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.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
We know that we are in an AI take-off, what is new is that we are in a math take-off. A math take-off is using math as a formal language, beyond the human-facing math-as-math use case, for AI to interface with the computational infrastructure. The message of generative AI and LLMs (large language models like GPT) is not that they speak natural language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for humanity-benefitting applications in biology, energy, and space science, however not without risk.
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.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
We know that we are in an AI take-off, what is new is that we are in a math take-off. A math take-off is using math as a formal language, beyond the human-facing math-as-math use case, for AI to interface with the computational infrastructure. The message of generative AI and LLMs (large language models like GPT) is not that they speak natural language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for humanity-benefitting applications in biology, energy, and space science, however not without risk.
Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery.
Bio:
Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing.
Introduction to semantic web. Includes its goal, features, why we need, semantic web related framework, RDF's, Advantages, Uniform resource locator, web ontology language, micro-formats.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
27 November 2014
This is the slideshow for a presentation I gave as part of my graduate coursework at the Institute for Innovation and Public Purpose at University College London (UCL IIPP). Drawing on the work of IIPP professors including Carlota Perez (techno-economic paradigms), Mariana Mazzucato (“The Entrepreneurial State”), and Tim O’Reilly, I evaluate the innovation trajectory of Deep Neural Networks as a method of machine learning. I trace the history of machine learning to its present-day and conclude that while Deep Neural Networks have not yet reached technological maturity, they are already starting to encounter barriers to exponential growth and innovation. These slides were designed to be read independently from the spoken portion. If you found this useful or interesting, please message me on LinkedIn! - Justin Beirold
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
An Introduction to Generative AI - May 18, 2023CoriFaklaris1
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Keynote at the AI in Medicine Conference (AIME 2005), giving an overview of the work in Ontology Mapping to people in Medical Informatics (which includes explaining the what and why of ontologies in general).
Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery.
Bio:
Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing.
Introduction to semantic web. Includes its goal, features, why we need, semantic web related framework, RDF's, Advantages, Uniform resource locator, web ontology language, micro-formats.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
27 November 2014
This is the slideshow for a presentation I gave as part of my graduate coursework at the Institute for Innovation and Public Purpose at University College London (UCL IIPP). Drawing on the work of IIPP professors including Carlota Perez (techno-economic paradigms), Mariana Mazzucato (“The Entrepreneurial State”), and Tim O’Reilly, I evaluate the innovation trajectory of Deep Neural Networks as a method of machine learning. I trace the history of machine learning to its present-day and conclude that while Deep Neural Networks have not yet reached technological maturity, they are already starting to encounter barriers to exponential growth and innovation. These slides were designed to be read independently from the spoken portion. If you found this useful or interesting, please message me on LinkedIn! - Justin Beirold
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
An Introduction to Generative AI - May 18, 2023CoriFaklaris1
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Keynote at the AI in Medicine Conference (AIME 2005), giving an overview of the work in Ontology Mapping to people in Medical Informatics (which includes explaining the what and why of ontologies in general).
Semantic Web research anno 2006:main streams, popular falacies, current statu...Frank van Harmelen
This keynote at the Cooperative Intelligent Agents Workshop was a good opportunity to give my view on the current state of Semantic Web research: what is it about, what is it not about, what has been achieved, what remains to be done. (Includes the now infamous slide "What's it like to be a machine")
Paul Henning Krogh A New Dawn For E Collaboration In ScienceVincenzo Barone
Plone has growing reputation within research for working as an important component in international scientific collaboration infrastructures. In this panel session researchers shall present and answer questions on both their experiences in using Plone in a scientific context and on their research of studying Plone in use by scientists. Attendees will leave with a better conception of what is needed for international scientific collaboration and what Plone can offer as an e-collaboration tool to support research infrastructures. The panel participants will bring in expertise on computer supported collaborative work (CSCW) to stimulate use and development of Plone applications for such use cases. Panel headlines: - Exchange experiences with Plone in research environments (use cases) - Requirements for Plone in research environments: what's available, which extensions or modifications do we need? - Coordinate actions around Plone products for scientific use - Promote the use of Plone in scientific environments - Confront conceptions of collaborative research processes with Plone implementations of such models
Survey of the current trends, and the future in Natural Language Generation Yu Sheng Su
Small talk on NLG/TG. In recent years, NLG has been widely used in many fields. Today, we'll start with a brief introduction to NLG methods including Pipeline, Autoregressive: Seq2seq, Transformer, GAN, and Non-Autoregressive models. Besides, we will roughly talk about NLG tasks/scenarios and some issues we encounter. Nowadays, Neural incorporates Symbolic is a powerful paradigm in future AI, as Bengio mentioned in AAAI 2020 conference. We will introduce how knowledge guides NLG as well.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
2. What is an Ontology?
• A formal, explicit specification of a shared
conceptualization.
• A shared vocabulary that can be used to model a
domain, i.e., the objects and/or concepts that
exist, their properties and relations
• Imposition of specific set of conceptualizations on
a domain of interest
– Tell me about analog electronics
– Tell me about digital electronics
• Definitions of terminology
– and constraints between terms
• Domain mini-theories
3. Ontology vs KB?
• Can think of an ontology as a kind of KB, but:
• Ontology serves different purpose:
– Only needs to describe vocabulary, axioms
– E.g. database schema is an ontology
• KB includes:
– Specific knowledge needed for problem-solving
4. Motivations
• Engineering motivation:
– Every knowledge-based system is based on an
ontology of its domain
– Explication of the ontology is a time-consuming
component of the development process
– Why not amortize the effort and share ontologies?
• E.g. “core ontologies” such as space, time, quantities
• Scientific motivation:
–Understand fundamental issues about human
conceptualizations
5. Pragmatic Motivations
• Responding to the unexpected
• Distributed Databases
• Distributed Applications Key Question: What does
• Communicating Agents he mean when he says
• Semantic Web <…>?
Person
Mediator
Common Ontology
Application 1
Design Common Ontology
schema1 schema2 schema3
db1 db3 Analysis Common Ontology
db2
Application 2
6. Aspects of an Ontology
• Content
• Form
• Purpose
• Development
7. Aspects of an Ontology
• Content
– types of objects, relationships
– e.g. the blocks world conceptualization includes:
• Object Classes: Blocks, Robot Hands
• Properties: shapes of blocks, color of blocks
• Relationships: On, Above, Below, Grasp
• Processes: stacking plan for a tower
• Form
• Purpose
• Development
8. Aspects of an Ontology
• Content
• Form
– Is the taxonomic relationship (instance-of, subclass)
primary?
– Are definitions of, or constraints on, terms provided?
– Is the definitional language as rich as a full logic?
– Is it process-centric or object-centric?
• Purpose
• Development
9. Aspects of an Ontology
• Content
• Form
• Purpose
– Knowledge sharing
• E.g. Between people, software systems, agents
– Knowledge reuse
• E.g. When models or systems change
– General (common sense) or domain specific
• Development
10. Aspects of an Ontology
• Content
• Form
• Purpose
• Development
– Is it acquired or engineered?
– If acquired, what about:
• Quality of knowledge
• Diversity of content
• Trust in knowledge
• Unpredictable use
11. Building an Ontology
• Planning
• Specification - consider scope and purpose
• Knowledge Acquisition
• Conceptualization - glossary of terms, top-
down, bottom-up, middle-out
• Integration - of existing relevant ontologies
• Implementation
• Evaluation - Clarity, Coherence, Extensibility,
Minimal Encoding Bias, Minimal Ontological
Commitment
12. Example Ontologies
see http://www.cs.utexas.edu/users/mfkb/related.html
* ARPI Pla n n in g and Sch ed uli n g o n to lo gi e s
* Aviat io n O n to lo g y
* BPMO - Th e Bus in e s s P roc es s Ma n a g e me nt On to lo g y
* CYC (a n d th e d e riva tive PDKB)
* DOLCE - a D e s cript ive On to lo g y fo r Lin g u is tic a n d C o g ni tive En gi n e e ring .
* Du b lin Cor e (bib lio g ra ph ic o rg an iz at io n )
* Th e Ent e rp ris e Onto lo g y (for b u s in e s s e n te rpr ise s)
* On to lo g ies f or e tho lo g y (a nim a l be h av ior) , e .g . Log g e rh ead T u rt le
* Fra m e Ne t (le x ica l re fe re n ce )
* Ge n e ra liz e d Up p er M o de l (for NLP)
* Mik roko sm os (fo r NLP)
* ON9 (th e CNR- ITBM On to lo g y Libra ry)
* OWL- S - Th e OWL (fo rm e rly DAML) Se rv ice s on t o log y.
* On to lin g u a O n to lo g y Libra ry
* Op e n Min d datab a s e a nd OM CSNe t S em ant ic N et wor k
* Ph ar mG KB - Pha rm a co g e n e tics a n d Pha rm a co g e no mic s Kno wled ge Ba s e
* PSL (p ro ce s s s pe cifi ca tion)
* Qo S (co m p u te rs a n d n et wo rks )
* SENSUS (fo r NLP)
* STEP (for pr o du ct data ex ch an ge )
* SUMO (th e Su gg e s te d Upp e r M e rg ed On to log y)
* th e T we n te O n to lo g y Co lle ctio n
* UMLS (b io me d icin e)
* Wilkin s ' on t o log y (17t h ce nt u ry !)
* Word Ne t (lex ica l re fe re n ce )
13. Example Tools for Ontologies
see http://www.cs.utexas.edu/users/mfkb/related.html,
http://www.xml.com/pub/a/2002/11/06/ontologies.html
* Ch im a e ra
* CODE4
* Ge n e ric Kn o wled g e -B a s e Ed itor
* Ika ru s
* JOE (Java O n to lo g y Edit or)
* KAON
* KACTUS
* OilEd
* On toE d it
* On tos a u ru s
* Prote ge
* Sn oba s e
* St an fo rd On to lo g y Edito r
* Sym Ont o s
* Word Map
15. Some Large Ontologies
CYC 105 concept types, 106 CYCL Partially Online:
common sense axioms 6000 Top
Concepts
SUMO 1000 terms, KIF Published Online
upper ontology 4200 assertions Also LOOM,
OWL,Protege
WordNet 152,059 word forms in Semantic Published Online
lexical memory 115,424 synsets Network
Sensus 70,000 terms Semantic Published Online
text understanding extension of WordNet Network
UMLS 135 Semantic Types, Semantic Published Online
biomedicine 54 semantic relations, Network
975,354 concepts
17. CYC
• Goal: Encode all of human common sense
knowledge
• Mechanization: human-entered axioms
• Periodic review, reorganization, compaction,
separation into distinct mini-theories, not
mutually consistent
• Driven by application domains
• Often seems ad-hoc
22. Perspectives
• Philosophy
• Library and Information Science
• Natural Language Processing
• Artificial Intelligence
• Semantic Web
Fredrik Arvidsson, Annika Flycht-Eriksson
23. Perspectives
• Philosophy
– Objectives: Classify and categorize the world
– E.g.: Aristotle …
• Library and Information Science
• Natural Language Processing
• Artificial Intelligence
• Semantic Web
24. Perspectives
• Philosophy
• Library and Information Science
– Objectives: organize bibliographic world, model
universal and domain knowledge
– Usage: provide access points to bibliographic entities
– E.g., MARC; LCC, UDC, SAB
• Natural Language Processing
• Artificial Intelligence
• Semantic Web
25. Perspectives
• Philosophy
• Library and Information Science
• Natural Language Processing
– Objectives: Model lexical and domain knowledge
– Usage: Machine Translation, Information Extraction,
Q/A
– E.g.: Wordnet, Sensus, Generalised Upper Model
• Artificial Intelligence
• Semantic Web
26. Perspectives
• Philosophy
• Library and Information Science
• Natural Language Processing
• Artificial Intelligence
– Objectives: Model common sense and domain
knowledge
– Usage: Knowledge representation and reasoning
– E.g.: OpenMind, CYC; UMLS, ...
• Semantic Web
27. Perspectives
• Philosophy
• Library and Information Science
• Natural Language Processing
• Artificial Intelligence
• Semantic Web
– Objectives: Provide semantics for web resources
– Usage: Describe resources and their contents
28. Application Example
Document comparison (Xerox; Everett, et al. CACM, Feb 02)
– Goal: Identify similar documents
– Have: 40,000 technician-authored tips for copier repair
29. EXAMPLE OF EUREKA TIPS
PROBLEM CAUSE SOLUTION
Left cover damage The left cover safety cable is breaking, Remove the plastic sleeve from around
TIP 27057
allowing the left cover to pivot too far, the cable. Cutting the plastic off of the
breaking the cover. cable makes the cable more flexible,
which prevents cable breakage. Cable
breakage is a major source of damage
to the left cover.
The current safety cable used in the 5100 The plastic jacket made the cable too When the old safety cable fails, replace it
TIP 27118
Document Handler fails prematurely, stiff. This causes stress to be concentrated with the new one, which has the plastic
causing the Left Document Handler Cover on the cable ends, where it eventually jacket shortened.
to break. snaps.
Figure by MIT OCW.
30. EXAMPLE OF EUREKA TIPS
PROBLEM CAUSE SOLUTION
Left cover damage The left cover safety cable is breaking, Remove the plastic sleeve from around
TIP 27057
allowing the left cover to pivot too far, the cable. Cutting the plastic off of the
breaking the cover. cable makes the cable more flexible,
which prevents cable breakage. Cable
breakage is a major source of damage
to the left cover.
The current safety cable used in the 5100 The plastic jacket made the cable too When the old safety cable fails, replace it
TIP 27118
Document Handler fails prematurely, stiff. This causes stress to be concentrated with the new one, which has the plastic
causing the Left Document Handler Cover on the cable ends, where it eventually jacket shortened.
to break. snaps.
Figure by MIT OCW.
31. Natural language requires a huge ontology…
xxxxxxxxx
(1) The left cover broke in half.
(2) The sheet of paper breaks the light beam.
(3) Before doing step 3, you might want to break for coffee.
32. Natural language requires a huge ontology…
xxxxxxxxx
(1) The left cover broke in half. BreakDamage
(2) The sheet of paper breaks the light beam. BreakInterrupt
(3) Before doing step 3, you might want to break for coffee.
BreakRecuperate
33. Natural language requires a more abstract ontology
one.
(1) The left cover broke in half. BreakDamage
(2) The sheet of paper breaks the light beam. BreakInterrupt
(3) Before doing step 3, you might want to break for coffee.
BreakRecuperate
Map to concepts instead.
34. A direct word-to-concept ontology for the concept Incapacitate.
Force Destroy
Crush
Shatter
Smash
Destroy Mangle
Pulverize
Fail Squash Force Damage
Break Shred
Wreck Eviscerate Gouge
Crunch Trample
Destroy
Topple Scratch
Raze Crash
Mash
Ruin Devour Pierce
Demolish Puncture Chip
Terminate Snap Tear
Crack
Cut
Incapacitate Disable Thermal Destroy Crumple
Dent
Remove Fry Abrade
Disable Incinerate Rip
Melt Slice
Bend
Nick
Damage
Corrupt
Thermal Damage
Damage
Disrupt Burn
Harm Scald
Scorch
Figure by MIT OCW.
35. A direct word-to-concept ontology for the concept Incapacitate.
Force Destroy
Crush
Shatter
Smash
Destroy Mangle
Pulverize
Fail Squash Force Damage
Break Shred
Wreck Eviscerate Gouge
Crunch Trample
Destroy
Topple Scratch
Raze Crash
Mash
Ruin Devour Pierce
Demolish Puncture Chip
Terminate Snap Tear
Crack
Cut
Incapacitate Disable Thermal Destroy Crumple
Dent
Remove Fry Abrade
Disable Incinerate Rip
Melt Slice
Bend
Nick
Damage
Corrupt The heat from the short circuit
cracks the drive gear. Thermal Damage
Damage
Disrupt Burn
Harm The short circuit burns Scald
Scorch
the drive gear.
Figure by MIT OCW.
36. A direct word-to-concept ontology for the concept Incapacitate.
Force Destroy
Crush
Shatter
Smash
Destroy Mangle
Pulverize
Fail Squash Force Damage
Break Shred
Wreck Eviscerate Gouge
Crunch Trample
Destroy
Topple Scratch
Raze Crash
Mash
Ruin Devour Pierce
Demolish Puncture Chip
Terminate Snap Tear
Crack
Cut
Incapacitate Disable Thermal Destroy Crumple
Dent
Remove Fry Abrade
Disable Incinerate Rip
Melt Slice
Bend
Nick
Damage
Corrupt The heat from the short circuit
cracks the drive gear. Thermal Damage
Damage
Disrupt Burn
Harm The short circuit burns Scald
Scorch
the drive gear.
Figure by MIT OCW.
37. Document Comparison Summary
• Different ways to represent the same knowledge
• Use determines representation
• Represent only knowledge that is needed
• c.f.: Common sense reasoning, don’t know use
or what knowledge is needed.
38. Success Using Ontologies?
• Domain-specific successes
– E.g. biomedicine
• More general use shows promise
– New languages
– New tools
– New applications
– Very active research community
39. Taxonomic Hierarchies
• Not always obvious what is class, instance,
role, etc.
• E.g., what is the relationship between:
– time duration (instances e.g. 1 hr) and
time interval (e.g. 1pm to 2pm today)?
– water and ocean?
– mammal and human and a particular human?
– human and species?
– book that is a bound volume, book that is abstract
concept?
40. Other Examples
• Enterprise Ontology (Edinburgh; Uschold, et al.)
• Document comparison (Xerox; Everett, et al.)
41. Other Examples
• Enterprise Ontology (Edinburgh; Uschold, et al.)
– Goal: improve planning via shared enterprise model
– Meta-ontology: entities, relationships, states of affairs
– Examples
42.
43. Other Examples
• Enterprise Ontology (Edinburgh; Uschold, et al. AIAI, 1998)
• Document comparison (Xerox; Everett, et al. CACM, Feb 02)
– Goal: Identify similar documents
– Have: 40,000 technician-authored tips for copier repair
– Current system: analyzes 15 pairs of similar tips
– Examples
44. A direct word-to-concept ontology for the concept Incapacitate.
Force Destroy
Crush
Shatter
Smash
Destroy Mangle
Pulverize
Fail Squash Force Damage
Break Shred
Wreck Eviscerate Gouge
Crunch Trample
Destroy
Topple Scratch
Raze Crash
Mash
Ruin Devour Pierce
Demolish Puncture Chip
Terminate Snap Tear
Crack
Cut
Incapacitate Disable Thermal Destroy Crumple
Dent
Remove Fry Abrade
Disable
? Incinerate Rip
Melt Slice
Bend
Nick
Damage
Corrupt The heat from the short circuit
cracks the drive gear. Thermal Damage
Damage
Disrupt Burn
Harm The short circuit burns Scald
Scorch
the drive gear.
Figure by MIT OCW.
45. A description of logic-based ontology for Damage.
Damage Genis Melt
Means End State Material Extent Means End State Material Extent
Force Deformed Friable Partial Heat Deformed Elastic Partial
Heat Pieces Flammable Total Total
Broken Integrity Brittle
Pierced Laminar
Cut Elastic
Category Level Category Level
Mid Level Low Level
KEY
Metaproperty Concept Role
Instead of traversing subsumption relations, logic representation supports arbitrary
binary relations between concepts. Matching starts with MidLevel concepts, e.g. Damage.
Figure by MIT OCW.