1. 1 Ontology Management in CALO, a Cognitive Assistant that Learns and Organizes Adam Cheyer Program Director, Cognitive Computing Group SRI International

2. 2 Abstract CALO is one of DARPA's most ambitious efforts to develop a persistent assistant that lives with, learns from, and supports users in managing the complexities of their daily work lives. A multi-year project that unites some 200+ researchers from 25 academic and commercial organizations, the goal is to produce a single system where learning happens "in vivo", inside an ever- evolving agent that can observe, comprehend, reason, anticipate, act, and communicate. In this talk, we will first provide an overview of CALO: the what, the how, the why. Next, we will discuss the engineering methods we use to develop and maintain the ontology of CALO. CALO has some unusual requirements, such as "Concept Learning" where the ontology is extended and modified "in-the-wild" by machine learning algorithms. Finally, we will demonstrate IRIS, a semantic desktop that serves as the office environment that integrates best with CALO. IRIS leverages many of CALO's techniques to ontology management, and being open source, provides a distributable, transparent example of the approach.

3. 3 Outline CALO Overview (separate presentation) Ontology Management in CALO Ontology Usage in CALOs Architecture CALOs Unique Issues (and Solutions Attempted) for Ontology Management and Maintenance In Practice Overview of IRIS Semantic Desktop Demonstration of CALO/IRIS

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 High-level CALO Architecture Task Registry Towel IRIS Office Environment

6. 6 Ontology in CALOs Architecture Query Manager Provides single entry point for querying knowledge in CALO – unifies many data sources and reasoning components Publish Subscribe Event Framework Across all cyber/physical events in CALO Episodic Memory (Timeline Server) Records instances of events for learning Task Interface Registry Engineered and Learned Actions in CALO Dialog Management Used for understanding user intent and generating interactions to user IRIS Office Environment Rich model of users electronic life MOKB Meeting Ontology KB Rich model of meeting events CALO Test Infrastructure (CATS) Evaluates CALOs abilities and how much learning in the wild has contributed High-level CALO Architecture

7. 7 CALO Ontology: Core+Office Core Ontology (aka CLIB) Created by UT Austin: http://www.cs.utexas.edu/users/mfkb/RKF/clib.htmlhttp://www.cs.utexas.edu/users/mfkb/RKF/clib.html Library of generic, composable and re-usable knowledge components. It was created before CALO and has been used in a variety of different projects including RKF, HALO and AURA. 857 core components (as of 2005-11-14) Ex: Time-Interval, Person, Organization, Message Office Ontology Extension of CORE suitable for CALO Office domain 108 office components (as of 2005-11-14) Ex: Author, Vendor, ProjectLeader, ElectronicPresentationDocument Implemented in KM (The Knowledge Machine) KM is a frame-based language with clear first-order logic semantics It contains sophisticated machinery for reasoning, including selection by description, unification, classification, and reasoning about actions using a situations mechanism http://www.cs.utexas.edu/users/mfkb/RKF/km.html

8. 8 CALOs Unique Ontology Management Issues Very large project, many different representation and inference needs 5 year project: Ontology will change. How to maintain consistency of code, data, and docs? Enduring Personal Cognitive Assistant: cant forget data. Concept & Task Learning: Ontology can change in the wild by the user and by CALO Uncertainty a reality, from many different reasoners and predictors

9. 9 Consistent Ontology Evolution KM vs. OWL (tools) Keeping Code, Data, and Doc in Synch Migrating acquired data instances forward through ontology changes from Engineering Releases Concept learning allows user ontologies to diverge How to rationalize with engineering releases? How to validate CALO- learned changes? KM (master) exports to OWL Documentation POJOs and Human Readable Doc Transactional POJOs SOUP: Simple Ontology Update Program applies system of patches to data to migrate forward to latest version Concept learning changes kept separate from main Engineering trunk Restrict changes allowed Add, rename properties and classes, Not move or delete Shadow ontology and validation processes IssuesSolutions Attempted

10. 10 Keeping Code, Data, and Docs in Synch Semantic Object CLIB Ontology (KM) Query APIs POJOs Java (RN) SPARQL Ontology Usage Spec Radar Networks KB JENA KB Lucene FullText Index KB1 KB2KB3 OWL Translator CLIB In OWL Query Manager BackEnd Front End Apps CALO UI UI Separation HTML Doc QM Domain File Data UI Event frmwk Action frmwk Plugin Svcs Cluster Framewk Classifier Other plugins CATS Tester MOKB Query Timeline TaskMgr IRIS Action To TaskMgr ILR Specialized OWL Ontologies OWLDoc

11. 11 CALO Concept Learning Concept Learning works in 2 steps / workflows 1. Building a Shadow Ontology/Knowledgebase Information harvesting Validation of harvested facts Integrated into a Shadow Ontology and Knowledgebase This is a longer term process and will be done first 2. Realtime updating of CALO Uses Shadow Ontology and KB CALO Queries CL about a concept CL returns one or more concepts CALO user verifies which was actually meant CALO Ontology and IRIS KB gets updated

12. 12 Uncertainty across multiple sources Issues When to write hypothesis as truth into KB? Maintaining consistency How to rationalize/combine hypotheses from different algorithms Credit assignment problem Solutions Attempted Year1: Global KB, some algorithms wrote, some hypotheses only accessible through APIs Year2: Provenance in global KB – record multiple solutions and where they came from Year3: Separate KBs by learning component, smart queries across sources Probablistic Consistency Engine maintains global what CALO believes repository

13. 13 IRIS: Integrate. Relate. Infer. Share. Real office applications (Mozilla, GLOW, Jabber, …) Plug-in Architecture (180+ plugins: UI, KB, NL, learning, apps, …) Semantic Object layer: JAVA objects on top of OWL Full-text & relational query (SPARQL) Ontology-based event and action framework Machine learning framework: classification, extraction, clustering, ranking, … LGPL Open Source http://www.openiris.org http://www.openiris.org Only small subset of CALO, but should be useful for many applications and uses many of techniques in this presentation IRIS Semantic Desktop

14. 14 Questions? Adam Cheyer