This document discusses techniques for managing and understanding large biomedical ontologies authored in OWL. It notes that OWL ontologies can be difficult to author and comprehend due to their complexity. However, several approaches are making ontologies more manageable, such as generating natural language descriptions, abstracting ontologies to find patterns of axioms, and hiding the OWL syntax from end users. Tools like Protege plugins can help apply these techniques to improve ontology development and application.
This tutorial tries to answer the following questions:
What is the best practice for ontology reuse?
Is it fine to use external ontology entities to model my local entities?
Should I import the ontologies that I reuse?
What if I only need a part of an ontology?
What if an external ontology that I reused, changes?
This tutorial tries to answer the following questions:
What is the best practice for ontology reuse?
Is it fine to use external ontology entities to model my local entities?
Should I import the ontologies that I reuse?
What if I only need a part of an ontology?
What if an external ontology that I reused, changes?
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
Postulate Approach to Library Classification
Normative Principles
Three Planes of Work
Modes of Formation of Subjects
Systems Approach to the Study of Subjects
Depth Classification
Classification in Electronic Environment
Classificatory basis for metadata
Knowledge Organization
Franz Et Al. Using ASP to Simulate the Interplay of Taxonomic and Nomenclatur...taxonbytes
Answer Set Programming (ASP) is a declarative, stable model approach to logic programming with an under-realized potential for representing and reasoning over biological information. ASP is particularly suited to address reasoning challenges with complex starting conditions and rule sets. One such challenge is the interplay of taxonomic and nomenclatural change in biological taxonomy that often results when a taxonomy is revised based on a previously published perspective. Depending on the nature of the taxonomic changes to be undertaken, one or more Code-mandated principles will apply to regulate specific and concomitant name changes. In the case of the International Code of Zoological Nomenclature, two principles of significance include the Principles of Priority and Typification. Although the relationship between the number of taxonomic and nomenclatural adjustments under a given transition scenario is not linear, the application of the name-changing rules is usually unambiguous and therefore amenable to logic representation. Here we explore the modeling of the taxonomy/nomenclature interplay in ASP with a simple, abstract nine-taxon use case that contains four terminal species of which two are type-bearers for their respective genera. Four distinct one-taxon transfer scenarios are simulated through a transition system approach, requiring 1-7 concomitant nomenclatural changes depending (1) on the priority relationships among the terminal taxa being repositioned and (2) the type-bearing name dependencies of their higher-level parents. ASP can simulate these rules faithfully and thus reason over situations that range from a one-to-one match of taxonomic and nomenclatural changes to situations where they two kinds of change become increasingly disconnected (e.g., transfer of non-type genera among tribes without name change, or "transfer" [in reverse direction] of a single priority-carrying name/taxon into a larger yet junior entity with numerous required name changes). Our results, though very preliminary, illustrate how ASP logic approach may be utilized to perform optimizations at the taxonomy/nomenclature intersection, and generally represent a novel step towards translating Code-mandated naming rules into logic, with potential benefits for virtual taxonomic domains.
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
Postulate Approach to Library Classification
Normative Principles
Three Planes of Work
Modes of Formation of Subjects
Systems Approach to the Study of Subjects
Depth Classification
Classification in Electronic Environment
Classificatory basis for metadata
Knowledge Organization
Franz Et Al. Using ASP to Simulate the Interplay of Taxonomic and Nomenclatur...taxonbytes
Answer Set Programming (ASP) is a declarative, stable model approach to logic programming with an under-realized potential for representing and reasoning over biological information. ASP is particularly suited to address reasoning challenges with complex starting conditions and rule sets. One such challenge is the interplay of taxonomic and nomenclatural change in biological taxonomy that often results when a taxonomy is revised based on a previously published perspective. Depending on the nature of the taxonomic changes to be undertaken, one or more Code-mandated principles will apply to regulate specific and concomitant name changes. In the case of the International Code of Zoological Nomenclature, two principles of significance include the Principles of Priority and Typification. Although the relationship between the number of taxonomic and nomenclatural adjustments under a given transition scenario is not linear, the application of the name-changing rules is usually unambiguous and therefore amenable to logic representation. Here we explore the modeling of the taxonomy/nomenclature interplay in ASP with a simple, abstract nine-taxon use case that contains four terminal species of which two are type-bearers for their respective genera. Four distinct one-taxon transfer scenarios are simulated through a transition system approach, requiring 1-7 concomitant nomenclatural changes depending (1) on the priority relationships among the terminal taxa being repositioned and (2) the type-bearing name dependencies of their higher-level parents. ASP can simulate these rules faithfully and thus reason over situations that range from a one-to-one match of taxonomic and nomenclatural changes to situations where they two kinds of change become increasingly disconnected (e.g., transfer of non-type genera among tribes without name change, or "transfer" [in reverse direction] of a single priority-carrying name/taxon into a larger yet junior entity with numerous required name changes). Our results, though very preliminary, illustrate how ASP logic approach may be utilized to perform optimizations at the taxonomy/nomenclature intersection, and generally represent a novel step towards translating Code-mandated naming rules into logic, with potential benefits for virtual taxonomic domains.
Strong second quarter
- Life Science and Healthcare post strong organic growth
- Organic sales growth in all regions
- Profitability rises thanks to growth and Sigma-Aldrich synergies
- Integration of Sigma-Aldrich on track
- Merck raises sales and earnings forecast for 2016 thanks to good business performance
Pistoia Alliance European Conference, Kings College London, April 19, 2016
Panel introduction to Big (Biomedical) Data and the challenges facing research in biomedical R&D with examples from genomics data around the world. #Pistoia2016
Event link:
https://www.eventbrite.co.uk/e/pistoia-alliance-european-conference-2016-tickets-19618953819
Read more about me and my work at:
http://dnadigest.org
http://repositive.io
https://uk.linkedin.com/in/fionanielsen
Building a Network of Interoperable and Independently Produced Linked and Ope...Michel Dumontier
Over 15 years ago, Sir Tim Berners Lee proclaimed the founding of an exciting new future involving intelligent agents operating over smarter data in order to perform complex tasks at the behest of their human controllers. At the heart of this vision lies an uneasy alliance between tedious formal knowledge representations and powerful analytics over big, but often messy data. Bio2RDF, our decade old open source project to create Linked Data for the life sciences, has weaved emergent Semantic Web technologies such as ontologies and Linked Data to generate FAIR - Findable, Accessible, Interoperable, and Reusable - data in the form of billions of machine accessible statements for use in downstream biomedical discovery.
This revolution in data publication has been strengthened by action from global bioinformatics institutions such as the NCBI, NCBO, EBI, and DBCLS. Notably, NCBI's PubChem has successfully coupled large scale data integration with community-based standards to offer a remakable biochemical knowledge resource amenable to data hungry discovery tools. Yet, in the face of increasing pressure from researchers, funders, and publishers, will these approaches be sufficient for growing and maintaining a comprehensive knowledge graph that is inclusive of all biomedical research?
Ontology is an important aspect of artificial intelligence. This slide presentation presents and overview of how ontology is defined while developing artificial intelligence systems
The sequence in which the isolate facets of a homogeneous set of compound subjects arrange themselves in the mind of the majority of readers may be called the absolute syntax of ideas. This may not coincide with the linguistic syntax—that is, the syntax of words—in all languages. It is conjectured that the syntax of facets is the same as the absolute syntax of ideas.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
OBOPedia: An Encyclopaedia of Biology Using OBO OntologiesObopedia swat4ls-20...robertstevens65
A talk on OBOPedia (HTTP://www.obopedia.org.uk) given at Semantic Web Applicaitons and Tools for Life Sciences (SWAT4Ls) 2015 in cambridge, UK December 2015
Issues and activities in authoring ontologiesrobertstevens65
Departmental seminar at Department of Computer Science, university of Birmingham, 6 November, 2014
abstract: Ontologies are complex knowledge representation artefacts used across biomedical sciences, the media and other domains for defining terminologies and providing metadata. Their use is increasing rapidly, but so far, ontology authoring tools have not benefited from empirical research into the ontology authoring process. Understanding how people build ontologies is key to developing tools that can properly support common authoring activities. In this talk I will first present the outcomes of qualative interviews with ontology authors and the issues it reveals. Second, I will present the results of a study that identifies common activity patterns through analysis of the event logs, screen capture and eye-tracking data collected from the popular authoring tool, Protege. Results from this bottom-up investigation suggest that the class hierarchy is the central focus of activity, playing a role beyond simple class representation. We also find that checking how updates to the ontology is hard and performance is hindered by inadequate support in the user interface. From this investigation we propose design guidelines for bulk editing, efficient reasoning and increased situational awareness in ontology authoring.
The state of the nation for ontology developmentrobertstevens65
Invited talk at European Ontology Network (EUON) 2014
Ontologies are now quite big, both literally and metaphorically. They have become central resources in disciplines such as biology, medicine, healthcare and others. Such developments rely on people, tools and methods to deliver ontologies that do the desired job, on-time and on-budget. In this talk I wil ask the question of whether the tools and methods we have are capable of doing what is necessary to deliver robust and maintainable ontologies. To explore this question I will borro from the Capability Maturity Model used to assess the capabilities of institutions to deliver software projects. Instead of institutional assessment, I will bend the CCM to the discipline of ontology engineering. The levels of the CMM range from the ad hoc to one where metrics are used to monitor and adjust ontology development. In this talk I will use some audience participation to gather views on ontology engineering maturity level and then deliver my own view of that maturity.
Properties and Individuals in OWL: Reasoning About Family Historyrobertstevens65
Slides used in an advanced OWL tutorial in 2012. The tutorial is based on family history and uses OWL individuals as a first class citizen in the learning.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
1. Working with big biomedical ontologies
Robert Stevens
BioHealth Informatics Group
University of Manchester
2. The message
• Ontologies authored in the Web Ontology Language
(OWL) can be tricky to author and understand
• But we’re starting to find ways of managing the
authoring and comprehension of these artefacts
– Natural language versions of class descriptions;
– Abstracting over ontologies to find “patterns” of axioms;
– Scripting not hand-crafting axioms – exploiting patterns;
– Making it easy to do the right thing – semantic
spreadsheets
– Hiding the ontology and hiding the OWL
3. The need for descriptions of data
• 27 millimetres
• Tail of 27 millimetres
• Mouse tail of 27 millimetres
• Mouse of strain x that is 28 days old, tail that is 27
millimetres
• Data is only as good as its metadata
7. OWL Axioms can be hard to understand
• “Person and hasPet some not Cat”
• How many cats may a person have as pets?
• Cognitive complexity of OWL…
Making information explicitMaking information explicit
Universal vs existential
quantification
Universal vs existential
quantification
Defined and primitive classesDefined and primitive classes
DisjointnessDisjointness
Domain and range
constraints
Domain and range
constraints
Open world
reasoning
Open world
reasoning
Trivial
satisfiability
Trivial
satisfiability
Coon logical
constructs
Coon logical
constructs
Making
definitions
Making
definitions
8. Measuring syntactic sophistication in an OWL
ontology
• : number of patterns in an ontology
• : is the complexity of a given pattern
• : the size of the ontology in axioms
• : number of axioms captured in a given pattern
9. The Elements Ontology
(http://robertdavidstevens.wordpress.com/2011/05/05/an-
ontology-of-the-periodic-table-using-electronic-structure-
of-the-atom/)Before reasoning
After reasoning
Class: PlatinumAtom
SubClassOf:
Atom
and (hasValenceElectronShell some
(DShell
and (contains exactly 9 Electron)
and (hasOrder value 5)))
and (hasValenceElectronShell some
(FShell
and (contains exactly 14 Electron)
and (hasOrder value 4)))
and (hasValenceElectronShell some
(SShell
and (contains exactly 1 Electron)
and (hasOrder value 6)))
and (hasValenceElectronShell only
((DShell
and (contains exactly 9 Electron)
and (hasOrder value 5))
or (FShell
and (contains exactly 14 Electron)
and (hasOrder value 4))
or (SShell
and (contains exactly 1 Electron)
and (hasOrder value 6))))
and (hasAtomicNumber value 78)
10. How syntactically sophisticated are OWL
ontologies?
• 25% have an S-mesure of 1
• About 50% has an S-mesure of 2
• Then it sort of tails off
• (the previous slide’s ontology has an S mesaure of
580)
11. Why can an OWL ontology be complex?
• We have potentially syntactically (and semantically)
complex axioms, but most ontologies have axioms that
are more or less simple in terms of form
• We have cognitive coplexity of even simmple syntactic
constructs
• We have coplexity through size; lots and lots of simple
things become complex
• We also have to understand the domain description too
13. So, what do we do about it?
• Complex for whom?
• For the developer
– Add comprehension tools; they have to interact with
OWL, so make it easier
• For an ontology user:
– Don’t show them the ontology!
14. Hide the ontology
• OWL is horrid to look at
• When being used in a tool by the ultimate users, it just
shouldn’t be seen
17. Populous
• Generic tool for populating ontology templates
• Spreadsheet style interface
• Supports validation at the point of data entry
• Expressive Pattern language for OWL Ontology generation
http://www.e-lico.eu/populous
18. Generating natural language from OWL
• An axiom is a sentence in OWL
• Transform it into natural language to give a familiar
form for reading (and for input…)
• Body hasPart some Head
• All bodies have at least one part that is a head….
• Bodies have heads….
• A body has part a head
• A body has part x, has part y, has part z, has part ….
19. OntoVerbal-
M
Output: English
Diastolic hypertension is a kind of
hypertensive disorder. More
specialised kinds of diastolic
hypertension are
•sustained diastolic hypertension
and
•labile diastolic hypertension.
Another relevant aspect of diastolic
hypertension is that: secondary
diastolic hypertension is a kind of
secondary hypertension and diastolic
hypertension.
1. Class: Diastolic hypertension
SubClassOf: Hypertensive
disorder
2. Class: Sustained diastolic
hypertension
SubClassOf: Diastolic
hypertension
3. Class: Labile diastolic
hypertension
SubClassOf: Diastolic
hypertension
4. Class: Secondary diastolic
hypertension
SubClassOf: Secondary
hypertension
and
Diastolic hypertension
1. Class: Diastolic hypertension
SubClassOf: Hypertensive
disorder
2. Class: Sustained diastolic
hypertension
SubClassOf: Diastolic
hypertension
3. Class: Labile diastolic
hypertension
SubClassOf: Diastolic
hypertension
4. Class: Secondary diastolic
hypertension
SubClassOf: Secondary
hypertension
and
Diastolic hypertension
Input: SNOMED-
CT
心臟舒張高血壓屬於高血壓失調,它也
包含了持續性的心臟舒張高血壓和不安
定的心臟舒張高血壓。其他與心臟舒張
高血壓相關的資訊為:續發性的心臟舒
張高血壓屬於續發性高血壓和心臟舒張
高血壓的交集。
Output: Mandarin
20. OntoVerbal-
M
Output: English
Renal arterial hypertension is a kind of
renovascular hypertension that has a
finding site in a kidney
1. Class: Renal arterial
hypertension
SubClassOf: renovascular
hypertension and
(has a finding
site some
kidney)
1. Class: Renal arterial
hypertension
SubClassOf: renovascular
hypertension and
(has a finding
site some
kidney)
Input: SNOMED-
CT
腎臟 (renal) 動脈 (artery) 的
(apostrophe) 高血壓 (hypertension) 是
(is) 一種 (a kind of) 腎血管性
(renovascular) 高血壓 (hypertension)
中 (among) 在 (at) 腎臟 (kidney) 結構
(structure) 上 (upon) 有 (has) 病灶
(finding site) 。
Output: Mandarin
21. OntoVerbal
• Ontoverbal is a plugin for Protégé 4
• It generates natural language for classes in an ontology
• http://swatproject.org/demos.asp
• It groups axioms, aggregates repeating properties, organises
axiom types according to rhetorical structure theory, does some
msoothing of language
• People can round-trip back to OWL better than with human written
natural language
• Finds favour as a way of generating natural languaage definitions
• See also http://jamesmaloneebi.blogspot.co.uk/2012/06/ontology-
turing-test.html
22. Gathering axioms for a focused class (e.g.
Diastolic hypertension)
Gathering axioms for a focused class (e.g.
Diastolic hypertension)
Identifying notions of axiomsIdentifying notions of axioms
Grouping axioms according to their notionsGrouping axioms according to their notions
Deploying groups in order into a top-level
Rhetorical Structure Theory schema
Deploying groups in order into a top-level
Rhetorical Structure Theory schema
Output
English text
Output
English text
English
generator
English
generator
English
labels and
lexicon
input
English
labels and
lexicon
input
Output
Mandarin text
Output
Mandarin text
Mandarin
generator
Mandarin
generator
Mandarin
labels and
lexicon
input
Mandarin
labels and
lexicon
input
23. Patterns in OWL ontologies
• Axioms (should) repeat themselves as patterns in an
ontology
• Patterns that represent accepted solutions to a
representation problem are design patterns
• A pattern doesn’t have to be a design pattern
• They can just be regularities in the ontology
24. Regularity Inspector for Ontologies (RIO)
RIO
Clustering
Tool
RIO
Clustering
Tool
RIO Protégé
4.1 plugin
RIO Protégé
4.1 pluginregularities
Ontology
IRI
Regularity Views
RIO url:
http://riotool.sourceforge.net/
25. Examples of regularities in SNOMED-CT
• Regularities expressed as axioms with variables holding similar
entities:
– E.g. ?Chronic_finding = [Chronic pyonephrosis (disorder),
Chronic pneumothorax (disorder)]
1. Syntactic Regularity describing ‘Chronic findings’:
?Chronic_finding EquivalentTo ?Disorder and (RoleGroup some
(‘Clinical course (attribute)’ some ‘Chronic (qualifier value)’))
?Chronic_finding EquivalentTo ?Disorder and (RoleGroup some
(‘Clinical course (attribute)’ some ‘Chronic (qualifier value)’))
2. Syntactic Regularity describing ‘Acute findings’:
?Acute_finding EquivalentTo ?Disorder and (RoleGroup some
(‘Clinical course (attribute)’ some
'Sudden onset AND/OR short duration (qualifier value)'))
?Acute_finding EquivalentTo ?Disorder and (RoleGroup some
(‘Clinical course (attribute)’ some
'Sudden onset AND/OR short duration (qualifier value)'))
26. Some stats on snomed and RIO
• Entities not included in any cluster can be a starting point for tracing
design defects
– Eg. ’Chronic back pain (finding)’ was missing an existential restriction
Acute findings Chronic findings
Number of clusters
describing the entities
34 34
Target entities not found in
a cluster
12 11
Axioms instantiating naming
pattern
76 (5%) 210 (11%)
27. Summary of the message
• We’re getting more sophisticated in how we handle
OWL ontologies
28. Acknowledgements
• Simon Jupp
• Julie Klein
• Fennie Liang
• Donia Scott
• Eleni Mikroyannidi
• Azlinayati Manaf
• Sean Bechhofer
Editor's Notes
I want each bullet reveal in turn….
This slide shows the number of articles on the keyword ontology in pubmed over the years 2000-2012.
it’s exponential.
Put this list in blobs around the text boxMaking information explicit
Universal vs existential quantification
Open world reasoning
Domain and range constraints
Trivial satisfiability
defined and primitive classes
Coon logical constructs
Disjointness
Making definitions
Class TransitionMetalAtom before and after reasoning and description of platinum atom.
The slide shows two dominant patterns describing two sets of terms; chronic and acute clinical findings (mainly disorders). RIO used the oppl vocabulary for expressing variables.
The stats on the table are with respect to the detected regularities. The naming patterns were described in the previous slide; one for the chronic entities and one for the acute.
E.g. 5% of the entities in that had the word acute in their label were actually instantiating a pattern on their axioms.