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1 Engineering a Knowledge Base for an Intelligent Personal Assistant Vinay K. Chaudhri Adam Cheyer Richard Guili Bill Jarrold Karen L. Myers John Niekrasz.

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Presentation on theme: "1 Engineering a Knowledge Base for an Intelligent Personal Assistant Vinay K. Chaudhri Adam Cheyer Richard Guili Bill Jarrold Karen L. Myers John Niekrasz."— Presentation transcript:

1 1 Engineering a Knowledge Base for an Intelligent Personal Assistant Vinay K. Chaudhri Adam Cheyer Richard Guili Bill Jarrold Karen L. Myers John Niekrasz

2 2 Outline Problem KB Development Knowledge Engineering Challenges Deploying the Knowledge Base Future Work Summary and Conclusions

3 3 Problem Cognitive Assistant that Learns and Organizes (CALO) Learn from experience Be told what to do Explain what it is doing Reflect on experience Respond robustly to surprises Situated in an office environment

4 4 CALO Functions Organize & Manage Information Schedule & Organize in Time Acquire, Allocate Resources Prepare Information Products Monitor & Manage Tasks Observe & Mediate Interactions CALO

5 5 An Ontology is needed to permit sharing of data and knowledge across these various subcomponents

6 6 Learning in Context Provides greater value, needs fewer examples Important? Meeting? …? To: Bob@sri.com Subj: fMRI meeting We need to meet soon to discuss the paper deadline. Learning Algorithm To: Sue @ sri.com Subj: Re: fMRI meeting Ok, I suggest Wednesday at 4pm. To: Bob@ sri.com Subj: Re: fMRI meeting See you then. Attached is the current draft. Leader of Projects Meetings Files Manager of Relevant to Works on Meeting for

7 7

8 8 Example Functionality CALO will automatically put together a portfolio of information (e.g., mail, files, web pages) relevant to your projects and to upcoming meetings CALO will summarize, prioritize, and classify an email. CALO will identifies the action items, and produce an annotated meeting record.

9 9 Test Questions: PQs and Iqs (Parameterized Questions & Instantiated Questions) What |sc:%Meeting| is being discussed or suggested in |io:%EmailMessage|? What is the duration suggested for the meeting discussed in |io:%EmailMessage|? What is the time suggested for the meeting discussed in |io:%EmailMessage|? What date is mentioned in |io:%Email|? What location is mentioned in |io:%Email|? What time is mentioned in |io:%Email|?

10 10 Outline Problem KB Development Knowledge Engineering Challenges Deploying the Knowledge Base Future Work Summary and Conclusions

11 11 KB Development Knowledge Representation Framework Development Process Overview of Knowledge Content

12 12 Knowledge Representation Framework The core ontology The Component Library (CLIB) Barker, Porter, Clark KCAP 2001. CLIB is written in KM (Knowledge Machine) Re-usable, Composable, Domain- Independent Library Richly axiomatized event classes (e.g. Move, Attach)

13 13 Knowledge Representation Framework We used OWL for sharing the knowledge with modules that needed to load the ontology We developed a KM to OWL translator We limited the translation to only that subset of KM that could be translated into OWL

14 14 Knowledge Representation Framework SPARK procedure language for representing knowledge about performing automated tasks Expressiveness of SPARK was essential for representing complex process structures necessary for accommodating office tasks We represent uncertain knowledge using weighted rules Weights are necessary to capture the output from learning methods We are still able to expose a deterministic interface to the rest of the system

15 15 Development Process Distributed team with over 20 different research groups We solicited requirements List of classes and relations Formal axioms Large scale reuse of ontologies iCalendar Work of Radarnetworks Ontology Simplification Eliminate unneeded constructs Simplify representation Distributed development Use Protégé for knowledge authoring

16 16 Overview of Knowledge Content - The Office Ontology People First name, last name Contacts Postal address, home address, work address Emails Sender, receiver, etc Calendars Start, end, repetition Projects/Tasks Meetings Meeting types, discussion topics, meeting roles Organizations Organizational roles Learning Methods Capability of learning methods, data needed Provenance Source of an information

17 17 Overview of Knowledge Content - Example Class ChatSessionMessage Comment: "Instances of #$ChatSessionMessage are complete messages passed between chat participants during a #$ChatSession. For example, if Bob and Fred are involved in CALO Online Chat Bob might send the chat message 'Hi Fred' to Fred. Such a message is a #$ChatSessionMessage. More specifically, it is a #$ChatTextMessage. Please see the subclasses of #$ChatSessionMessage because developers are more likely to be referencing its subclasses. A negative example of #$ChatSessionMessage would be a portion of the message sent from Bob to Fred such as 'Hi Fr'. Superclasses: ElectronicMessage ComputerEncodedInformation

18 18 Overview of Knowledge Content Process model system (PTIME) has approx 50 process models In Core plus Office Ontology Approx 1000 classes Approx 500 relations

19 19 Outline Problem KB Development Knowledge Engineering Challenges Deploying the Knowledge Base Future Work

20 20 Knowledge Engineering Challenges Reusing iCalendar Representing Meetings Representing Tasks Ensuring Interoperability

21 21 Reusing iCalendar Prune the relations needed All the relations were not needed We did not want to bloat the ontology We retained only what was needed Define symbol name mappings We renamed the relations to fit in our standard naming convention But, we retained the mappings to the original name Link to the rest of the ontology We needed to define iCalendar relations using existing vocabulary of People, and Time

22 22 Representing Meetings Communication Model Modeling multi-modal communication Modeling Discourse Structure Modeling Meeting Activity

23 23 Representing Meetings Model of Communication

24 24 Representing Meetings Modeling Discourse Structure Modeling Dialog Structures Define Communicate subclasses such as Statement, Question, BackChannel, etc. Modeling Argument Structure Define coarser level actions such as Raising an Issue, Proposal, Acceptance, etc.

25 25 Representing Meetings Modeling the Meeting Activity Provide ways to segment a meeting Physical state of participants Sitting, standing, etc Agenda state of participants Position within a previously defined meeting structure

26 26 Representing Tasks Tasks are modeled in terms of a task type a set of input and output parameters, Whether a parameter is required or optional allowed constraints Task instances are used through out the system Descriptive properties Priority, documentation, source, location, resource allocation and usage Temporal properties Creation time, start time (adapted from iCalendar)

27 27 Ensuring Interoperability OWL does not allow n-ary relationships Task representation requires representing position of an argument in a list Needed to reify each argument so that the position could be specified OWL does not allow specialization of primitive data types Special kinds of strings such as Postal Code, Telephone Number are of interest Needed to define a hierarchy of ``pseudo ranges

28 28 Outline Problem KB Development Knowledge Engineering Challenges Deploying the Knowledge Base Future Work

29 29 Deploying the Knowledge Base Querying the Knowledge base Uniform point of access provided by a query manager Updating the knowledge base Methods to update the instance data if ontology changes Mechanism to propagate additions to the ontology at runtime

30 30 Documentation (via Owldoc)

31 31 Outline Problem KB Development Knowledge Engineering Challenges Deploying the Knowledge Base Future Work

32 32 Future Work Align Different OWL files Ontology to Help Stacked Learning A software engineer can solve a new learning problem by writing its specification in ontology An end-user can specify a goal, and CALO can compute how to learn to meet that goal CALO can infer a users goal and learn how to achieve that goal


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