SlideShare une entreprise Scribd logo
1  sur  21
Télécharger pour lire hors ligne
Modelling and Managing Ambiguous
Context in Intelligent Environments
    Aitor Almeida, Diego López-de-Ipiña
   Deusto Institute of Technology - DeustoTech
               University of Deusto
                   Bilbao, Spain
       {aitor.almeida, dipina}@deusto.es
Need for ambiguity modeling
• We can’t take certainty for granted when modeling real world
  environments
   – Unreliable sensors: Sensors usually have a known degree of precision
   – Conflicting measures: What happens if various sensors of a room
     provide different measures?
   – Users’ subjective view of reality: the does not carry the same exact
     meaning for all the users.
Components of the ambiguity
• We have considered two aspects of the ambiguity:
  uncertainty and vagueness
• Uncertainty models the truthfulness of the different context
  data by assigning to them a certainty factor
   – E.g. The temperature of this room is 23 ºC with a certainty of 0.9
• Vagueness helps us to model those situations where the
  boundaries between categories are not clearly defined
   – E.g. The room can be cold, medium or hot.
AMBI2ONT: An ontology for the
          ambiguous reality
• We have created an ontology that takes into account the
  certainty (represented by a certainty factor, CF) and the
  vagueness (represented by fuzzy sets) of the context.
   – “the temperature of the room is 27ºC with a certainty factor of 0.2 and
     18ºC with a certainty factor of 0.8”
   – “the temperature of the room is cold with a membership of 0.7”
AMBI2ONT: An ontology for the
     ambiguous reality
AMBI2ONT: An ontology for the
          ambiguous reality
• Each ContextData individual has the following properties:
   – crisp_value: the measure taken by the associated sensor. In our
     system a sensor is defined as anything that provides context
     information.
   – certainty_factor: the degree of credibility of the measure. This metric
     is given by the sensor that takes the measure and takes values
     between 0 and 1.
   – linguistic_term: each measure has its fuzzy
     representation, represented as the linguistic term name and the
     membership degree for that term.
AMBI2ONT: An ontology for the
     ambiguous reality
Semantic Context Management For
        Ambiguous Data
Semantic Context Management For
          Ambiguous Data
1. Adding the measure
   – The sensor must provide the measure type, its value, location and a
     certainty factor
2. Processing the semantic and spatial data:
   –   We apply a semantic inference process to achieve two goals:
       •   make explicit the hidden implicit knowledge in the ontology
       •   infer the positional information of each measure
   –   Two different sets of rules: the semantic rules and the spatial
       heuristic rules
Semantic Context Management For
          Ambiguous Data
• Example of semantic rules:
Semantic Context Management For
          Ambiguous Data
• Example of spatial rules:
Semantic Context Management For
          Ambiguous Data
3. The data fusion process
   –   Each room can have multiple sensors that provide the same context data
       (e.g various thermometers in the same room).
   –   The values and certainty factor of those measures can differ.
   –   The process refines those individual measures into a single global
       measure for each room.
   –   We use two types of strategies for this process: tourney and
       combination.
   –   Using the tourney strategy the measure with the best CF is selected as
       the global measure of the room.
   –   The combination strategy has three different behaviors :
       •   Severe: The worst certainty factor from all the input measures is assigned to the
           combined measure.
       •   Indulgent: The best certainty factor from all the input measures is assigned to the
           combined measure.
       •   Cautious: An average certainty factor is calculated using the certainty factor from the
           input measures.
Semantic Context Management For
          Ambiguous Data
3. The data fusion process
   –   To determine the combined measure value we weight the individual
       values:




            Where:
                     m: the measure values.
                     cf: the measure certainty factor.
Semantic Context Management For
          Ambiguous Data
4. Processing the ambiguity
   –   We have modified the JFuzzyLogic Open Source fuzzy reasoner to
       accept also uncertainty information
   –   The modified reasoner supports two types of uncertainty, uncertain
       data and uncertain rules.
   –   The first type occurs when the input data is not completely reliable
Semantic Context Management For
          Ambiguous Data
4. Processing the ambiguity
   –   The second type of uncertainty takes place when the outcome of a
       rule is not fixed
       •   E.g. “if the barometric pressure is high and the temperature is low there is a 60%
           chance of rain”
Semantic Context Management For
          Ambiguous Data
4. Processing the ambiguity
   –   Uncertainty and fuzziness can appear in the same rule and influence
       each other.
       •   To process this we use the model described in “R. A. Orchard. FuzzyCLIPS Version
           6.04A User’s Guide. Integrated Reasoning Institute for Information Technology
           National Research Council Canada. 1998”.
   –   This model contemplates three different situations:
       •   CRISP Simple Rule where both antecedent and matching fact are crisp values,
       •   FUZZY_CRISP Simple Rule where both the antecedent and matching fact are fuzzy
           and the consequent is crisp and finally the
       •   FUZZY_FUZZY Simple rule where all three are fuzzy.
Semantic Context Management For
          Ambiguous Data
4. Processing the ambiguity
   –   CF in CRISP Simple Rule




                Where:
                   CFc: the certainty factor of the consequent.
                   CFr: the certainty factor of the rule
                   CFf: the certainty factor of the fact
Semantic Context Management For
          Ambiguous Data
4. Processing the ambiguity
   –   CF in FUZZY_CRISP Rules

       Where S is the measure of similarity between both fuzzy sets and is calculated using
       the following formula:




       Where:

       And:
Semantic Context Management For
          Ambiguous Data
4. Processing the ambiguity
   –   Finally in the case of FUZZY_FUZZY Simple Rule the certainty factor of
       the consequent is calculated using the same formula than in the
       CRISP Simple RULE.
   –   Currently we do not support this type of combined reasoning for
       complex rules that involve multiple clauses in their antecedent.
Future Work
•   Create a mechanism that automatically assesses the certainty factor of a
    sensor comparing its data with the one provided by other sensors.
    –   This will allow us to identify and discard malfunctioning sensors automatically.
•   Develop an ecosystem of reasoners to distribute the inference process.
    –   We hope that this distribution will lead to a more agile and fast reasoning over the
        context data, allowing us to combine less powerful devices to obtain a rich and
        expressive inference
•   Explore the possibility of including uncertainty in the membership
    functions.
Thanks for your attention

                       Questions?
This work has been supported by project grant TIN2010-20510-C04-03
(TALIS+ENGINE), funded by the Spanish Ministerio de Ciencia e Innovación

Contenu connexe

Similaire à Modelling and Managing Ambiguous Context in Intelligent Environments

interpolation
interpolationinterpolation
interpolation8laddu8
 
Decentralized Workflow Execution using a Chemical Metaphor
Decentralized Workflow Execution using a Chemical MetaphorDecentralized Workflow Execution using a Chemical Metaphor
Decentralized Workflow Execution using a Chemical MetaphorHéctor Fernández
 
5530: Chapter 24
5530: Chapter 245530: Chapter 24
5530: Chapter 24bodo-con
 
eigen valuesandeigenvectors
eigen valuesandeigenvectorseigen valuesandeigenvectors
eigen valuesandeigenvectors8laddu8
 
linear transformation
linear transformationlinear transformation
linear transformation8laddu8
 
Quality improvement overwiew presentation
Quality improvement overwiew presentationQuality improvement overwiew presentation
Quality improvement overwiew presentationsaja1111
 
SiriusT3 Brochure
SiriusT3 BrochureSiriusT3 Brochure
SiriusT3 BrochureJon Mole
 
Using Database Constraints Wisely
Using Database Constraints WiselyUsing Database Constraints Wisely
Using Database Constraints Wiselybarunio
 
[EN] Club Automation presentation "Quality Model for Industrial Automation", ...
[EN] Club Automation presentation "Quality Model for Industrial Automation", ...[EN] Club Automation presentation "Quality Model for Industrial Automation", ...
[EN] Club Automation presentation "Quality Model for Industrial Automation", ...Itris Automation Square
 
partial diffrentialequations
partial diffrentialequationspartial diffrentialequations
partial diffrentialequations8laddu8
 
Cloud Computing with InduSoft
Cloud Computing with InduSoftCloud Computing with InduSoft
Cloud Computing with InduSoftAVEVA
 
Quality improvement - Jess and Alex
Quality improvement - Jess and AlexQuality improvement - Jess and Alex
Quality improvement - Jess and Alexlexie_daryan
 
Hacker tooltalk: Social Engineering Toolkit (SET)
Hacker tooltalk: Social Engineering Toolkit (SET)Hacker tooltalk: Social Engineering Toolkit (SET)
Hacker tooltalk: Social Engineering Toolkit (SET)Chris Hammond-Thrasher
 
fourier series
fourier seriesfourier series
fourier series8laddu8
 

Similaire à Modelling and Managing Ambiguous Context in Intelligent Environments (17)

Cyber Crime
Cyber CrimeCyber Crime
Cyber Crime
 
interpolation
interpolationinterpolation
interpolation
 
Decentralized Workflow Execution using a Chemical Metaphor
Decentralized Workflow Execution using a Chemical MetaphorDecentralized Workflow Execution using a Chemical Metaphor
Decentralized Workflow Execution using a Chemical Metaphor
 
G. Pillsbury Stanislaus Online Readiness at Stanislaus
G. Pillsbury Stanislaus Online Readiness at StanislausG. Pillsbury Stanislaus Online Readiness at Stanislaus
G. Pillsbury Stanislaus Online Readiness at Stanislaus
 
5530: Chapter 24
5530: Chapter 245530: Chapter 24
5530: Chapter 24
 
eigen valuesandeigenvectors
eigen valuesandeigenvectorseigen valuesandeigenvectors
eigen valuesandeigenvectors
 
linear transformation
linear transformationlinear transformation
linear transformation
 
Use case+2-0
Use case+2-0Use case+2-0
Use case+2-0
 
Quality improvement overwiew presentation
Quality improvement overwiew presentationQuality improvement overwiew presentation
Quality improvement overwiew presentation
 
SiriusT3 Brochure
SiriusT3 BrochureSiriusT3 Brochure
SiriusT3 Brochure
 
Using Database Constraints Wisely
Using Database Constraints WiselyUsing Database Constraints Wisely
Using Database Constraints Wisely
 
[EN] Club Automation presentation "Quality Model for Industrial Automation", ...
[EN] Club Automation presentation "Quality Model for Industrial Automation", ...[EN] Club Automation presentation "Quality Model for Industrial Automation", ...
[EN] Club Automation presentation "Quality Model for Industrial Automation", ...
 
partial diffrentialequations
partial diffrentialequationspartial diffrentialequations
partial diffrentialequations
 
Cloud Computing with InduSoft
Cloud Computing with InduSoftCloud Computing with InduSoft
Cloud Computing with InduSoft
 
Quality improvement - Jess and Alex
Quality improvement - Jess and AlexQuality improvement - Jess and Alex
Quality improvement - Jess and Alex
 
Hacker tooltalk: Social Engineering Toolkit (SET)
Hacker tooltalk: Social Engineering Toolkit (SET)Hacker tooltalk: Social Engineering Toolkit (SET)
Hacker tooltalk: Social Engineering Toolkit (SET)
 
fourier series
fourier seriesfourier series
fourier series
 

Dernier

Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.YounusS2
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfAnna Loughnan Colquhoun
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfDianaGray10
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024SkyPlanner
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxYounusS2
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPathCommunity
 
Things you didn't know you can use in your Salesforce
Things you didn't know you can use in your SalesforceThings you didn't know you can use in your Salesforce
Things you didn't know you can use in your SalesforceMartin Humpolec
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureEric D. Schabell
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024D Cloud Solutions
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
 
Introduction to Quantum Computing
Introduction to Quantum ComputingIntroduction to Quantum Computing
Introduction to Quantum ComputingGDSC PJATK
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.francesco barbera
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfinfogdgmi
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaborationbruanjhuli
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7DianaGray10
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Adtran
 

Dernier (20)

Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdf
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptx
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation Developers
 
Things you didn't know you can use in your Salesforce
Things you didn't know you can use in your SalesforceThings you didn't know you can use in your Salesforce
Things you didn't know you can use in your Salesforce
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability Adventure
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
 
Introduction to Quantum Computing
Introduction to Quantum ComputingIntroduction to Quantum Computing
Introduction to Quantum Computing
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdf
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™
 

Modelling and Managing Ambiguous Context in Intelligent Environments

  • 1. Modelling and Managing Ambiguous Context in Intelligent Environments Aitor Almeida, Diego López-de-Ipiña Deusto Institute of Technology - DeustoTech University of Deusto Bilbao, Spain {aitor.almeida, dipina}@deusto.es
  • 2. Need for ambiguity modeling • We can’t take certainty for granted when modeling real world environments – Unreliable sensors: Sensors usually have a known degree of precision – Conflicting measures: What happens if various sensors of a room provide different measures? – Users’ subjective view of reality: the does not carry the same exact meaning for all the users.
  • 3. Components of the ambiguity • We have considered two aspects of the ambiguity: uncertainty and vagueness • Uncertainty models the truthfulness of the different context data by assigning to them a certainty factor – E.g. The temperature of this room is 23 ºC with a certainty of 0.9 • Vagueness helps us to model those situations where the boundaries between categories are not clearly defined – E.g. The room can be cold, medium or hot.
  • 4. AMBI2ONT: An ontology for the ambiguous reality • We have created an ontology that takes into account the certainty (represented by a certainty factor, CF) and the vagueness (represented by fuzzy sets) of the context. – “the temperature of the room is 27ºC with a certainty factor of 0.2 and 18ºC with a certainty factor of 0.8” – “the temperature of the room is cold with a membership of 0.7”
  • 5. AMBI2ONT: An ontology for the ambiguous reality
  • 6. AMBI2ONT: An ontology for the ambiguous reality • Each ContextData individual has the following properties: – crisp_value: the measure taken by the associated sensor. In our system a sensor is defined as anything that provides context information. – certainty_factor: the degree of credibility of the measure. This metric is given by the sensor that takes the measure and takes values between 0 and 1. – linguistic_term: each measure has its fuzzy representation, represented as the linguistic term name and the membership degree for that term.
  • 7. AMBI2ONT: An ontology for the ambiguous reality
  • 8. Semantic Context Management For Ambiguous Data
  • 9. Semantic Context Management For Ambiguous Data 1. Adding the measure – The sensor must provide the measure type, its value, location and a certainty factor 2. Processing the semantic and spatial data: – We apply a semantic inference process to achieve two goals: • make explicit the hidden implicit knowledge in the ontology • infer the positional information of each measure – Two different sets of rules: the semantic rules and the spatial heuristic rules
  • 10. Semantic Context Management For Ambiguous Data • Example of semantic rules:
  • 11. Semantic Context Management For Ambiguous Data • Example of spatial rules:
  • 12. Semantic Context Management For Ambiguous Data 3. The data fusion process – Each room can have multiple sensors that provide the same context data (e.g various thermometers in the same room). – The values and certainty factor of those measures can differ. – The process refines those individual measures into a single global measure for each room. – We use two types of strategies for this process: tourney and combination. – Using the tourney strategy the measure with the best CF is selected as the global measure of the room. – The combination strategy has three different behaviors : • Severe: The worst certainty factor from all the input measures is assigned to the combined measure. • Indulgent: The best certainty factor from all the input measures is assigned to the combined measure. • Cautious: An average certainty factor is calculated using the certainty factor from the input measures.
  • 13. Semantic Context Management For Ambiguous Data 3. The data fusion process – To determine the combined measure value we weight the individual values: Where: m: the measure values. cf: the measure certainty factor.
  • 14. Semantic Context Management For Ambiguous Data 4. Processing the ambiguity – We have modified the JFuzzyLogic Open Source fuzzy reasoner to accept also uncertainty information – The modified reasoner supports two types of uncertainty, uncertain data and uncertain rules. – The first type occurs when the input data is not completely reliable
  • 15. Semantic Context Management For Ambiguous Data 4. Processing the ambiguity – The second type of uncertainty takes place when the outcome of a rule is not fixed • E.g. “if the barometric pressure is high and the temperature is low there is a 60% chance of rain”
  • 16. Semantic Context Management For Ambiguous Data 4. Processing the ambiguity – Uncertainty and fuzziness can appear in the same rule and influence each other. • To process this we use the model described in “R. A. Orchard. FuzzyCLIPS Version 6.04A User’s Guide. Integrated Reasoning Institute for Information Technology National Research Council Canada. 1998”. – This model contemplates three different situations: • CRISP Simple Rule where both antecedent and matching fact are crisp values, • FUZZY_CRISP Simple Rule where both the antecedent and matching fact are fuzzy and the consequent is crisp and finally the • FUZZY_FUZZY Simple rule where all three are fuzzy.
  • 17. Semantic Context Management For Ambiguous Data 4. Processing the ambiguity – CF in CRISP Simple Rule Where: CFc: the certainty factor of the consequent. CFr: the certainty factor of the rule CFf: the certainty factor of the fact
  • 18. Semantic Context Management For Ambiguous Data 4. Processing the ambiguity – CF in FUZZY_CRISP Rules Where S is the measure of similarity between both fuzzy sets and is calculated using the following formula: Where: And:
  • 19. Semantic Context Management For Ambiguous Data 4. Processing the ambiguity – Finally in the case of FUZZY_FUZZY Simple Rule the certainty factor of the consequent is calculated using the same formula than in the CRISP Simple RULE. – Currently we do not support this type of combined reasoning for complex rules that involve multiple clauses in their antecedent.
  • 20. Future Work • Create a mechanism that automatically assesses the certainty factor of a sensor comparing its data with the one provided by other sensors. – This will allow us to identify and discard malfunctioning sensors automatically. • Develop an ecosystem of reasoners to distribute the inference process. – We hope that this distribution will lead to a more agile and fast reasoning over the context data, allowing us to combine less powerful devices to obtain a rich and expressive inference • Explore the possibility of including uncertainty in the membership functions.
  • 21. Thanks for your attention Questions? This work has been supported by project grant TIN2010-20510-C04-03 (TALIS+ENGINE), funded by the Spanish Ministerio de Ciencia e Innovación