Dr. Barry Smith is the director of the National Center for Ontological Research. He discussed how semantic technology can help solve the problem of data silos by enabling data from different sources to be integrated and analyzed together. Ontologies, or controlled vocabularies, can be used to semantically enhance data by tagging it in an interoperable way. This allows the data to be retrieved, understood, and used by others even if they were not involved in creating the data. The semantic enhancement approach aims to break down silos incrementally by coordinating the creation of ontologies and linking datasets through shared terms.
IAO-Intel: An Ontology of Information Artifacts in the Intelligence DomainBarry Smith
We describe on-going work on IAO-Intel, an information artifact ontology developed as part of a suite of ontologies designed to support the needs of intelligence community. IAO-Intel provides a controlled, structured vocabulary for the consistent formulation of metadata about documents, images, emails and other carriers of information. It will provide a resource for uniform explication of the terms used in multiple existing military dictionaries, thesauri and metadata registries, thereby enhancing the degree to
which the content formulated with their aid will be available to computational reasoning.
Presented at the 2013 STIDS (Semantic Technology for Intelligence, Defense and Security) conference: http://stids.c4i.gmu.edu/
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
IAO-Intel: An Ontology of Information Artifacts in the Intelligence DomainBarry Smith
We describe on-going work on IAO-Intel, an information artifact ontology developed as part of a suite of ontologies designed to support the needs of intelligence community. IAO-Intel provides a controlled, structured vocabulary for the consistent formulation of metadata about documents, images, emails and other carriers of information. It will provide a resource for uniform explication of the terms used in multiple existing military dictionaries, thesauri and metadata registries, thereby enhancing the degree to
which the content formulated with their aid will be available to computational reasoning.
Presented at the 2013 STIDS (Semantic Technology for Intelligence, Defense and Security) conference: http://stids.c4i.gmu.edu/
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Building Biomedical Knowledge Graphs for In-Silico Drug DiscoveryVaticle
The rapid development and spread of analytical tools in the biomedical sciences has produced a variety of information about all sorts of biological components and their functions. Though important individually, their biological characteristics need to be understood in relation to the interactions they have with other biological components, which requires the integration of vast amounts of complex, semantically-rich, heterogenous data.
Traditional systems are inadequate at accurately modelling and handling data at this scale and complexity, making solutions that speed up the integration and querying of such data a necessity.
In this talk, we present various approaches being used in organisations to build biomedical computational pipelines to address these problems using tools such as Machine Learning and TypeDB. In particular, we discuss how to create an accurate and scalable semantic representation of molecular level biomedical data by presenting examples from drug discovery, precision medicine and competitive intelligence.
Speaker: Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle, dedicated to building a strongly-typed database for intelligent systems. He works directly with TypeDB's open source and enterprise users so they can fulfil their potential with TypeDB and change the world. He focuses mainly in life sciences, cyber security, finance and robotics.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
Knowledge Graph Research and Innovation ChallengesSören Auer
Gives an overview on some challenges regarding the combination of machine-learning and knowledge graph technologies and the vision of devising a concept of Cognitive Knowledge Graphs consisting of graphlets instead of mere entity descriptions.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
TF-IDF, short for Term Frequency - Inverse Document Frequency, is a text mining technique, that gives a numeric statistic as to how important a word is to a document in a collection or corpus. This is a technique used to categorize documents according to certain words and their importance to the document
The FAIR data movement and 22 Feb 2023.pdfAlan Morrison
To realize the promise of FAIR data, companies must be data mature. They must adopt data-centric architecture and the #FAIR (findable, accessible, interoperable and reusable) principles. When they do, the data they need will be linked and self-describing. The data when queried will tell you where it is.
A desiloed, #semantic graph data abstraction--the only feasible means behind creating FAIR data at this point--is not only the means to data discovery, but also a path to model-driven development and data sharing at scale, both of which will break an organization's habit of duplicating data and logic.
This webinar highlights fresh enterprise case studies that are starting to realize the dream of #FAIRdata, as well as how these companies are succeeding:
- Zero copy integration: How to think about eliminating #dataduplication and stop the application buying binge that only exacerbates the problem.
- Dynamic, unified data model: Standard graphs provide a means of modeling once, use anywhere, for conceptual, logical and physical purposes all at once.
- Persuasion and teamwork: The #graph approach provides an ideal way to loop business units and domain experts in and empower them to recommend model changes that are easily implemented.
The whole process is bringing #enterprises like Walmart, Uber, Goldman Sachs and Nokia into the age of #contextualcomputing. Learn how to be a fast follower by thinking big, but starting small.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Building Biomedical Knowledge Graphs for In-Silico Drug DiscoveryVaticle
The rapid development and spread of analytical tools in the biomedical sciences has produced a variety of information about all sorts of biological components and their functions. Though important individually, their biological characteristics need to be understood in relation to the interactions they have with other biological components, which requires the integration of vast amounts of complex, semantically-rich, heterogenous data.
Traditional systems are inadequate at accurately modelling and handling data at this scale and complexity, making solutions that speed up the integration and querying of such data a necessity.
In this talk, we present various approaches being used in organisations to build biomedical computational pipelines to address these problems using tools such as Machine Learning and TypeDB. In particular, we discuss how to create an accurate and scalable semantic representation of molecular level biomedical data by presenting examples from drug discovery, precision medicine and competitive intelligence.
Speaker: Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle, dedicated to building a strongly-typed database for intelligent systems. He works directly with TypeDB's open source and enterprise users so they can fulfil their potential with TypeDB and change the world. He focuses mainly in life sciences, cyber security, finance and robotics.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
Knowledge Graph Research and Innovation ChallengesSören Auer
Gives an overview on some challenges regarding the combination of machine-learning and knowledge graph technologies and the vision of devising a concept of Cognitive Knowledge Graphs consisting of graphlets instead of mere entity descriptions.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
TF-IDF, short for Term Frequency - Inverse Document Frequency, is a text mining technique, that gives a numeric statistic as to how important a word is to a document in a collection or corpus. This is a technique used to categorize documents according to certain words and their importance to the document
The FAIR data movement and 22 Feb 2023.pdfAlan Morrison
To realize the promise of FAIR data, companies must be data mature. They must adopt data-centric architecture and the #FAIR (findable, accessible, interoperable and reusable) principles. When they do, the data they need will be linked and self-describing. The data when queried will tell you where it is.
A desiloed, #semantic graph data abstraction--the only feasible means behind creating FAIR data at this point--is not only the means to data discovery, but also a path to model-driven development and data sharing at scale, both of which will break an organization's habit of duplicating data and logic.
This webinar highlights fresh enterprise case studies that are starting to realize the dream of #FAIRdata, as well as how these companies are succeeding:
- Zero copy integration: How to think about eliminating #dataduplication and stop the application buying binge that only exacerbates the problem.
- Dynamic, unified data model: Standard graphs provide a means of modeling once, use anywhere, for conceptual, logical and physical purposes all at once.
- Persuasion and teamwork: The #graph approach provides an ideal way to loop business units and domain experts in and empower them to recommend model changes that are easily implemented.
The whole process is bringing #enterprises like Walmart, Uber, Goldman Sachs and Nokia into the age of #contextualcomputing. Learn how to be a fast follower by thinking big, but starting small.
Data Integration in Multi-sources Information Systemsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Framework for Geospatial Web Services for Public Health by Dr. Leslie LenertWansoo Im
A Framework for Geospatial Web Services for Public Health
by Leslie Lenert, MD, MS, FACMI, Director
National Center for Public Health Informatics, CCHIS, CDC
June 8 2009 URISA Public Health Conference
uploaded by Wansoo Im, Ph.D.
URISA Membership Committee Chair
http://www.gisinpublichealth.org
FAIR data_ Superior data visibility and reuse without warehousing.pdfAlan Morrison
The advantages of semantic knowledge graphs over data warehousing when it comes to scaling quality, contextualized data for machine learning and advanced analytics purposes.
Leveraging Open Source Technologies to Enable Scientific Archiving and Discovery; Steve Hughes, NASA; Data Publication Repositories
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
As the volume and complexity of data from myriad Earth Observing platforms, both remote sensing and in-situ increases so does the demand for access to both data and information products from these data. The audience no longer is restricted to an investigator team with specialist science credentials. Non-specialist users from scientists from other disciplines, science-literate public, to teachers, to the general public and decision makers want access. What prevents them from this access to resources? It is the very complexity and specialist developed data formats, data set organizations and specialist terminology. What can be done in response? We must shift the burden from the user to the data provider. To achieve this our developed data infrastructures are likely to need greater degrees of internal code and data structure complexity to achieve (relatively) simpler end-user complexity. Evidence from numerous technical and consumer markets supports this scenario. We will cover the elements of modern data environments, what the new use cases are and how we can respond to them.
Neuroinformatics_Databses_Ontologies_Federated Database.pptxJagannath University
This will introduce and describe NIF(Neuroscience information framework), Federated databases, data federation vs data warehouse, ontology, ontology vs database, steps in creating ontology.
Neuroinformatics Databases Ontologies Federated Database.pptxJagannath University
Neuroscience Information Framework(NIF), Federated Database, Data Federation vs Data warehouse, ontology, steps in creating ontology, ontology vs database
The current status of Linked Open Data (LOD) shows evidence of many datasets available on the Web in RDF. In the meantime, there are still many challenges to overcome by organizations in their journey of publishing five stars datasets on the Web. Those challenges are not only technical, but are also organizational. At this moment where connectionist AI is gaining a wave of popularity with many applications, LOD needs to go beyond the guarantee of FAIR principles. One direction is to build a sustainable LOD ecosystem with FAIR-S principles. In parallel, LOD should serve as a catalyzer for solving societal issues (LOD for Social Good) and personal empowerment through data (Social Linked Data).
Data accessibility and the role of informatics in predicting the biosphereAlex Hardisty
The variety, distinctiveness and complexity of life – biodiversity in other words and by implication the ecosystems in which it is situated – is our life support system. It is absolutely essential and more important than almost everything else but it is typically taken for granted. Today’s big societal challenges – food and water security, coping with environmental change and aspects of human health – are beyond the abilities of any one individual or research group to solve. Solving them depends not only on collaboration to deliver the appropriate scientific evidence but increasingly on vast amounts of data from multiple sources (environmental, taxonomic, genomic and ecological) gathered by manual observation and automated sensors, digitisation, remote sensing, and genetic sequencing. In April 2012 we called the biodiversity and ecosystems research communities to arms to formulate a consensus view on establishing an infrastructure to improve the accessibility of the ever-increasing volumes of biological data. We published the whitepaper: “A decadal view of biodiversity informatics: challenges and priorities” that has since been viewed more than 24,000 times. We envisage a shared and maintained multi-purpose network of computationally-based processing services sitting on top of an open data domain. By open data domain we mean data that is accessible i.e., published, registered and linked. BioVeL, pro-iBiosphere, ViBRANT and other FP7 funded projects have all explored aspects of this vision.
Achieving Semantic Integration of Medical Knowledge for Clinical Decision Sup...AmrAlaaEldin12
Abstract. Enhancing the outputs of the Clinical Decision Support systems (CDS) is a permanent concern for many research communities, which have to deal with an abundance of entities, data, structures, methods, application, tools, and so on. In the few past decades, there were theorized and standardized tech- nologies that could help researchers to obtain better results. The paper presents a method to enrich the inputs of the CDS through a semantic integration of sev- eral medical knowledge sources, by using the Topic Maps standard, in order to obtain more refined medical recommendations. Future research directions and challenges are summarized and conclusions are issued.
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
Similar to Ontology Tutorial: Semantic Technology for Intelligence, Defense and Security (20)
We can distinguish two families of approaches to the building of ontologies -- corresponding roughly to the contrast between 'neats' and 'scruffies' in artificial intelligence research. We describe the implications of each approach for the building of an ontology of philosophy, focusing especially on the Indiana Philosophy Ontology (InPhO) project led by Colin Allen.
A video presentation based on these slides is available here: https://www.youtube.com/watch?v=5HV3M0NvyPM
An application of Basic Formal Ontology to the Ontology of Services and Commo...Barry Smith
Basic Formal Ontology (BFO) is an upper level ontology widely used in biomedical informatics and other domains to support information integration across disciplines, We here apply BFO to the development of a coherent ontological treatment of the distinction between commodities and services.
Ways of Worldmarking: The Ontology of the EruvBarry Smith
‘Eruv’ is a Hebrew word meaning literally ‘mixture’ or ‘mingling’. An eruv is an urban region demarcated within a larger urban region by means of a boundary made up of telephone wires or similar markers. Through the creation of the eruv, the smaller region is turned symbolically (halachically = according to Jewish law) into a private domain. So long as they remain within the boundaries of the eruv, Orthodox Jews may engage in activities that would otherwise be prohibited on the Sabbath, such as pushing prams or wheelchairs, or carrying walking sticks. There are eruvim in many towns and university campuses throughout the world. There are five eruvim in Chicago, five in Brooklyn, twenty three in Queens and Long Island, and at least three in Manhattan. The US Supreme Court is (like most other major US Federal Government buildings) located within the eruv of Washington DC. In many cases, not all of those living within or near the area of an actual or proposed eruv will themselves be Orthodox Jews, and this has sometimes led to protests against eruv creation. For further details see http://ontology.buffalo.edu/smith/articles/eruv.pdf
Contemporary philosophy of collective agency, as illustrated by the work of Searle, Bratman, Gilbert, Pettit and others, focuses predominantly on small groups of agents sharing common goals. In his groundbreaking paper “Massively Shared Agency” of 2014, Scott Shapiro shows the limits of this approach when dealing with the large groups of agents that form industrial corporations, armies, or systems of law enforcement. Such groups will involve alienated or uncommitted participants pursuing motives of their own. And as Shapiro shows, they can manifest shared agency only when the actions of all participants are coordinated through authority structures organized hierarchically. Here I wish to focus on that dimension of massively shared agency that has to do with the transmission of authority. I will show that while such transmission almost always involves communication through speech (or through the digital counterparts of speech), transmission of this sort is too transient,
and falls short of creating the type of enduring intermeshing of plans and intentions that is required for the imposition of hierarchical authority structures across large organizations. To create and maintain the needed hierarchical authority structures what is required are complexes of intermeshed documents. Such documents provide for what we can think of as a division of deontic labor, allowing plans, orders, and obligations to be meshed together over time.
Presented at the conference on Truth, Image and Normativity, Cagliari, Sardinia, October 23, 2014
Increasingly, biological and clinical scientists are using ontologies to serve integration and coordination of research across diverse organisms and scientific fields. Ontologies, in this context, are logically organized collections of terms defined in such a way that they can be used consistently across multiple disciplines to describe clinical and experimental data. Ontologies are used in aging research to unify experimental results from a broad range of fields including genetics, proteomics, (stem) cell biology, oncology, model organism biology, psychogerontology, and many more. We will explore against this background questions such as the following: What is aging? What is premature aging? And more specifically: Is aging a disease?
In a lecture, delivered in Vienna in 1894 and dedicated "to the academic youth of Austria-Hungary", Franz Brentano outlined four phases of advance and decline which he saw as providing the key to the understanding of the history of Western philosophy. In the first cycle, in antiquity, the initial advancing phase culminated in the work of Aristotle, and was followed by three phases of decline, terminating in the irrational mysticism of the Neo-Pythagoreans. These four phases then repeated themselves: in the Middle Ages, beginning with Aquinas and ending with the "learned ignorance" of Nicholas of Cusa; and then in the modern period, beginning with Bacon and reaching its low point in the work of Kant, Fichte, Schelling, and Hegel. In the contemporary era we are currently witnessing the end of the fourth cycle in the work of (for example) Derrida, Rorty; but also the beginnings of a new, fifth cycle, which is described in the talk. (Presented at the conference Consequences of Realism, Rome, May 4-6, 2014.)
There is blind chess but there is no blind poker. This is because to play poker essentially involves the use of cards and chips (or representations of or proxies for cards and chips). A game of chess, in contrast, may involve only the exchange of speech acts. We draw initial conclusions for the ontology of poker from this distinction.
Talk presented on March 14, 2014
For video presentation see http://www.youtube.com/edit?video_id=PgwpR9NPKzw
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...Barry Smith
Presentation to the Clinical and Research Ethics Seminar, Clinical and Translational Science Center, Buffalo, January 21, 2014
https://immport.niaid.nih.gov/
http://youtu.be/booqxkpvJMg
Presentation to ImmPort Science Meeting, February 27, 2014 on the proper treatment of value sets in the Immport Immunology Database and Analysis Portal
The Philosophome: An Exercise in the Ontology of the HumanitiesBarry Smith
Presentation at the opening of the Humanomics Research Centre at the University of Copenhagen, 7 February 2014
For background links see: http://philosophome.org/
We describe the methodology of omics disciplines in biology, and consider how analogous methods might be applied in humanities disciplines, focusing specifically on philosophy. We conclude by outlining a possible strategy for a research center in humanomics, identifying possible sources of data in the philosophical domain.
Talk presented at the conference on the Philosophy of Emerging Media, Boston University, October 26-27, 2013
If you try to find information about a gene or a molecule or a restaurant or a sports team or a politician on the web, it’s likely that some ontology will be involved in your search. An ontology is (briefly put) a semantically organized consensus representation of the types of entities in a given domain and of the relations between these entities – it is something like a large graph of the way some part of the world is structured. So important have ontologies become to organizations such as the BBC or the New York Times, that there is a running joke in the Semantic Web community to the effect that the Columbia School of Journalism is about to be renamed the Columbia School of Journalism and Ontology. I will attempt to draw conclusions from these phenomena concerning the ways in which social interactions are being influenced, and to some degree also transformed, by digital media.
Surveys a series of ethical, economic, clinical and also safety issues relating to the application of informatics to healthcare, focusing especially on the role of informatics in the Patient Protection and Affordable Care Act. Talk presented in the University at Buffalo Clinical/Research Ethics Seminar - Ethics, Informatics and Obamacare, November 20, 2012. Slides are available here: http://ontology.buffalo.edu/13/ethics-informatics-obamacare.pptx
The idea underlying biomedical ontology is that, if common terms are used to annotate or tag heterogeneous data collected by scientists working in different disciplines, then these data will be more easily reused for integration and
analysis. To this end, the terms in ontologies need to be carefully defined. Smith examines definitions
of terms central to ageing research in this light, focusing on the Gene Ontology (GO), the Foundational Model of Anatomy Ontology (FMA) and the Plant Ontology (PO).
ImmPort strategies to enhance discoverability of clinical trial dataBarry Smith
Describes strategies for submission of clinical trial data to the NIAID Immunology Database and Analysis Portal in order to advance discoverability, comparability and analysis
Introduces the idea of a theory of document acts, analogous to the theory of social acts advocated in 1913 by Adolf Reinach, and to the theory of speech acts advanced by Austin and Searle.
Ontology and the National Cancer Institute Thesaurus (2005)Barry Smith
The National Cancer Institute Thesaurus is described by its authors as "a biomedical vocabulary that provides consistent, unambiguous codes and definitions for concepts used in cancer research" and which "exhibits ontology-like properties in its construction and use". We performed a qualitative analysis of the Thesaurus in order to assess its conformity with principles of good practice in terminology and ontology design.
MATERIALS AND METHODS:
We used both the on-line browsable version of the Thesaurus and its OWL-representation (version 04.08b, released on August 2, 2004), measuring each in light of the requirements put forward in relevant ISO terminology standards and in light of ontological principles advanced in the recent literature.
RESULTS:
We found many mistakes and inconsistencies with respect to the term-formation principles used, the underlying knowledge representation system, and missing or inappropriately assigned verbal and formal definitions.
CONCLUSION:
Version 04.08b of the NCI Thesaurus suffers from the same broad range of problems that have been observed in other biomedical terminologies. For its further development, we recommend the use of a more principled approach that allows the Thesaurus to be tested not just for internal consistency but also for its degree of correspondence to that part of reality which it is designed to represent.
Introduction to the Logic of DefinitionsBarry Smith
We focus on definitions for common nouns such as 'human being' and 'leukocyte'
A definition, for terms such as this, is a statement of necessary and sufficient conditions for an entity's falling under an instance of the type to which the term refers.
Such definitions should be of Aristotelian form, which means that they specify the genus, and state what it is about certain instances of this genus in virtue of which they are instances of the type (species) defined.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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2. Barry Smith – who am I?
Director: National Center for Ontological Research (Buffalo)
Founder: Ontology for the Intelligence Community (OIC, now STIDS)
conference series
Ontology work for:
NextGen (Next Generation) Air Transportation System
National Nuclear Security Administration, DoE
Joint-Forces Command Joint Warfighting Center
Army Net-Centric Data Strategy Center of Excellence
and for many national and international science and healthcare
agencies
2
3. The problem: many, many silos
• DoD spends more than $6B annually developing a
portfolio of more than 2,000 business systems
and Web services
• these systems are poorly integrated
• deliver redundant capabilities,
• make data hard to access, foster error and waste
• prevent secondary uses of data
https://ditpr.dod.mil/ Based on FY11 Defense Information Technology
Repository (DITPR) data
3
4. Some questions
• How to find data?
• How to understand data when you find it?
• How to use data when you find it?
• How to compare and integrate with other data?
• How to avoid data silos?
• How to make a battlefield situation rapidly
understandable
• How to decide what to do next in a battlefield
situation 4
5. The problem of retrieval, integration
and analysis of siloed data
• is not confined to the DoD
• affects every domain due to massive legacy of
non-interoperable data models and data
systems
• and as new systems are created along the
same lines, the situation is constantly getting
worse.
5/24
6. One solution: Über-model
– must be built en bloc beforehand
– inflexible, unresponsive to warfighter needs
– heavy-duty manual effort for both construction
and ingestion, with loss and/or distortion of
source data and data-semantics
– might help with data retrieval and integration
– but offers limited analytic capability
– has a limited lifespan because it rests on one point
of view
6
7. A better solution, begins with the Web
(net-centricity)
• You build a site
• Others discover the site and they link to it
• The more they link, the more well known the
page becomes (Google …)
• Your data becomes discoverable
7
8. 1. Each group creates a controlled vocabulary of
the terms commonly used in its domain, and
creates an ontology out of these terms using
OWL syntax
4. Binds this ontology to its data and makes these
data available on the Web
5. The ontologies are linked e.g. through their use
of some common terms
6. These links create links among all the
datasets, thereby creating a „web of data‟
The roots of Semantic Technology
9. Where we stand today
• increasing availability of semantically enhanced
data and semantic software
• increasing use of OWL in attempts to create
useful integration of on-line data and
information
• “Linked Open Data” the New Big Thing
9
11. The problem: the more Semantic
Technology is successful, they more it fails
The original idea was to break down silos via
common controlled vocabularies for the tagging
of data
The very success of the approach leads to the
creation of ever new controlled vocabularies –
semantic silos – as ever more ontologies are
created in ad hoc ways
Every organization and sub-organization now
wants to have its own “ontology”
The Semantic Web framework as currently
conceived and governed by the W3C yields
minimal standardization
11
14. 14
The problem of joint / coalition operations
Fire
Support
LogisticsAir Operations
Intelligence
Civil-Military
Operations
Targeting
Maneuver
&
Blue
Force
Tracking
15. An alternative solution:
Semantic Enhancement
A distributed incremental strategy of coordinated annotation
– data remain in their original state (is treated at ‘arms length’)
– ‘tagged’ using interoperable ontologies created in tandem
– allows flexible response to new needs, adjustable in real time
– can be as complete as needed, lossless, long-lasting because
flexible and responsive
– big bang for buck – measurable benefit even from first small
investments
– strong tool support for data analysis
– multiple successful precedents
The strategy works only to the degree that it rests on shared
governance and training
15
16. 16Jonathan Underly - EIW Program
Manager
25
BusinessEnterpriseArchitecture(BEA)
ectly:
prise
tics
liance
ortfolio
gement
DoD EA
HR Domain
Vocabulary
Acq Domain
Vocabulary
Log Domain
Vocabulary
Fin Domain
Vocabulary
Real Prop Domain
Vocabulary
Svc Member OUID
(GFMDI)(EDIPI)
Warfighter Domain
Vocabulary
E2E BP executes via BEA directly
BP models
uniformly
described
OMG Primitives
Conformance class
2.0
Data described in RDF Relationship described in OWLandards Legend: DoD Authoritative Data Source
s End-to-
Process
P)
Dennis E. Wisnosky: A Vision for DoD Solution Architectures
17. What can semantic technology do
for you?
• software, hardware, business processes, target domains
of interest change rapidly
• but meanings of common words change only slowly
• semantic technology allows these meanings to be
encoded separately from data files and from application
code – decoupling of semantics from data and
applications
• ontologies (controlled, logically
structured, vocabularies), which are used to enhance
legacy and source content
− to make these contents retrievable even by those not
involved in their creation
− to support integration of data deriving from heterogeneous
sources
− to allow unanticipated secondary uses
17
18. The capability for massing timely and
accurate artillery fires by dispersed
batteries upon single targets required
• real-time communications of a sort that could
– create a common operational picture that could take account
of new developments in the field
– thereby transforming dispersed batteries into a single system
of interoperable modules.
• this was achieved (in Ft. Sill around 1939) through
– a new type of information support
– a new type of governance and training
– new artillery doctrine
18/24
19. The capability for massing timely and
accurate intelligence “fires”
will similarly require real-time pooling of information of a
sort that can
– create a common operational picture able to be constantly
updated in light of new developments in the field
– thereby transforming dispersed data artifacts within the
Cloud into a single system of interoperable modules
This will require in turn
– a new type of support (for semantic enhancement of data)
– a new type of governance and training
– new intelligence doctrine to include applied semantics
19/24
21. ICODES
• from 2 days to 10 minutes manual coding effort
• more elaborate loading scenarios can be supported
• different forces can share the same ships because
their loading categories are built into the same
ontology
• high flexibility as cargoes, ships and loading
technologies change
21
Performance Metrics
Tested Procedure V 3.0 (1998) V 5.0( 2001) V 5.4 (2005)
Create 2-ship load-plan, 2,400 normal cargo items 20 min 8 min 1.5 min
Create 2-ship load-plan, 1,200 hazardous cargo items 25 min 11 min 2.5 min
Unload inventory of 2,400 items from 2 ships 10 min 5 min 1.0 min
25. How to find data?
How to find other people’s data?
How to find your own data?
How to reason with data when you find it?
How to combine data from multiple sources?
25
26. 26
How to solve the problem of making
the data we find queryable and re-
usable by others?
Part of the solution must involve:
standardized terminologies and
coding schemes, analogous to the SI
System of Units
27. 27
ontologies = standardized labels
designed for use in annotations
to make the data cognitively
accessible to human beings
and algorithmically accessible
to computers
40. Common legends
• help human beings use and understand complex
representations of reality
• help human beings create useful complex
representations of reality
• help computers process complex representations of
reality
• help glue data together
But common legends serve these purposes only
if the ontologies themselves are developed in a
coordinated, non-redundant fashion
40
41. A good solution to this silo problem must be:
• modular
• incremental
• independent of hardware and software
• bottom-up
• evidence-based
• revisable
• incorporate a strategy for motivating potential
developers and users
41
42. RELATION
TO TIME
GRANULARITY
CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
(FMP, CPRO) Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
The Open Biomedical Ontologies (OBO) Foundry
42
43. CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
(FMP, CPRO) Phenotypic
Quality
(PaTO)
Organism-Level
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
Cellular Process
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RNAO, PRO)
Molecular Function
(GO)
Molecular
Process
(GO)
rationale of OBO Foundry coverage
GRANULARITY
RELATION TO
TIME
43
44. RELATION
TO TIME
GRANULARITY
CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
COMPLEX OF
ORGANISMS
Family, Community,
Deme, Population
Organ
Function
(FMP, CPRO)
Population
Phenotype
Population
Process
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO) Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
Population-level ontologies 44
45. RELATION
TO TIME
GRANULARITY
CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
(FMP, CPRO)
Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
Environment Ontology
environments
45
46. Creation of new ontology
consortia, modeled on the OBO Foundry
46
NIF Standard Neuroscience Information
Framework
eagle-I
Ontologies
used by VIVO and CTSA
connect for publications,
patents, credentials, data and
sample collections
IDO Consortium Infectious Disease Ontology
48. Levels of coordination
• What there is
– XML – syntactic interoperability
– RDF, OWL, CL – representational interoperability
(URIs plus triples) and semantic interoperability (=
exposed semantics) -- you use ‘Person’, I use
‘P’, he uses ‘Persn’ …
– Creating genuine semantic
interoperability, especially with more expressive
languages such as OWL or Common Logic is in
practice impossible across broad heterogeneous
communities
48
49. The problem
• Interoperability is necessary but not sufficient. It allows
automatic processing of content, but only if you can
sustain alignments between multiple independent
vocabularies simultaneously.
• For example, you have class Human Being with subclass
Person in one system and P in another. If you want to
establish that Person = P, you can do this in such a way
that it will be understandable to you and everyone else,
and the system will work without any additional help.
• However, if you then have to say that Persn = P, and will
have to make similar assertions over and over again and
to keep the alignments consistent over time, then you
will rapidly lose control.
49
50. The SE solution: Ontology (only) at the center
• Establish common ontology content, which we and
our collaborators (and our software control)
• Keep this content consistent and non-redundant as it
evolves.
• Seek semantic sharing only in the SE environment.
– so what SE brings is semantic interoperability plus
constrained syntax
– it brings a kind of substitute for semantic
interoperability of source data models, through
the use by annotators of ontologies from the
single evolving SE suite
50
51. SE annotations applied to source data in the DSC
Cloud
Dr Malyuta will discuss the DSC Cloud Dataspace
in the next segment
See also tomorrow’s presentation:
• 11:30 Horizontal Integration of Warfighter
Intelligence Data
51
52. Distributed Common Ground System – Army
(DCGS-A)
Semantic Enhancement
of the Dataspace
on the Cloud
Dr. Tatiana Malyuta
New York City College of Technology
of the City University of New York
53. Integrated Store of Intelligence Data
• Lossless integration without heavy pre-
processing
• Ability to:
– Incorporate multiple integration models / approaches /
points of view of data and data-semantics
– Perform continuous semantic enrichment of the integrated
store
• Scalability
53
54. Solution Components
• Cloud implementation
– Cloudbase (Accumulo)
• Data Representation and Integration
Framework
– Comprehensive unified representation of
data, data semantics, and metadata
• This work was funded by US Army CERDEC
Intelligence and Information Warfare
Directorate (I2WD).
54
55. Dealing with Semantic Heterogeneity
Physical Integration.
A separate data store
homogenizing
semantics in a
particular data-model
– works only for
special cases, entails
loss and distortion of
data and semantics,
creates a new data
silo.
Virtual integration. A
projection onto a
homogeneous data-
model exposed to
users – is more
flexible, but may have
the problem of data
availability (e.g.
military, intelligence).
Also, a particular
homogeneous model
has limited usage,
does not expose all
content, and does not
support enrichment
55
56. Pursuit of the Holy Grail of Intelligence
Data Integration
•In a highly dynamic semantic environment
evolving in ad hoc ways
– how to have it all and have it available immediately
and at any time?
• Traditional physical and virtual integration approaches
fail to respond to these requirements
– how to use these data resources efficiently
(integrate, query, and analyze)?
56
57. Workable Solution
A physical store
incorporating
heterogeneous contents.
Data Representation and
Integration Framework
(DRIF) – is based on a
decomposed
representation of
structured data (RDF-style)
and allows collection of
data resources without loss
and or distortion and
thereby achieve
representational integration
Light Weight Semantic
Enhancement (SE)
supports semantic
integration and
provides a decent
utilization capability
without adding storage
and processing weight
to the already storage-
and processing-heavy
Dataspace
57
58. DRIF Dataspace
• Integration without heavy pre-processing (ad-hoc rapid
integration):
– Of any data artifact regardless of the model (or absence of it) and
modality
– Without loss and or distortion of data and data-semantics
• Continuous evolution and enrichment
• Pay-as-you-go solution
– While data and data-semantics are expected to be enriched and
refined, they can be efficiently utilized immediately after entering
the DataSpace through querying, navigation, and drilling
D. Salmen et al. Integration of Intelligence Data through Semantic Enhancement.
http://stids.c4i.gmu.edu/STIDS2011/papers/STIDS2011_CR_T1_SalmenEtAl.pdf 58
59. Organization of the DRIF Dataspace
Registration
Ingestion
Extraction [Transformation] / Enrichment
60. Goals of Semantic Enhancement
• Simple yet efficient harmonization strategy
– Takes place not by changing the data semantics to which it is applied , but
rather by adding an extra semantic layer to it
– Long-lasting solution that can be applied consistently and in cumulative
fashion to new models entering the Dataspace
• Strategy compliant with and complementing the DRIF
– Source data models are not changed
• Be used efficiently, and in a unified fashion, in search,
reasoning, and analytics
– Provides views of the Dataspace of different level of detail
• Mapping to a particular Über-model or choosing a single
comprehensive model for harmonization do not provide
the benefits described
60
61. Ontology vs. Data Model
• Each ontology provides a comprehensive synoptic view of a
domain as opposed to the flat and partial representation
provided by a data model
Computer
Skill
Single Ontology Multiple Data models
PersonPerson
Person
Name
First
Name
Last
Name
PersonSkill
PersonName NetworkSkill ProgrammingSkill
Is-a Bearer-of
Skill
Last Name First Name Skill
Person Name Computer Skill
Programming
Skill
Network
Skill
Skill
61
62. Illustration
• DRIF Dataspace with lots of data models
• Incremental annotations of these data models
through SE ontologies
• Preserving the native content of data
resources
• Presenting the native content via the SE
annotations
• Benefits of the approach
62
63. Sources
• Source database Db1, with tables Person and Skill, containing
person data and data pertaining to skills of different
kinds, respectively.
• Source database Db2, with the table Person, containing data
about IT personnel and their skills:
• Source database Db3, with the table ProgrSkill, containing data
about programmers’ skills:
PersonID SkillID
111 222
SkillID Name Description
222 Java Programming
ID SkillDescr
333 SQL
EmplID SkillName
444 Java
63
64. Representation in the Dataspace
Value and
Associated Label
Relation Value and
Associated Label
111, Db1.PersonID hasSkillID 222, Db1.SkillID
222, Db1.SkillID hasName Java, Db1.Name
222, Db1.SkillID hasDescription Programming,
Db1.Description
333, Db2.ID hasSkillDescr SQL, Db2.SkillDescr
444, Db3.EmplID hasSkillName Java, Db3.SkillName
Label Relation SE Label
Db1.Name Is-a SE.Skill
Db2.SkillDescr Is-a SE.ComputerSkill
Db3.SkillName Is-a SE.ProgrammingSkill
Db1.PersonID Is-a SE.PersonID
Db2.ID Is-a SE.PersonID
Db3.EmplID Is-a SE.PersonID
SE.ComputerSkill Is-a SE.Skill
SE.ProgrammingSkill Is-a SE.ComputerSkill
Representation of
data-models, SE and
SE annotations as
Concepts and
ConceptAssociations
Blue – SE annotations
Red – SE hierarchies
Native
representation
of structured
data
64
65. Indexed Contents Based on the SE
Index entries based on the SE and native (blue) vocabularies
Index Entry Associated Field-Value
111,
PersonID
Type: Person
Skill: Java
Db1.Description:Programming
333,
PersonID
Type: Person
ComputerSkill: SQL
444,
PersonID
Type: Person
ProgrammingSkill: Java
65
66. Benefits of DRIF + SE
• Leverages syntactic integration provided by DRIF, semantic
integration provided by the SE vocabulary and annotations of
native sources, and rich semantics provided by ontologies in
general
– Entering Skill = Java (which will be re-written at run time as: Skill = Java
OR ComputerSkill = Java OR ProgrammingSkill = Java OR NetworkSkill =
Java) will return: persons 111 and 444
– Entering ComputerSkill = Java OR ComputerSkill = SQL will return:
persons 333 and 444
– entering ProgrammingSkill = Java will return: person 444
– entering Description = Programming will return: person 111
• Allows to query/search and manipulate native representations
• Light-weight non-intrusive approach that can be improved and
refined without impacting the Dataspace
66
67. Index Contents without the SE
Index Entry Associated Field-Value
111, PersonID Type: Person
Name: Java
Description: Programming
333, ID Type: Person
SkillDescr: SQL
444, EmplID Type: Person
SkillName: Java
Index entries based on native vocabularies
67
68. Problems
• Even for our toy example we can see how much
manual effort the analyst needs to apply in
performing search without SE – and even then
the information he will gain will be meager in
comparison with what is made available
through the Index with SE.
–For example, if an analyst is familiar with the labels
used in Db1 and is thus in a position to enter Name
= Java, his query will still return only: person 111.
Directly salient Db4 information will thus be missed.
68
69. Additional Notes on the SE process
• Original data and data-semantics are included in the Dataspace
without loss and or distortion; thus there is no need to cover all
semantics of the Dataspace – what is unlikely to be used in search or
is not important for integration will still be available when needed
• A complex ontology is not needed – a common and shared
vocabulary is sufficient for virtual semantic integration and
search/analytics
• The approach is very flexible, and investments can be made in
specific areas according to need (pay-as-you-go)
• The approach is tunable – if the chosen annotations of a particular
subset of a source data-model are too general for data analyses, the
respective LLOs can be further developed and source models re-
annotated
69
70. Benefits of the Approach
• Does not interfere with the source content
• Enhancement enables this content to evolve in a cumulative fashion as
it accommodates new kinds of data
• Does not depend on the data resources and can be developed
independently from them in an incremental and distributed fashion
• Provides a more consistent, homogeneous, and well-articulated
presentation of the content which originates in multiple internally
inconsistent and heterogeneous systems
• Makes management and exploitation of the content more cost-effective
• The use of the selected ontologies brings integration with other
government initiatives and brings the system closer to the federally
mandated net-centric data strategy
• Creates an integrated content that is effectively searchable and that
provides content to which more powerful analytics can be applied
70
71. Data Models and SE
PersonID Name Description
111 Java Programming
222 SQL Database
SQL Java C++
ProgrammingSkill
ComputerSkill
Skill Education
Technical
Education
71
72. The Meaning of ‘Enhancement’
• Amazing semantic enhancement/enrichment of data without
any change to data – on a string of the annotation, we put on
the top of a database field the whole knowledge system. For
example, not only can analysts analyze the data about
computer skills “vertically” along the Skill hierarchy, they can
analyze it “horizontally” via relations between Skill and
Education, and further… While data in the database does not
change, its analysis can be richer and richer as our
understanding of the reality changes
• For this richness to be leveraged by different
communities, persons, and applications it needs to be
constructed in accordance with the principles of the SE
72
73. Towards Globalization and Sharing
• Using the SE approach
to create a Shared
Semantic Resource for
the Intelligence
Community to enable
interoperability across
systems
• Applying it directly to or
projecting its contents
on a particular
integration solution
73
74. Building the Shared Semantic Resource
• Methodology of distributed incremental
development
• Training
• Governance
• Common Architecture of Ontologies to support
consistency, non-redundancy, modularity
– Upper Level Ontology (BFO)
– Mid-Level Ontologies
– Low Level Ontologies
74
75. Governance
• Common governance
– coordinating editors, one from each ontology, responsible
for managing changes and ensuring use of common best
practices
– small high-level board to manage interoperability
• How much can we embed governance into software?
• How much can we embed governance into training?
– analogy with military doctrine
75
76. Governance principles
1. All ontologies are expressed in a common shared syntax (initially
OWL 2.0; perhaps later supplemented by CLIF) (Syntax for
annotations needs to be fixed later; potentially RDF.)
2. Each ontology possesses a unique identifier space (namespace) and
each term has a unique ID ending with an alphanumeric string of
the form GO:0000123456
3. Each ontology has a unique responsible authority (a human being)
4. If ontologies import segments from other ontologies then imported
terms should preserve the original term ID (URI).
5. Versioning: The ontology uses procedures for identifying distinct
successive versions (via URIs).
6. Each ontology must be created through a process of downward
population from existing higher-level ontologies
76
77. Governance principles
7. Each ontology extends from BFO 2.0
8. Each lower-level ontology is orthogonal to the other ontologies at
the same level within the ontology hierarchy
9. The ontologies include textual (human readable) and logical
definitions for all terms.
10. The ontology uses relations which are unambiguously defined
following the pattern of definitions laid down in the Relation
Ontology that is incorporated into BFO 2.0
11. Each ontology is developed collaboratively, so that in areas of
overlap between neighboring ontologies authors will settle on a
division of terms.
77
78. Orthogonality
• For each domain, ensure convergence upon a single
ontology recommended for use by those who wish to
become involved with the Foundry initiative
• Thereby: avoid the need for mappings – which are in too
expensive, too fragile, too difficult to keep up-to-date as
mapped ontologies change
• Orthogonality means:
– everyone knows where to look to find out how to
annotate each kind of data
– everyone knows where to look to find content for
application ontologies
78
79. Orthogonality = non-redundancy
for reference ontology modules on
any given level
• application ontologies can overlap, but then
only in those areas where common coverage
is supplied by a reference ontology
79
80. Definitions (one example of OBO
Foundry traffic rules)
all definitions should be of the genus-species
form
A =def. a B which Cs
where B is the parent term of A in the ontology
hierarchy
80
81. Because the ontologies in the
Foundry
are built as orthogonal modules which form an
incrementally evolving network
• scientists are motivated to commit to
developing ontologies because they will need in
their own work ontologies that fit into this
network
• users are motivated by the assurance that the
ontologies they turn to are maintained by
experts
81
82. More benefits of orthogonality
• helps those new to ontology to find what they
need
• to find models of good practice
• ensures mutual consistency of ontologies
(trivially)
• and thereby ensures additivity of annotations
82
83. More benefits of orthogonality
• No need to reinvent the wheel for each new
domain
• Can profit from storehouse of lessons learned
• Can more easily reuse what is made by others
• Can more easily reuse training
• Can more easily inspect and criticize results of
others’ work
• Leads to innovations (e.g. Mireot, Ontofox) in
strategies for combining ontologies 83
90. Blinding Flash of the Obvious
Continuant Occurrent
process, event
Independent
Continuant
thing
Dependent
Continuant
quality
.... ..... .......
quality depends
on bearer
91. Blinding Flash of the Obvious
Continuant Occurrent
process, event
Independent
Continuant
thing
Dependent
Continuant
quality, …
.... ..... .......
event depends
on participant
92. Occurrents depend on participants
instances
15 May bombing
5 April insurgency attack
occurrent types
bombing
attack
participant types
explosive device
terrorist group
93. Blinding Flash of the Obvious
Continuant
Occurrent
process, eventIndependent
Continuant
thing
Dependent
Continuant
quality
.... ..... .......
process is change
in quality
94. What is a datum?
Continuant Occurrent
process
Independent
Continuant
laptop, book
Dependent
Continuant
quality
.... ..... .......
datum: a pattern in some
medium with a certain
kind of provenance
95. General lessons for ontology success
Strategy of low hanging fruit
Lessons learned and disseminated as
common guidelines –developers are doing
it the same way
Ontologies built by domain experts
Ontologies based on real thinking (not text
mining)
96. Low Hanging Fruit
Start with simple assertions which you
know to be universally true
hand part_of body
cell death is_a death
pneumococcal bacterium is_a bacterium
(Computers need to be led by the hand)
Use only the lowest node in the tree of
which you are sure that it holds
97. Examples
• How to cope with ontology change (role
of versioning, authority structure to ensure
evolution in tandem within the networked
ontology structure) – how to ensure that
resources invested in ontology do not lose
their value when the ontology changes
• Versioning demands term-IDs which
change whenever a term or definition
changes
98. Experience with BFO in
building ontologies provides
a community of skilled ontology developers and
users
associated logical tools
documentation for different types of users
a methodology for building conformant
ontologies by starting with BFO and populating
downwards
101. Conclusion
Ontologists have established best practices
– for building ontologies
– for linking ontologies
– for evaluating ontologies
– for applying ontologies
which have been thoroughly tested in use
and which conform precisely to the hub-and-
spokes strategy of the UCore and C2 efforts
105. Ontology Defined
Ontology is the science of representing, defining, and
relating the kinds and structures of
objects, properties, events, processes and relations in every
area of reality.
An ontology is an exhaustive classification of entities in
some sphere of being, which results in the formulation of
robust and shareable descriptions of a given domain.
(e.g. Physics, Biology, Medicine, Intelligence, etc.).
105
107. 107
Orders of Reality
1st Order. Reality as it is. In the action in the
upper image to the right, reality is what is, not
what we think is happening, as we peer
through the fog of war.
2d Order. Participant Perceptions. What we
believe is happening as we peer through the
fog of war. Examples: as a participant in the
action shown in the upper image or as a
member of an operations center in the lower
image.
3rd Order. Reality as we record it. In the lower
image, the computer displays are 3rd order
reality.
The gaps between the orders of reality introduce risk. These gaps are not the only
form of risk but reducing these gaps contributes to reducing risk.
107
110. 110
Warfighters’ Information Sharing Environment
Fire
Support
LogisticsAir Operations
Intelligence
Civil-Military
Operations
Targeting
Maneuver
&
Blue
Force
Tracking
111. Merriam-Webster’s Collegiate
Dictionary
Joint Publication 1-02 DoD Dictionary
of Military and Related Terms
Joint Publication 3-0 Joint Operations
Joint Publication 3-13 Joint Command
and Control
Joint Publication 3-24
Counterinsurgency
Joint Publication 3-57 Civil-Military
Operations
JP 3-10, Joint Security Operations in
Theater
Joint Publication 3-16 Multinational
Operations
Joint Publication 5-0 Joint Operations
Planning
Authoritative References
http://www.dtic.mil/doctrine/
Warfighter Lexicon
Controlled Vocabulary
Stable
Horizontally Integrated
Common Operational Picture
111
113. 113
JP 3-0
Operations
JP 2-0
Intelligence
JP 6-0
Comm
Support
JP 4-0
Logistics
JP 3-16
Multinational
Operations
JP 3-33
JTF
Headquarters
JP 1-02
DoD Dictionary
Civil-Military Operations
Area of Operations XXX X
Area of Responsibility X
X
XX
X
C2 Systems X
X
X X
Doctrinal Publications
Consistent Terminology (Data Elements, Names and Definitions)
Area of Interest X X
X
Key: word for word
115. Previous Information Revolution
• 1800 Cartographic Revolution
• Explosion of production, dissemination and use
of cartography
• Revolutionary and Napoleonic wars
• Several individual armies in the extended terrain
• New spatial order of warfare
• Urgent need for new methods of spatial
management…*
*SOURCE: PAPER EMPIRES: MILITARY CARTOGRAPHY AND THE MANAGEMENT OF SPACE115
119. Ontology & Military Symbology
• Elements of Military Ontology
• Represent Entities and Events found in military
domains
• Used to develop the Common Operational
Picture
• Used to develop Situational Awareness
• Used to develop Situational Understanding
• Used for Operational Design
• Used to Task Organize Forces
• Used to Design/Create Information Networks
• Enhance the Military Decision Making Process
119
122. Task Organizing
Ontological methods are used in the process of
Task-Organizing
A Task-Organization is the Output (Product) of
Task Organizing
A Task-Organization is a Plan or part of a Plan
A Plan is an Information Content Entity
Task-Organizing — The act of designing an operating
force, support staff, or logistic package of specific size
and composition to meet a unique task or mission.
Characteristics to examine when task-organizing the
force include, but are not limited to: training,
experience, equipage, sustainability, operating
environment, enemy threat, and mobility. (JP 3-05)
122
123. Operational Design
Source: FM 3-0 Operations
Military Ontologies help planners and operators “see” and
understand the relations between Entities and Events in the
area of operations.
Military Ontologies are prerequisites of military innovations
such as Airborne Operations, Combined Fires and Joint
Operations.
Military Ontologies are prerequisites for the creation of effective
information systems.
Operational Design — The conception and construction of the
framework that underpins a campaign or major operation plan
and its subsequent execution. See also campaign; major
operation. (JP 3-0)
123
124. Asserted (Reference) Ontologies
• Generic Content
• Aggressive Reuse
• Multiple Different Types of Context
• Better Definitions
• Prerequisite for Inferencing
Target List
Target
Nomination
List
Candidate
Target List
High-Payoff
Target List
Protected
Target List
Intelligence
Product
Geospatial
Intelligence
Product
Target
Intelligence
Product
Signals
Intelligence
Product
Human
Intelligence
Product 124
141. Infantry Company is part_of a Battalion (Continuant to Continuant)
Civil Affairs Team participates_in a Civil Reconnaissance (Continuant to Occurrent)
Military Engagement is part_of a Battle Event (Occurrent to Occurrent)
House is a Building (Universal to Universal)
3rd Platoon, Alpha Company participates_in Combat Operations (Instance to Universal)
3rd Platoon, Alpha Company is located_at Forward Operating Base Warhorse (Instance to Instance)
Relations
• How we make sense of the world
• Message In Plain English (MIPE)
• How Data becomes Information
141
143. 2 Data Model Labels
• Region.water.distanceBetweenLatrinesAndWaterSource
• Region.water.fecalOrOralTransmittedDiseases
– How are these labels used?
– No way to standardize or horizontally integrate
– Trying to pack too much into each label
– Contain elements from several asserted ontologies
– Need to be Decomposed into elements
– Relating elements from different asserted ontologies
– Common events and objects in an Area of Operations
143
147. located
near
Unpacking: Region.water.distanceBetweenLatrinesAndWaterSource
Latrine
Well
‘VT 334 569’
Distance
Measurement
Result
Village
Name
‘Khanabad
Village’
Village
is_a
instance_of
Geopolitical
Entity
Spatial
Region
Geographic
Coordinates
Set
designates
instance_of
located
in
instance_of
has
location designates
has
location
instance_of
instance_of
’16 meters’
instance_of
measurement_of
148. Sample Ontology Terms/Labels
Contamination Event Consumable Role Disease Event Epidemic Event
Geographic
Coordinates Set
Geospatial Region Measurement Result Microorganism
Pathogen Role Tribal Region Village Water Source
• Application Neutral Labels (Preferred Labels)
• Much better for Horizontal Integration
– database query
– Inferencing
– Standardization
– Reuse (e.g. Disease Ontology already exists)
– Training
148
151. Agenda
• Standardized Processes
• Scoping the Domain
• Creating Initial Lexicon
• Initial Ontology
• Feedback and Iteration
• Publish and Share
151
152. 152
Method:
1.A particular procedure for accomplishing or
approaching something, esp. a systematic or
established one.
2. Orderliness of thought or behavior; systematic
planning or action: “combination of knowledge
and method”.
Merriam-Webster‟s Collegiate Dictionary
Standardized Process
155. The Repeatable Process for
the...
Military Decision Making
Process (MDMP)...
as depicted in Doctrine
This is a highly refined and
documented process
All Leaders, Planners, and
Decision-Makers are well
versed in the MDMP
155
164. What is the baseline definition/description for this
domain?
What are the primary activities involved in this domain?
What are the subordinate activities in this domain?
Who participates in these activities?
What environment do these activities take place in?
What are the intended outcomes of these activities?
What are the intended products of these activities?
What information is consumed in these activities?
Who consumes this information?
What information is produced by these activities?
Where is this information found?
Where is this information stored?
What organizations are involved in this domain?
How are these organizations related?
What do these outputs contribute to?
What is the relation between agents and organizations in this domain?
What are the ultimate goals for the domain?
What are the subordinate goals for the domain?
What larger enterprise/objective does this domain contribute to?
What happens if these activities fail to produce their intended outcomes?
Metrics:
20 Questions for C2 Related Domains
164
166. Joint Operation Planning Process: Planning activities associated with joint military operations by
combatant commanders and their subordinate joint force commanders in response to
contingencies and crises. Joint operation planning includes planning for the mobilization,
deployment, employment, sustainment, redeployment, and demobilization of joint forces. (Joint
Publication 3-0 Joint Operations)
Planning Activity Joint Military Operation Combatant Commander
Subordinate Joint Force
Commander
Contingency Event Crisis Event
Mobilization Event Deployment Event Employment Event
Sustainment Event Redeployment Event Demobilization
Joint Force Planning Process Response
Building the Domain Lexicon
166
167. Example:
Dog: An Animal [parent class]…
which is a member of the genus Canis, probably descended from the common wolf, that has
been domesticated by man since prehistoric times; occurs in many breeds [differentia from
all other animals]
(Merriam Webster‟s Collegiate Dictionary)
Definitions
• Always make Two-Part Definitions which
include:
Reference to Parent Class (Genus) &
Differentia (Species)
167
168. Definitions
Attack Geography:
A description of the geography surrounding the IED
incident, such as road
segment, buildings, foliage, etc. Understanding the
geography indicates enemy use of landscape to
channel tactical response, slow friendly
movement, and prevent pursuit of enemy forces.
IED Attack Geography:
A Geospatial Region where some IED Incident takes
place.
IED Attack Geography Description:
A Description of the physical features of some
Geospatial Region where an IED Incident takes
place.
Original “Definition” Improved Definition(s)
168
169. Method of Emplacement:
A description of where the device was delivered, used, or
employed. (original definition)
Original “Definition” Improved Definition(s)
Method of IED Emplacement:
A systematic procedure used in the positioning of an
Improvised Explosive Device.
Method of IED Emplacement Description:
A description of the systematic procedure used in the
positioning of an Improvised Explosive Device.
Example 2: Method of Emplacement
169
170. Example 3: Method of Employment
Method of Employment:
A description of where the device was delivered, used, or
employed. (original definition)
Original “Definition” Improved Definition(s)
Method of IED Employment:
A systematic procedure used in the delivery of an
Improvised Explosive Device.
Method of IED Employment Description:
A description of the systematic procedure used in the
delivery of an Improvised Explosive Device.
170
171. Doctrinal Definitions
intelligence estimate — The appraisal, expressed in
writing or orally, of available intelligence relating to a
specific situation or condition with a view to determining
the courses of action open to the enemy or adversary
and the order of probability of their adoption. (JP 2-0)
171
174. Specifically
Dependent
Continuant
Agent
Generically
Dependent
Continuant
Independent Continuant
Object Aggregate/Object/Site
Process
Natural Process
- Biological Process
- Weather Process
- Geological Process
Capability
- Sustained Rate of Fire
- Lethal Capability
- Skill
Role
- Geospatial Role
-- Area of Interest
-- Area of Operations
- Personal Role
--Key Leader Role
--Insurgent Role
- Artifact Role
--IED Component
- Target Role
- Equipment Role
- Cargo Role
Act
- Political Act
- Violent Act
- Planning Act
Quality
Physical Characteristic
- Eye Color
- Height
- Weight
Physical Artifact
- Vehicle
- -Tractor
- Weapon
- - Rifle
- - Improvised Explosive Device
System
-Weapon System
- C2 System
--Targeting System
- Intelligence System
Site
- Geospatial Region
Independent Continuant
Object Aggregate/Object/Site
Organization
- Military
Organization
- Political
Organization
Organism
- Human Being
- Non-Human
Animal
- Bacteria
Information
Artifact
- Directive
-- Plan
-- Prescription
-- Guidance
- Description
-- Narrative
-- Comment
-- Remark
- Designation
--Address
-- Grid Coordinate
-- Name
--Code
- Measurement
-- Altitude
-- Height
-- Weight
-- Distance
174
175. Revisions
Process
with SME’s
SME Feedback
Military Ontology is the task of establishing and representing the salient types of entities
and relations in a given domain (Battlespace)
175
Ontology Review
177. Intelligence Ontology Suite
No. Ontology Prefix Ontology Full Name List of Terms
1 AO Agent Ontology
2 ARTO Artifact Ontology
3 BFO Basic Formal Ontology
4 EVO Event Ontology
5 GEO Geospatial Feature Ontology
6 IIAO Intelligence Information Artifact Ontology
7 LOCO Location Reference Ontology
8 TARGO Target Ontology
Home Introduction PMESII-PT ASCOPE References Links
Welcome to the I2WD Ontology Suite!
I2WD Ontology Suite: A web server aimed to facilitate ontology visualization, query, and development for the Intelligence
Community. I2WD Ontology Suite provides a user-friendly web interface for displaying the details and hierarchy of a specific
ontology term.
177
180. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
A simple battlefield ontology (from W. Ceusters)
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-into
Ontology
181. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
Ontology used for annotating a situation
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-into
Ontology
Situation
182. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
Referent Tracking (RT) used for representing a situation
#1 #2 #3 #4 #10
Ontology
Situational
model
Situation
#5 #6 #8#7
usesuses
uses
uses
uses
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
183. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
use the same weapon
use the same
type of
weapon
Referent Tracking preserves identity
#2 #3 #4 #10
Ontology
Situational
model
Situation
#6 #8#7
uses
uses
uses
uses
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
184. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
faithful
Specific relations versus generic relations
#1 #2 #3 #4 #10
Ontology
Situational
model
Situation
#5 #6 #8#7
usesuses
uses
uses
uses
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
185. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
Specific relations versus generic relations
Ontology
Situational
model
Situation
NOT faithful
uses
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
186. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
Representation of times when relations hold
#3
Ontology
Situational
model
Situation
soldier
private sergeant sergeant-major
at t1
at t2
at t3
187. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
#1 #2
Ontology
Situational
model
Situation
#5 #6
uses
at t1
uses
at t1
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
188. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
#1 #2
Ontology
Situational
model
Situation
#5
uses
at t2
after the death of #1 at t2
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
189. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
RT deals with conflicting representations by
keeping track of sources
#1 #2
Situational
model
Situation
#5 #6
uses
at t1
uses
at t1
uses
at t2
at t3
Ontology corpse
asserts at t2
190. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
#1 #2
Situational
model
Situation
#5 #6
uses
at t1
uses
at t1
uses
at t2
at t3
Ontology corpse
asserts at t4
RT deals with conflicting representations by
keeping track of sources
191. New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
Advantages of Referent Tracking
• Preserves identity
• Allows to assert relationships amongst entities that
are not generically true
• Appropriate representation of the time when
relationships hold
• Deals with conflicting representations by keeping
track of sources
• Mimics the structure of reality
192. Towards a Video Ontology
Video as information artifact
Process by which video is created
Video content for tagging (man shooting rifle)
192
193. How a video ontology can help the
process of intelligence analysis
193
i) The video file
ii) The content of (what is represented by) the video file
iii) The process of inserting text into the video that could
later be queried
iv) The video's role in the intelligence/decision making
process
VideoFile has_role IntelligenceProduct
194. Example
194
An FMV PO focuses the CSP on some suspicious activity. The PO
zooms in, and identifies three tracked vehicles moving through a NAI.
He immediately tags a 15-second clip with the text
"3 x tracked vehicles pending ID"
That clip is also automatically tagged with MGRS and DTG. After
reporting this EEI to the supported BCT, an imagery analyst immediately
conducts a search for "tracked vehicles" within the time window reported.
The motion imagery clip in question pops up, and the analyst
adds/modifies
the text to
"3 x likely Ukrainian T-72 moving from north to south through NAI 9 at low
speed; tank commanders had hatch open and did not appear to anticipate
contact."