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Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
Ontology-based Rules for Recommender Systems
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Ontology-based Rules for Recommender Systems

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Nowadays, smart devices perceive a large amount of information from device sensors, usage, and other sources which contribute to defining the user’s context and situations. The main problem is that …

Nowadays, smart devices perceive a large amount of information from device sensors, usage, and other sources which contribute to defining the user’s context and situations. The main problem is that although the data is available, it is not processed to help the user deal with this information easily. Our approach is based on the assumption that, given that this information can be unified in a single personal data space, it can be used to discover and learn rules to provide the user with personal recommendations. In this paper we introduce a Rule Management Ontology to support the representation of event-based rules that trigger specific actions. We also discuss how a context listener component can provide recommendations based on the perceived context-data, or in the future, semi-automatically learnt rules.

Download the full paper from: http://ceur-ws.org/Vol-919/paper5.pdf

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  • Personal Information Overload Smart devices, personal services and social networks (generate lots of disconnected information)Increasingly hard to manage personal information sphereIncreasingly complex features (apps for everything, privacy settings, etc.)Increasingly hard to stay in control (users can’t keep up with their daily tasks)Loss in ProductivityContext-aware Proactive SystemRecommends Items & Actions (based on the user’s perceived activity)Makes decisions & Performs tasksHelps you stay in control (of your day-to-day tasks)
  • Work presented is part of a bigger picture di.me project (European project) Project’s main objectives for an intelligent userwarePersonal Information Sphere Integration & Management: Integrated ontologies for modelling various PIM domains.For further details refer to Scerri et al. 2012: di.me: Context-Aware, Privacy-Sensitive Management of the Integrated Personal Information Sphere.User Activity Context Monitoring & Interpretation: Extraction of context from device and virtual sensors (e.g. Microposts).For further details refer to Scerri et al., 2012: DCON: Interoperable Context Representation for Pervasive Environments.Context-driven Recommendation & Automation (FOCUS OF THIS PRESENTATION): Context-driven rules are defined either by the user manually, or by the system automatically (learning rules). Rules consist of one of more conditions which trigger one or more corresponding actions. Rule conditions are defined based on known concepts in the Personal Data Cloud (e.g. people, emails, events, files), and which satisfy certain constraints (e.g. people in a group, emails having certain subject, files with a certain tag). Actions vary from notifications (in the UI) to automated system actions. The parameters of an action can consist of items in both the Personal Data Cloud (e.g. forward an existing email, suggest a related file) and the LOD cloud (e.g. suggest a nearby restaurant, similar artist/movie).
  • Other research (including di.me) targets the modeling of Personal Information. The users are able to manage their personal information such as creating new tasks, events, and send emails to known contacts manuallyIn di.me we also unify multiple activity context sources in a model in order to gather meaningful data with regard to the user’s environment(FOCUS OF THIS PRESENTATION)In this work we target the automatic control of Personal Information via rules, based on the perceived rich context data. Such automatic tasks include:task automation – automating daily tasks such as “switch my mobile to silent when I am in the office”item discovery – provide recommendations of items to the user based on their contextprivacy control – provide privacy suggestions to the user based on the peers who are in the same environment (e.g. warn me it a person who has a low trust value is in the same room as me)
  • The Rule Management Ontology allow the definition of context-driven rules based on the known items in the personal data cloud.Currently, in di.me rules can be defined by the user via a UI in order to automate tasks or receive recommendations based on their current context environmentWe are also investigating techniques how rules can be automatically discovered and defined via user activity patterns.Apart from modeling, we also need to have a processor which process the user’s activity context and eventually triggers any rules giving in-context recommendations or automate tasksFinally, we also focus on exploiting the Linked Open Data cloud in order to give the user enriched recommendations, which are not known to the user, based on his current context environment.
  • DRMO – The model which handles the definition of rules based on the Personal Data CloudDRMO Rule Registering – Transforms DRMO instances into SPARQL queries and registers them into a Rule Pool in the Context ListenerRule Filtering – Filters possible rules which could trigger according to the perceived contextPattern Matching – Execute the rule’s SPARQL query against the working memory (the event log and data sources) and triggers any satisfied rulesTime-Window Process – Removes events from the Event Log after a certain period of time (Similar to CQELS)
  • The ontology is modeled on the Event-Condition-Action pattern conceptsThe ECA pattern is a structure used in event-driven architectures, where the event specifies what triggers the rule, in the form of a number of conditions, and the action part specifies what is to be executed. A rule is represented by the concept drmo:Rule, which corresponds to the Event in the ECA pattern.Incontrast to traditional event-driven architectures, DRMO rules are not dependent on a specific list of events, such as “on update” or “on delete”.A DRMO rule is composed (drmo:isComposedOf) of a number of drmo:Condition blocks, which triggers (drmo:triggers) one or more drmo:Actioninstances.
  • Conditions can consist of anything from a specific resource (e.g. an item in the Personal Information Model (PIM)) to an entire RDF named graph, and can indicate it’s creation, update and deletion in the PIM.Multiple condition blocks can be composed together using logical (and,or) or event (succeeded, preceeded) operators.Condition blocks can also be negated.A condition block can have one or more constraints (which is also an instance of condition)These are used to give the rule conditions values on which tests can be performed on. For example rather than just saying “if I receive an email”, the user can define a rule such as “if I receive an email from anna”Relational Operators such as “containts”, “<=“ etc.. can be defined for the values in the constraintEach condition property helps in transforming a condition block into a SPARQL triple pattern.
  • thedrmo:Action class specifies an action instances (such as Recommend) which is understood by the userware.The drmo:Action class have two properties: hasSubject property – which specifies any parameters that has to be passed to the actions, and the hasObject property – indicates the receiver of the action (e.g. ‘Send’ emailX to personA)
  • Here we describe the steps from rule transformation to rule activation….A rule is learnt is created as a DRMO instanceIt is then transformed into a SPARQL query and register it into a Rule Pool, which will help us in the pattern matchingThe Context Rule Listener has a module for Pattern Matching Process, a trigger store and an Event Disposal module which removes events from event log. The Pattern-Matching Process is composed of a rule filtering, pattern matching and an action executorThe event log is the part of the RDF triple store and will log the events perceived in the di.me userwareWhen a new event is perceived, the pattern matching process startsFilter Rules from rule pool - by choosing those rules which might trigger according to the list of event occurrencesPattern Matching – chosen rules are checked for satisfied constraints (by executing the sparql queries)Action Executor – any queries which return a result will have their corresponding action triggeredThe action executor connects to the required LOD source in-order to enrich the personalised recommendation
  • In the future we will be investigating further the process of automating ruleIn di.me, past user activity context snapshots models are time-stamped and persisted into a History Model (DUHO).Using CBR, one of the techniques under investigation, similar user patterns can be identified and new rules can be learnt.In the Context Listener we are investigating techniques which manage rule conflicts and handle repeated defined rules.Due to resource-intensive context listening, performance needs to be optimisedAn evaluation for determining the effectiveness and usability of the system will also be conducted
  • Transcript

    • 1. Digital Enterprise Research Institute www.deri.ie Ontology-based Rules for Recommender Systems Jeremy Debattista, Simon Scerri, Ismael Rivera, and Siegfried Handschuh Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Enabling Networked Knowledge
    • 2. MotivationDigital Enterprise Research Institute www.deri.ie  Personal Information Overload  Many smart devices, personal services and social networks  Increasingly hard to manage  Increasingly complex features  Loss in Productivity  Hard to keep up with day-to-day tasks  Context-aware Proactive System  Recommends Items & Actions  Makes decisions & Performs tasks if I am “Travelling” and it is “Lunch Time” recommend a  Helps you stay in control nearby “Restaurant” Enabling Networked Knowledge
    • 3. di.me ProjectDigital Enterprise Research Institute www.deri.ie Personal LOD Data Cloud Cloud Context Listener PIM Personal Information Crawler Context Extractor  Intelligent di.me Userware  Personal Information Sphere Integration & Management  User Activity Context Monitoring & Interpretation  Context-driven Recommendation & Automation Enabling Networked Knowledge
    • 4. Problem SpecificationDigital Enterprise Research Institute www.deri.ie  Distributed Personal Information Management  Scerri et al. 2012: di.me: Context-Aware, Privacy-Sensitive Management of the Integrated Personal Information Sphere.  User Activity Context Integration  Scerri et al., 2012: DCON: Interoperable Context Representation for Pervasive Environments.  Users unable to exploit existing rich context data to automatically control Personal Information  Task Automation  Item Discovery  Privacy Control Enabling Networked Knowledge
    • 5. ObjectivesDigital Enterprise Research Institute www.deri.ie  Enable declarative way for users to define context-driven rules on top of the Personal Data Cloud  Enable automatic discovery of rules based on user activity patterns.  Process user activity context to trigger in-context recommendations.  Exploit Linked Open Data cloud to improve recommendations. Enabling Networked Knowledge
    • 6. ApproachDigital Enterprise Research Institute www.deri.ie  Context-driven Rule Modeling  di.me Rule Management Ontology (DRMO)  Context Listener  DRMO Rule Registering  Rule Filtering  Pattern Matching  Time-Window Process Enabling Networked Knowledge
    • 7. DRMODigital Enterprise Research Institute www.deri.ie  Based on ECA pattern  drmo:Rule represents the “unconditional” Event in ECA Minimized version of the DRMO showing the main concepts and properties Enabling Networked Knowledge
    • 8. DRMODigital Enterprise Research Institute www.deri.ie  Defining a condition based on an event type in the PIM Condition Categories  Logical and Event Operators  Multiple Constraints  Represents SPARQL triple pattern Condition Properties for rule creation Enabling Networked Knowledge
    • 9. DRMODigital Enterprise Research Institute www.deri.ie  Instance understood by the system  Allows the definition of the receiver (hasObject) and the contents/parameters of Action Properties the actions (hasSubject) Enabling Networked Knowledge
    • 10. A Context ListenerDigital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge
    • 11. Future WorkDigital Enterprise Research Institute www.deri.ie  Automatic discovery of rules via CBR.  User activity context snapshots are time-stamped and persisted into a History Model (DUHO)  CBR techniques identify similar patterns of context and corresponding actions to define new rules  Enhancing the Context Listener.  Technique for managing rule conflicts.  Handling repeated rules.  Performance Optimisation  Evaluation for determining effectiveness and usability of the system. Enabling Networked Knowledge
    • 12. SummaryDigital Enterprise Research Institute www.deri.ie  Contributions  Ontology-driven recommender system (DRMO).  Definition of Rules – Manually via a User Interface – Learning of rules from PIM models.  Recommendation of items unknown to the KB via LOD.  Scalable Context Listener for rule triggering. Enabling Networked Knowledge
    • 13. Contact InformationDigital Enterprise Research Institute www.deri.ie  Forward any questions to jeremy.debattista@deri.org This work is supported in part by the European Commission under the Seventh Framework Program FP7/2007-2013 (digital.me – ICT-257787) and in part by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Líon-2). http://www.dime-project.eu/ Enabling Networked Knowledge

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