brain function across multiple scales of organization. Central to this effort is understanding "neural choreography" - the integrated functioning of neurons into brain circuits. The Neuroscience Information Framework (NIF) aims to facilitate discovery and utilization of web-based neuroscience resources. However, the neuroscience community has not fully exploited currently available data or prepared for forthcoming data.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
27 November 2014
The real world of ontologies and phenotype representation: perspectives from...Maryann Martone
The document discusses the Neuroscience Information Framework (NIF) and its role in facilitating discovery and use of neuroscience resources through a consistent semantic framework. NIF provides a portal for searching various types of neuroscience data and information organized by categories. It utilizes ontologies and advanced technologies to allow simultaneous searching of multiple sources. Challenges include the large number of databases and other resources, differing data types, and inconsistent naming of brain structures across sources.
RDAP14: Maryann Martone, Keynote, The Neuroscience Information FrameworkASIS&T
The Neuroscience Information Framework (NIF) is an initiative of the NIH Blueprint to maximize access to and utility of worldwide neuroscience research resources. NIF catalogs over 10,000 resources including databases, literature, and materials. It provides search capabilities across these resources and develops ontologies and semantic frameworks to integrate diverse data types and scales. NIF aims to make dispersed neuroscience information more findable, accessible, interoperable, and reusable to enable new insights.
This document provides a summary of three articles on ontology-based information extraction and the Sophie system. It describes key concepts in OBIE like using ontologies to guide information extraction and presenting extracted information using ontologies. It also summarizes the Yago ontology which extracts information from Wikipedia and WordNet to build a large knowledge base and the Sophie system which aims to incrementally expand ontologies by leveraging existing knowledge to generate and evaluate new hypotheses.
The document discusses the Neuroscience Information Framework (NIF), which provides a portal for finding and utilizing web-based neuroscience resources. NIF allows simultaneous searching of multiple data sources through a concept-based interface organized by categories. It indexes over 35 million records from 65+ databases. NIF aims to address the challenges of dispersed and inconsistent neuroscience data by providing a common framework and tools to integrate data from various sources. Ontologies are discussed as a way to represent neuroscience concepts and relationships in a machine-readable way to facilitate data integration and querying across multiple scales and domains.
A knowledge capture framework for domain specific search systemsramakanz
This is the product roll out presentation at the AFRL on creating a focused knowledge base, search, and retrieval system for the domain of human performance and cognition.
Data-knowledge transition zones within the biomedical research ecosystemMaryann Martone
Overview of the Neuroscience Information Framework and how it brings together data, in the form of distributed databases, and knowledge, in the form of ontologies to show the mapping of the dataspace and places where there are mismatches between data and knowledge.
An expert knowledge base on human performance and cognition was created by extracting information from scientific literature using natural language processing and pattern-based techniques. Over 3 million facts were extracted from abstracts and mapped to a hierarchical structure derived from Wikipedia. The knowledge base was deployed through a browsing tool called Scooner that allows users to navigate relationships between concepts. Further work is focused on improving knowledge base quality by normalizing entities, filtering assertions, and integrating related ontologies and vocabularies.
The document discusses the Neuroscience Information Framework (NIF), which aims to provide a portal for finding and utilizing web-based neuroscience resources. NIF provides a consistent framework for describing various resources like databases, literature, and images. It allows simultaneous searches across these different data types and is supported by neuroscience ontologies. NIF currently catalogs over 5,000 resources and is working to integrate these diverse data sources to help answer questions and discover gaps in our knowledge about the brain.
Fairport domain specific metadata using w3 c dcat & skos w ontology viewsTim Clark
FAIRPORT is an international project to develop a lightweight interoperability architecture for biomedical - and potentially other - data repositories.
This slide deck is a presentation to the FAIRPORT technical team. It describes a proposed model for supporting domain-specific search metadata using a common schema model across all repositories.
The proposal makes use of the following existing technologies, with minor extensions:
- the W3C DCAT model for dataset description
- the W3C SKOS knowledge organization system
- OWL2 Ontology Language
- Dublin Core Vocabulary
- NCBO Bioportal biomedical ontologies collection
Neuroscience research increasingly relies on large, heterogeneous datasets from various sources. Integrating these diverse data types and making them accessible presents challenges. The NIF (Neuroscience Information Framework) addresses this by creating a federated search engine and unified interface to access multiple neuroscience databases. NIF aims to make neuroscience data more discoverable, accessible, and usable through techniques like unique identifiers, metadata standards, and semantic integration. This will help researchers more effectively find and use relevant neuroscience information.
How do we know what we don't know? Exploring the data and knowledge space th...Maryann Martone
The document discusses the Neuroscience Information Framework (NIF), an initiative that aims to catalog and integrate neuroscience resources and data. NIF surveys the neuroscience resource landscape, currently cataloging over 3000 databases and datasets. It provides semantic integration of these resources through the use of ontologies and allows deep search of aggregated data. However, significant amounts of neuroscience data and resources remain inaccessible in publications, databases, and file drawers. Barriers to data sharing include lack of incentives, standards, and resources. NIF and related efforts aim to develop solutions to make more neuroscience data FAIR - findable, accessible, interoperable, and reusable.
This document discusses why journals should ask authors to include Research Resource Identifiers (RRIDs) in their manuscripts. RRIDs help answer questions about what antibodies, animals, cell lines, or software tools were used in a study and allow others to find papers that used the same resources. The document notes that RRIDs improve reproducibility by making materials and methods more transparent. It also discusses how RRIDs can help identify problematic resources like contaminated cell lines or antibodies that do not work or are no longer available. The document provides examples of journals that now require RRIDs and how compliance is implemented.
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Similar to NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement
The document discusses the Neuroscience Information Framework (NIF), which aims to provide a consistent framework and portal for discovering and utilizing web-based neuroscience resources. It summarizes the goals of NIF in indexing over 2000 databases and making their content searchable through an expansive neuroscience ontology. The document outlines the history and development of NIF, describes its search capabilities and use of ontologies, and provides examples of tools and resources that integrate NIF services like the Whole Brain Catalog.
Big data from small data: A survey of the neuroscience landscape through the...Maryann Martone
The document discusses the Neuroscience Information Framework (NIF), an initiative by the NIH Blueprint to provide a single access point for searching across multiple neuroscience databases and data types. NIF aims to maximize access to and utility of worldwide neuroscience resources by creating a consistent framework for describing resources and enabling simultaneous searches. It notes that neuroscience data exists in many forms, from raw data to processed data to claims, across multiple scales and data types. NIF is designed to rapidly integrate these diverse resources through a tiered system that has a low barrier for data providers to participate.
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The document discusses using the Neuroscience Information Framework (NIF) to reveal knowledge gaps in neuroscience. It summarizes that NIF aims to maximize awareness, access, and utility of neuroscience research resources by uniting information from over 200 databases containing over 400 million records. However, it notes that certain domains may still be underrepresented due to biases in available data driven by factors like funding priorities. The framework uses ontologies to help integrate diverse data types and link them with defined concepts, but notes that neuroanatomical structures in particular pose challenges due to inconsistent naming conventions across studies.
The document describes Knowledge Engineering from Experimental Design (KEfED), a semantic framework for representing biomedical experimental data and knowledge. KEfED models experiments using logical elements like activities, experimental objects, parameters, measurements, and branches to represent experimental designs, observations, and interpretations. It aims to introduce formalism to heterogeneous biomedical statements in a way that is intuitive for scientists. KEfED can be used as the basis for data repositories and integrated with tools like the Open Biomedical Ontology. Future work includes developing domain-specific reasoning models and semantic links to other frameworks.
The document discusses navigating the neuroscience data landscape. It notes that a grand challenge in neuroscience is to understand brain function across multiple scales of organization. Central to this effort is understanding "neural choreography" - the integrated functioning of neurons into brain circuits. The Neuroscience Information Framework (NIF) aims to facilitate discovery and utilization of web-based neuroscience resources. However, the neuroscience community has not fully exploited currently available data or prepared for forthcoming data.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
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The real world of ontologies and phenotype representation: perspectives from...Maryann Martone
The document discusses the Neuroscience Information Framework (NIF) and its role in facilitating discovery and use of neuroscience resources through a consistent semantic framework. NIF provides a portal for searching various types of neuroscience data and information organized by categories. It utilizes ontologies and advanced technologies to allow simultaneous searching of multiple sources. Challenges include the large number of databases and other resources, differing data types, and inconsistent naming of brain structures across sources.
RDAP14: Maryann Martone, Keynote, The Neuroscience Information FrameworkASIS&T
The Neuroscience Information Framework (NIF) is an initiative of the NIH Blueprint to maximize access to and utility of worldwide neuroscience research resources. NIF catalogs over 10,000 resources including databases, literature, and materials. It provides search capabilities across these resources and develops ontologies and semantic frameworks to integrate diverse data types and scales. NIF aims to make dispersed neuroscience information more findable, accessible, interoperable, and reusable to enable new insights.
This document provides a summary of three articles on ontology-based information extraction and the Sophie system. It describes key concepts in OBIE like using ontologies to guide information extraction and presenting extracted information using ontologies. It also summarizes the Yago ontology which extracts information from Wikipedia and WordNet to build a large knowledge base and the Sophie system which aims to incrementally expand ontologies by leveraging existing knowledge to generate and evaluate new hypotheses.
The document discusses the Neuroscience Information Framework (NIF), which provides a portal for finding and utilizing web-based neuroscience resources. NIF allows simultaneous searching of multiple data sources through a concept-based interface organized by categories. It indexes over 35 million records from 65+ databases. NIF aims to address the challenges of dispersed and inconsistent neuroscience data by providing a common framework and tools to integrate data from various sources. Ontologies are discussed as a way to represent neuroscience concepts and relationships in a machine-readable way to facilitate data integration and querying across multiple scales and domains.
A knowledge capture framework for domain specific search systemsramakanz
This is the product roll out presentation at the AFRL on creating a focused knowledge base, search, and retrieval system for the domain of human performance and cognition.
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Overview of the Neuroscience Information Framework and how it brings together data, in the form of distributed databases, and knowledge, in the form of ontologies to show the mapping of the dataspace and places where there are mismatches between data and knowledge.
An expert knowledge base on human performance and cognition was created by extracting information from scientific literature using natural language processing and pattern-based techniques. Over 3 million facts were extracted from abstracts and mapped to a hierarchical structure derived from Wikipedia. The knowledge base was deployed through a browsing tool called Scooner that allows users to navigate relationships between concepts. Further work is focused on improving knowledge base quality by normalizing entities, filtering assertions, and integrating related ontologies and vocabularies.
The document discusses the Neuroscience Information Framework (NIF), which aims to provide a portal for finding and utilizing web-based neuroscience resources. NIF provides a consistent framework for describing various resources like databases, literature, and images. It allows simultaneous searches across these different data types and is supported by neuroscience ontologies. NIF currently catalogs over 5,000 resources and is working to integrate these diverse data sources to help answer questions and discover gaps in our knowledge about the brain.
Fairport domain specific metadata using w3 c dcat & skos w ontology viewsTim Clark
FAIRPORT is an international project to develop a lightweight interoperability architecture for biomedical - and potentially other - data repositories.
This slide deck is a presentation to the FAIRPORT technical team. It describes a proposed model for supporting domain-specific search metadata using a common schema model across all repositories.
The proposal makes use of the following existing technologies, with minor extensions:
- the W3C DCAT model for dataset description
- the W3C SKOS knowledge organization system
- OWL2 Ontology Language
- Dublin Core Vocabulary
- NCBO Bioportal biomedical ontologies collection
Neuroscience research increasingly relies on large, heterogeneous datasets from various sources. Integrating these diverse data types and making them accessible presents challenges. The NIF (Neuroscience Information Framework) addresses this by creating a federated search engine and unified interface to access multiple neuroscience databases. NIF aims to make neuroscience data more discoverable, accessible, and usable through techniques like unique identifiers, metadata standards, and semantic integration. This will help researchers more effectively find and use relevant neuroscience information.
How do we know what we don't know? Exploring the data and knowledge space th...Maryann Martone
The document discusses the Neuroscience Information Framework (NIF), an initiative that aims to catalog and integrate neuroscience resources and data. NIF surveys the neuroscience resource landscape, currently cataloging over 3000 databases and datasets. It provides semantic integration of these resources through the use of ontologies and allows deep search of aggregated data. However, significant amounts of neuroscience data and resources remain inaccessible in publications, databases, and file drawers. Barriers to data sharing include lack of incentives, standards, and resources. NIF and related efforts aim to develop solutions to make more neuroscience data FAIR - findable, accessible, interoperable, and reusable.
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An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement
1. NIFSTD AND NEUROLEX: DEVELOPMENT OF A
COMPREHENSIVE NEUROSCIENCE ONTOLOGY
Fahim IMAM, Stephen LARSON, Georgio ASCOLI, Gordon SHEPHERD,
Anita BANDROWSKI, Jeffery S. GRETHE, Amarnath GUPTA, Maryann E. MARTONE
University of California, San Diego, CA
George Mason University, Fairfax, VA
Yale University, New Haven, CT
ICBO Workshop 2011
July 26, 2011
Funded in part by the NIH Neuroscience Blueprint
HHSN271200800035C via NIDA
NEUROSCIENCE INFORMATION FRAMEWORK
2. NIF: DISCOVER AND UTILIZE WEB-BASED
NEUROSCIENCE RESOURCES
A portal for finding and
using neuroscience
resources
A consistent framework
for describing resources
Provides simultaneous
search of multiple types
of information, organized
by category
NIFSTD Ontology, a
critical component
Enables concept-based search
UCSD, Yale, Cal Tech, George Mason, Harvard MGH
Supported by NIH Blueprint
Easier
The Neuroscience Information Framework (NIF), http://neuinfo.org
3. NIF STANDARD ONTOLOGIES (NIFSTD)
• Set of modular ontologies
– Covering neuroscience relevant
terminologies
– Comprehensive ~60, 000 distinct
concepts + synonyms
• Expressed in OWL-DL language
– Supported by common DL Resoners
• Closely follows OBO community
best practices
• Avoids duplication of efforts
– Standardized to the same upper level
ontologies
• e.g., Basic Formal Ontology (BFO), OBO
Relations Ontology (OBO-RO),
Phonotypical Qualities Ontology (PATO)
– Relies on existing community ontologies
e.g., CHEBI, GO, PRO, OBI etc.
3
• Modules cover orthogonal domain
e.g. , Brain
Regions, Cells, Molecules, Subcellul
ar parts, Diseases, Nervous system
functions, etc.
Bill Bug et al.
4. 4
NIFSTD EXTERNAL COMMUNITY SOURCES
Domain External Source Import/ Adapt Module
Organism taxonomy NCBI Taxonomy, GBIF, ITIS, IMSR, Jackson Labs mouse catalog Adapt NIF-Organism
Molecules IUPHAR ion channels and receptors, Sequence Ontology (SO),
ChEBI, and Protein Ontology (PRO); pending: NCBI Entrez
Protein, NCBI RefSeq, NCBI Homologene, NIDA drug lists
Adapt
IUPHAR,
ChEBI;Import
PRO, SO
NIF-Molecule
NIF-Chemical
Sub-cellular Sub-cellular Anatomy Ontology (SAO). Extracted cell parts and
subcellular structures. Imported GO Cellular Component
Import NIF-Subcellular
Cell CCDB, NeuronDB, NeuroMorpho.org. Terminologies; pending:
OBO Cell Ontology
Adapt NIF-Cell
Gross Anatomy NeuroNames extended by including terms from BIRN, SumsDB,
BrainMap.org, etc; multi-scale representation of Nervous
System Macroscopic anatomy
Adapt NIF-
GrossAnatomy
Nervous system
function
Sensory, Behavior, Cognition terms from NIF, BIRN,
BrainMap.org, MeSH, and UMLS
Adapt NIF-Function
Nervous system
dysfunction
Nervous system disease from MeSH, NINDS terminology;
Disease Ontology (DO)
Adapt/Import NIF- Dysfunction
Phenotypic qualities PATO is Imported as part of the OBO foundry core Import NIF-Quality
Investigation: reagents Overlaps with molecules above, especially RefSeq for mRNA Import NIF-Investigation
Investigation:
instruments, protocols
Based on Ontology for Biomedical Investigation (OBI) to include
entities for biomaterial transformations, assays, data
transformations
Adapt NIF-Investigation
Investigation: Resource NIF, OBI, NITRC, Biomedical Resource Ontology (BRO) Adapt NIF-Resource
Biological Process Gene Ontology’s (GO) biological process in whole Import NIF-BioProcess
Cognitive Paradigm Cognitive Paradigm Ontology (CogPO) Import NIF-Investigation
5. IMPORTING OR ADAPTING A NEW ONTOLOGY OR
VOCABULARY SOURCE
Source Import/adapt
a source already in OWL, uses the OBO-
RO and the BFO and is orthogonal to
existing modules
the import simply involves adding an
owl:import statement
existing orthogonal ontology is in OWL
but does not use the same foundational
ontologies as NIFSTD
an ontology-bridging module (explained
later) is constructed declaring the deep
level semantic equivalencies such as
foundational objects and processes.
external source is satisfied by the above
two rules but observed to be too large for
NIF’s scope of interests
a relevant subset is extracted.
MIREOT principles has been adopted
external source has not been represented
in OWL, or does not use the same
foundation as NIFSTD,
the terminology is adapted to
OWL/RDF in the context of the
NIFSTD foundational layer ontologies
6. NIFSTD DESIGN PRINCIPLES
• Single Inheritance for Named Classes
– Follows simple inheritance principle for named
classes
– An asserted named class can have only one named
class as its superclass
– Promotes the named classes to be univocal and to
avoid ambiguities
• Classes with multiple named superclasses
– Can be inferred using automated reasoners
– Saves a great deal of manual labor and minimizes
human errors
• Alan Rector’s Normalization principles.
7. DESIGN PRINCIPLES
• Unique Identifiers and Annotation Properties.
– NIFSTD entities are identified by a unique identifier
and accompanied by a variety of annotation
properties
• Derived from Dublin Core Metadata (DC) and Simple
Knowledge Organization System (SKOS) model.
• Synonyms, acronyms, definition, defining source etc.
– Reuse the same URI through MIREOTed classes from
external source,
• Allows to avoid extra mapping annotations, e.g., class
identifiers remain unaltered.
8. DESIGN PRINCIPLES
• Annotation properties associated with
versioning different levels of contents
– creation date and modification dates
– file level versioning for each of the modules
– annotations for retiring antiquated concept
definitions
• hasFormerParentClass and isReplacesByClass etc.
• tracking former ontology graph position and
replacement concepts.
9. DESIGN PRINCIPLES
• Object Properties and Bridge Modules.
– Mostly drawn from OBO Relations Ontology (OBO-RO)
– Intra-module relations are kept within the same
module
• ONLY universal restrictions are considered
– e.g., partonomy relations within different brain regions
– The cross-module relations are specified in separate
bridging modules
• Modules that only contain logical restrictions on a set of
classes assigned between multiple modules.
• Allows main domain modules—e.g., anatomy, cell type, etc.
to remain independent of one another
10. DESIGN PRINCIPLES
Helps keeping the modularity principles intact
facilitate extensions for broader communities without NIF-centric views
These bridging modules can easily be excluded in order to focus on core modules
Two example bridging modules in NIFSTD
11. TYPICAL KNOWLEDGE MODEL
A typical knowledge model in NIFSTD. Both cross-modular and intra-modular
classes are associated through object properties mostly drawn from the OBO
Relations ontology (RO).
13. TYPICAL USE OF ONTOLOGY IN NIF
• Basic feature of an ontology
– Organizing the concepts involved in a domain
into a hierarchy and
– Precisely specifying how the classes are
‘related’ with each other (i.e., logical axioms)
• Explicit knowledge are asserted but implicit
logical consequences can be inferred
– A powerful feature of an ontology
13
14. Class name Asserted necessary conditions
Cerebellum Purkinje cell 1. Is a ‘Neuron’
2. Its soma lies within 'Purkinje cell layer of cerebellar cortex’
3. It has ‘Projection neuron role’
4. It uses ‘GABA’ as a neurotransmitter
5. It has ‘Spiny dendrite quality’
Class name Asserted defining (necessary & sufficient) expression
Cerebellum neuron Is a ‘Neuron’ whose soma lies in any part of the
‘Cerebellum’ or ‘Cerebellar cortex’
Principal neuron Is a ‘Neuron’ which has ‘Projection neuron role’, i.e., a
neuron whose axon projects out of the brain region in
which its soma lies
GABAergic neuron Is a ‘Neuron’ that uses ‘GABA’ as a neurotransmitter
ONTOLOGY – ASSERTED HIERARCHY
14
15. NIF CONCEPT-BASED SEARCH
• Search Google: GABAergic neuron
• Search NIF: GABAergic neuron
– NIF automatically searches for types of
GABAergic neurons
Types of GABAergic
neurons
16. NIFSTD CURRENT VERSION
• Key feature: Includes useful defined concepts to
infer useful classification
NIF Standard Ontologies 16
17. NIFSTD AND NEUROLEX WIKI
• Semantic wiki platform
• Provides simple forms for
structured knowledge
• Can add
concepts, properties
• Generate hierarchies
without having to learn
complicated ontology tools
• Good teaching tool for
principles behind
ontologies
• Community can contribute
NIF Standard Ontologies
17
Stephen D. Larson et al.
18. NeuroLex vs.NIFSTD
NeuroLex NIFSTD
A semantic mediawiki based website
containing the content of the NIFSTD
plus additional community contributions
Collection of cohesive, unified modular
ontologies deployed in OWL
Categories Classes
Content is fluid and can be updated at
any time.
Structure is based on OBO foundry
principles
Defines relationships between
categories as simple properties
Defines relationships between classes as
OWL restrictions derived from RO
At a glance guide to the differences between NeuroLex and NIFSTD
Larson et. al
19. Top Down Vs. Bottom up
Top-down ontology construction
• A select few authors have write privileges
• Maximizes consistency of terms with each other
• Making changes requires approval and re-publishing
• Works best when domain to be organized has: small corpus, formal
categories, stable entities, restricted entities, clear edges.
• Works best with participants who are: expert catalogers, coordinated users, expert
users, people with authoritative source of judgment
Bottom-up ontology construction
• Multiple participants can edit the ontology instantly
• Control of content is done after edits are made based on the merit of the content
• Semantics are limited to what is convenient for the domain
• Not a replacement for top-down construction; sometimes necessary to increase flexibility
• Necessary when domain has: large corpus, no formal categories, no clear edges
• Necessary when participants are: uncoordinated users, amateur users, naïve catalogers
• Neuroscience is a domain that is less formal and neuroscientists are more uncoordinated
Larson et. al
NIFSTD
NEUROLEX
23. ACCESS TO NIFSTD CONTENTS
• NIFSTD is available as
– OWL Format
http://ontology.neuinfo.org
– RDF and SPARQL Endpoint
http://ontology.neuinfo.org/spar
ql-endpoint.html
• Specific contents through web
services
– http://ontology.neuinfo.org/onto
quest-service.html
• Available through NCBO Bioportal
– Provides annotation and mapping
services
– http://bioportal.bioontology.org/
NIF Standard Ontologies 23
25. SUMMARY AND CONCLUSIONS
• NIF with NIFSTD provides an example of how ontologies can
be practically applied to enhance search and data integration
across diverse resources
• We believe, we have defined a process to form complex
semantics to various neuroscience concepts through NIFSTD
and through NeuroLex collaborative environment.
• NIF encourages the use of community ontologies
• Moving towards building rich knowledgebase for
Neuroscience that integrates with larger life science
communities.
25
26.
27. Point of Discussion
• Gaining OBO Foundry community consensus
for a production system is difficult as we often
need to move quickly along with the project
• We rather favor a system whereby we start
with minimal complexity as required and add
more as the ontologies evolve over time
towards perfection
• What should be the most effective way to
collaborate and gain community consensus?
Editor's Notes
Analogy for modularization of ontologies…Given 5 different lines with different colors and a given a set of possible angular relationships easier to build different shapes and patterns
Here is an example, that would hopefully illustrates the strengths and usefulness of having our ontology. NIFSTD has various neuron types with an asserted simple hierarchy within the NIF-Cell module (here is an example with five neuron types). However, we assert various logical restrictions about these neurons.
Having the defined classes enabled us to have useful concept-based queries through the NIF search interface. For example, while searching for ‘GABAergic neuron’, the system recognizes the term as ‘defined’ from the ontology, and looks for any neuron that has GABA as a neurotransmitter (instead of the lexical match of the search term like in Google) and enhances the query over those inferred list of neurons.
One of the largest roadblocks that we encountered during our ontology development was the lack of tools for domain experts to contribute their knowledge to NIFSTD. To bridge these gaps, NIF has created NeuroLex (http://neurolex.org), a semantic wiki interface for the domain experts as an easy entry point to the NIFSTD contents. It has been extensively used in the area of neuronal cell types where NIF is working with a group of neuroscientists such as Gordon Shephard and Georgio Ascoli, to create a comprehensive list of neurons and their properties.
We envision NeuroLex as the main entry point for the broader community to access, annotate, edit and enhance the core NIFSTD content. The peer-reviewed contributions in the media wiki are later implanted in formal OWL modules. While the properties in NeuroLex are meant for easier interpretation, the restrictions in NIFSTD are usually based on rigorous OBO-RO standard relations. For example, the property ‘soma located in’ is translated as ‘Neuron X’ has_part some (‘Soma’ and (part_of some ‘Brain region Y’)) in NIFSTD.
While the principles promote developing highly interoperable and reusable reference ontologies in ideal cases, following some of them in a rigid manner is often proven to be too ambitious for day-to-day development.