The document discusses the problem of knowledge acquisition in artificial intelligence. It describes knowledge acquisition as the critical bottleneck problem that has hindered the development of successful applied AI. The document outlines several historical problems with knowledge engineering, including a lack of hardware and trained knowledge engineers. It also discusses various methods that were developed to help with the process of eliciting and acquiring knowledge from experts, such as repertory grids, think aloud methods, and card sorting. Modern approaches and methodologies for building ontologies are also covered, such as CommonKADS and METHONTOLOGY.
Driving Style and Behavior Analysis based on Trip Segmentation over GPS Info...Marco Brambilla
Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage ``better" driving behaviour through immediate feedback
while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style.
In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour.
This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance
policies and car rentals.
Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different
behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Driving Style and Behavior Analysis based on Trip Segmentation over GPS Info...Marco Brambilla
Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage ``better" driving behaviour through immediate feedback
while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style.
In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour.
This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance
policies and car rentals.
Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different
behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This workshop is a hands-on introduction to machine learning with R and was presented on December 8, 2017 at the University of South Carolina for the 2017 Computational Biology Symposium held by the International Society for Computational Biology Regional Student Group-Southeast USA.
How Can AI Transform the Software Development Process?Capital Numbers
Ready to dive into the future of software development?
We have unveiled the incredible ways AI is reshaping the software development landscape.
From automated code generation to predictive analysis, this is a game-changer!
Eager to explore?
Check out below!
https://bit.ly/3S4dpcf
Multi-Objective Optimization using Non-Dominated Sorting Genetic Algorithm wi...Ahmed Gad
When solving a problem, the goal is not only solving it but also optimizing such solution. There might be multiple solutions to a problem and the challenge is to find the best of them. The more metrics defining the solution goodness, the harder finding the best solution. This presentation discusses one of the multi-objective optimization techniques called non-dominated sorting genetic algorithm II (NSGA-II) explaining its steps including non-dominated sorting, crowding distance, tournament selection, and genetic algorithm. The presentation works through a numerical example step-by-step.
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
"You can download this product from SlideTeam.net"
Machine Learning ML Overview Algorithms Use Cases and Applications is for the mid level managers giving information about Machine Learning, how Machine Learning works, Machine Learning algorithms and its use cases. You can also learn the difference between Machine learning vs Traditional programming to understand how to implement machine learning in a better way for business growth. https://bit.ly/2ZaVSG9
A brief history of artificial intelligence for businessJack C Crawford
Since the 1960s, Artificial Intelligence has promised us benefits in business and in our personal lives. This presentation takes us from the early days up to machine learning and applications for enterprise businesses that are delivering personalized experiences to customers ... to a "segment of one."
Presented by Matthew Brems and Melissa Hannebaum, students at Franklin College, documenting results of summer research under the direction of Dr. Robert Talbert, PhD.
Although my background is in sales, sales training and marketing, I have been influenced by production processes in two ways. One is fighting for deliveries for my customers. Another was working on a production set-up project in China for a customer several years ago. Through those experiences, I learned a great deal about process efficiencies and prepared material on it in this presentation. Have a look at it. Maybe there are some ideas in it that would be helpful in our processes.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This workshop is a hands-on introduction to machine learning with R and was presented on December 8, 2017 at the University of South Carolina for the 2017 Computational Biology Symposium held by the International Society for Computational Biology Regional Student Group-Southeast USA.
How Can AI Transform the Software Development Process?Capital Numbers
Ready to dive into the future of software development?
We have unveiled the incredible ways AI is reshaping the software development landscape.
From automated code generation to predictive analysis, this is a game-changer!
Eager to explore?
Check out below!
https://bit.ly/3S4dpcf
Multi-Objective Optimization using Non-Dominated Sorting Genetic Algorithm wi...Ahmed Gad
When solving a problem, the goal is not only solving it but also optimizing such solution. There might be multiple solutions to a problem and the challenge is to find the best of them. The more metrics defining the solution goodness, the harder finding the best solution. This presentation discusses one of the multi-objective optimization techniques called non-dominated sorting genetic algorithm II (NSGA-II) explaining its steps including non-dominated sorting, crowding distance, tournament selection, and genetic algorithm. The presentation works through a numerical example step-by-step.
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
"You can download this product from SlideTeam.net"
Machine Learning ML Overview Algorithms Use Cases and Applications is for the mid level managers giving information about Machine Learning, how Machine Learning works, Machine Learning algorithms and its use cases. You can also learn the difference between Machine learning vs Traditional programming to understand how to implement machine learning in a better way for business growth. https://bit.ly/2ZaVSG9
A brief history of artificial intelligence for businessJack C Crawford
Since the 1960s, Artificial Intelligence has promised us benefits in business and in our personal lives. This presentation takes us from the early days up to machine learning and applications for enterprise businesses that are delivering personalized experiences to customers ... to a "segment of one."
Presented by Matthew Brems and Melissa Hannebaum, students at Franklin College, documenting results of summer research under the direction of Dr. Robert Talbert, PhD.
Although my background is in sales, sales training and marketing, I have been influenced by production processes in two ways. One is fighting for deliveries for my customers. Another was working on a production set-up project in China for a customer several years ago. Through those experiences, I learned a great deal about process efficiencies and prepared material on it in this presentation. Have a look at it. Maybe there are some ideas in it that would be helpful in our processes.
Is a creative bottleneck costing your business? Hightail
- 73% of B2C marketers plan to boost the amount of content they create next year
- This is because of the explosion of new marketing channels
- Which has increase the demand of visual content
- Marketers have lots of tools for analyzing and automating campaigns
- But what about the fuel that powers these tools?
- But 75% admit that they don't have an effective creative collaboration process
- More content + lack of process = content bottleneck
- A content bottleneck wastes money
- Opportunities are missed
- Innovation is stifled
- Creative process technology is the next evolution in marketing
- A purpose-built creative tool like Hightail lets you collaborate on visual content more effectively
- It's time to shatter the content bottleneck
- Find out more at www.hightail.com
Nothing can be more frustrated than unable to
grow a company that doesn't have many
problems. Diamond Constrain Model helps
SMEs to grow healthy and realistically.
The core values include strategic driven
approach to position every move, installing
relevant management systems to set the
company in the proper path, build necessary growth
factors make the company growable, remove
bottlenecks and management taboos, etc.
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
Presentation of our paper at the WHISE workshop at ESWC 2016 on requirements for metadata over non-public datasets for the science & technology studies field.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
Linkitup: Link Discovery for Research DataRinke Hoekstra
Linkitup is a Web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. It extracts references from publications, and tries to find the corresponding Digital Object Identifier (DOI). Linkitup feeds the enriched metadata back as links to the original article in the repository, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output in its own right. In this paper, we compare Linkitup to the standard workflow of publishing linked data, and show that it significantly lowers the threshold for publishing linked research data.
A Network Analysis of Dutch Regulations - Using the Metalex Document ServerRinke Hoekstra
In this paper we explore the possibilities of using the Linked Data representation of all Dutch regulations stored in the MetaLex Doc- ument Server for the purposes of network analysis over the citation graph between regulations, both at the document level, and at the article level. We show that this is possible using relatively straightforward SPARQL queries, and present preliminary results of the analysis.
A Network Analysis of Dutch Regulations. Rinke Hoekstra. figshare.
http://dx.doi.org/10.6084/m9.figshare.689880
Retrieved 11:12, Oct 07, 2013 (GMT)
This presentation describes the use by Data2Semantics (http://www.data2semantics.org) of the VIVO portal (http://vivoweb.org) for interlinking researchers contributing to projects within the COMMIT programme (http://www.commit-nl.nl).
The Data2Semantics project (COMMIT P23) is all about enriching research data, and making it more reusable for future research. Using Linked Data for this task is a fairly obvious step to make (surprise!). However, there are several shortcomings the current practices in publishing Linked Data, that calls for a slightly
different approach which (hopefully) bridges a gap between Web 2.0 and Web 3.0. I will present a proof-of-concept service (Linkitup) that works on top of existing scientific data repositories, and allows individual researchers to enrich their data with additional (linked) metadata.
Talk about the use of Linked Data in historical research on census data. Has some slides about TabLInker as well (http://github.com/Data2Semantics/TabLinker). Part of the data2semantics project (http://data2semantics.org)
Presentatie voor de Belastingdienst in het kader van een onderzoek naar de (on)mogelijkheden rond het herkennen en extraheren van concepten en hun definities, en het representeren daarvan met Semantic Web standaarden.
History of Knowledge Representation (SIKS Course 2010)Rinke Hoekstra
The goal of AI research is the simulation and approximation of human intelligence by computers. To a large extent this comes down to the development of computational reasoning services that allow machines to solve problems. Robots are the stereotypical example: imagine what a robot needs to know before it is able to interact with the world the way we do? It needs to have a highly accurate internal representation of reality. It needs to turn perception into action, know how to reach its goals, what objects it can use to its advantage, what kinds of objects exist, etc.
The field of knowledge representation (KR) tries to deal with the problems surrounding the incorporation of some body of knowledge (in whatever form) in a computer system, for the purpose of automated, intelligent reasoning. In this sense, knowledge representation is the basic research topic in AI. Any artificial intelligence is dependent on knowledge, and thus on a representation of that knowledge. The history of knowledge representation has been nothing less than turbulent. The roller coaster of promise of the 50's and 60's, the heated debates of the 70's, the decline and realism of the 80's and the ontology and knowledge management hype of the 90's each left a clear mark on contemporary knowledge representation technology and its application.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
1. The Knowledge
Reengineering Bottleneck
Rinke Hoekstra
rinke.hoekstra@vu.nl
VU University Amsterdam/University of Amsterdam
vrijdag 24 februari 12
2. Knowledge Engineering
“Critical scientific problem [...] successful applied AI requires that
knowledge move from the heads of experts into programs”
FEIGENBAUM, E. A. (1984), Knowledge Engineering. Annals of the New York Academy of Sciences, 426: 91–107. doi: 10.1111/j.1749-6632.1984.tb16513.x
vrijdag 24 februari 12
3. ‣ The lack of adequate and appropriate hardware
‣ Lack of cumulation of AI methods and techniques
‣ Shortage of trained knowledge engineers
‣ The problem of knowledge acquisition
‣ The development gap
Problems of
Knowledge Engineering
vrijdag 24 februari 12
4. Knowledge Acquisition Bottleneck
“The problem of knowledge acquisition is the critical bottleneck
problem in artificial intelligence”
FEIGENBAUM, E. A. (1984), Knowledge Engineering. Annals of the New York Academy of Sciences, 426: 91–107. doi: 10.1111/j.1749-6632.1984.tb16513.x
vrijdag 24 februari 12
7. Knowledge Elicitation
Repertory Grids
Think Aloud Method
Cardsorting MYCIN and GUIDON
... Knowledge Types
vrijdag 24 februari 12
8. Knowledge Elicitation
Repertory Grids
Think Aloud Method
Cardsorting MYCIN and GUIDON
... Knowledge Types
CommonKADS
Engineering Methodology
Problem Solving Methods
Domain Models
vrijdag 24 februari 12
9. Knowledge Elicitation
Repertory Grids
Think Aloud Method
Cardsorting MYCIN and GUIDON
... Knowledge Types
CommonKADS
Engineering Methodology
Problem Solving Methods
Domain Models
Ontolingua
“Explicit specification of a shared conceptualization”
Sharing ontologies
vrijdag 24 februari 12
10. How to build
the right
ontology?
vrijdag 24 februari 12
11. How to build
the right Methodologies
Middle Out Approach
Documentation
Ontology
ontology?
Identify Capture
Purpose and
Uschold & Gruninger Specify
Scope
Ontology
METHONTOLOGY Guidelines
Motivating
Coding
Evaluation
KACTUS Scenarios
Ontology
Competency
SENSUS Questions
Integration
(KA)2
vrijdag 24 februari 12
12. How to build
the right Methodologies
Middle Out Approach
Documentation
Ontology
ontology?
Identify Capture
Purpose and
Uschold & Gruninger Specify
Scope
Ontology
METHONTOLOGY Guidelines
Motivating
Coding
Evaluation
KACTUS Scenarios
Ontology
Competency
SENSUS Questions
Integration
(KA)2
Top Ontology
Ontology Types
Representation Ontology
Generic Ontology
Top Foundation
Core Ontology
Generic Domain
Domain
Ontology Application Core
vrijdag 24 februari 12
13. How to build
the right Methodologies
Middle Out Approach
Documentation
Ontology
ontology?
Identify Capture
Purpose and
Uschold & Gruninger Specify
Scope
Ontology
METHONTOLOGY Guidelines
Motivating
Coding
Evaluation
KACTUS Scenarios
Ontology
Competency
SENSUS Questions
Integration
(KA)2
Top Ontology
Ontology Types
Representation Ontology
Generic Ontology
Top Foundation
Core Ontology
Generic Domain
Domain
Ontology Application Core
Principles
OntoClean
Ontology vs. Epistemology
vrijdag 24 februari 12
14. How to build
the right Methodologies
Middle Out Approach
Documentation
Ontology
ontology?
Identify Capture
Purpose and
Uschold & Gruninger Specify
Scope
Ontology
METHONTOLOGY Guidelines
Motivating
Coding
Evaluation
KACTUS Scenarios
Ontology
Competency
SENSUS Questions
Integration
(KA)2
Top Ontology
Ontology Types
Representation Ontology
Generic Ontology
Top Foundation
Core Ontology
Generic Domain
Domain
Ontology Application Core
Principles
OntoClean
Ontology vs. Epistemology
Ontology Reuse
Merging & Alignment
Modularization
Ontology Design Patterns
vrijdag 24 februari 12
15. Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
vrijdag 24 februari 12
16. Linked
LOV User Slideshare tags2con
Audio
Feedback 2RDF delicious
Moseley Scrobbler Bricklink Sussex
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GTAA
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tune stuhl- Resource NTU
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tions
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(Data News LEM
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tion (En- (DBTune) Last.FM ia theses. LCSH Rådata
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AKTing) (rdfize) my fr nå!
data.gov. data.go Codes
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tors Program MeSH
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mes BBC IdRef GND
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BBC America Media
DBpedia Calames heim
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legislation Local nl RDF graphie
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Insti- York Open Mashup Cultural
tutions Times URI Greek P20
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codes DBpedia ECS Wiki
statistics lobid
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uk Concept ECS ampton sations
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ticians stan- reference ampton
data.gov.uk book Freebase Explorer) Budapest
dards data.gov. NASA EPrints
uk intervals Project OAI
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meter
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CORDIS Explorer) Linked Eurécom
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(FUB) Sensor Data
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Central) riese Enipedia
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(Ontology totl.net
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graphy Net WordNet WordNet JISC
(W3C) (RKB
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dations Alpine bible
palities Viajero OC
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Tourism KEGG
Ocean
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Enzyme PBAC Geographic
Metoffice GEMET ChEMBL
Italian Drilling OMIM KEGG
Weather Open
public Codices AEMET Linked MGI Pathway
schools Forecasts
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Open InterPro GeneID Publications
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AMP UniParc UniRef UniSTS Government
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Yahoo! Airports Museums pound
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wrapper
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activity UniPath
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dam medu- Open
rates Numbers
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As of September 2011
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
vrijdag 24 februari 12