1. 1 Defining Vocabularies, Ontological and Linguistic: A Tool for Ontologizing the Ontolog Patrick Cassidy MITRE Corporation* Presented to the Ontolog Forum July 13, 2006 * NOTE: The authors affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITREs concurrence with, or support for, the positions, opinions or viewpoints expressed by the author.

2. 2 Outline A Common Upper Ontology is a Good Thing to have. The minimum upper ontology will represent the set of basic concepts sufficient to specify the meanings of all other more specialized concepts. A Linguistic Defining Vocabulary that parallels the Ontological Defining Vocabulary will make the Upper Ontology a lot easier to build, understand, and exploit. To help in ontologizing the Ontolog, we can use the Linguistic Defining Vocabulary right now.

3. 3 Problem Solving with Computers: Single Applications Interface Procedural Program Specification: Solve Current Problem. Report results Short-Term Task-specific Memory Persistent Stored Data

4. 4 Tightness of Coupling & Semantic Explicitness Implicit, TIGHT Explicit, Loose Local Far 1 System: Small Set of Developers Systems of Systems Enterprise Community Internet Looseness of Coupling Semantics Explicitness Data Same Address Space Same DBMS Federated DBs Data Warehouses Data Marts Workflow Ontologies Semantic Mappings XML, XML Schema Conceptual Models RDF/S, OWL Web Services: UDDI, WSDL OWL-S Modal Policies Application Same Process Space Same CPU Same OS Same Programming Language Same Local Area Network Same Wide Area Network Client-Server Same Intranet Compiling Linking Agent Programming Web Services: SOAP Distributed Systems OOP Applets Semantic Brokers Middleware Web Peer-to-peer N-Tier Architecture EAI From Synchronous Interaction to Asynchronous Communication Performance = k / Integration_Flexibility Source: Leo Obrst

5. 5 Problem Solving with Computers: Application Suite Interface Common Operating System for Stored data Access Short-Term Task-specific Memory Stored Data 1 Message Format and Protocol App 1App 2App 3 Stored Data 2

6. 6 Upper Ontology: Provides defining concepts to specify conceptual message Content Where Does The Upper Ontology Fit? NLP Understanding and generation Case-Based Reasoning Informtn. Retrieval Probabilistic Reasoning Spatial Reasoning Long-Term Knowledge Base: Ontology uses Upper Ontology for Concept Specifications Interfaces Robotics Expert Systems Task Control: Select Processes To Solve Current Problem. Report results Short-Term Memory: Ontology uses Upper Ontology

7. 7 Overlord: Situation Awareness Goal-Directed Action Selection Upper Ontology: Provides defining concepts to specify conceptual message Content Where Does The Upper Ontology Fit? NLP Understanding and generation Case-Based Reasoning Informtn. Retrieval Probabilistic Reasoning Spatial Reasoning EGO: Self Awareness Episodic Memory Knowledge Base: Ontology uses Upper Ontology for Concept Specifications Interfaces Robotics Expert Systems Task Control: Select Processes To Solve Current Problem. Report results

8. 8 Agents: Expectations & Norms Goal Stack Rules: (1)Laws (2)Community (3) User (4) Supervisor/Owner (5) Self-generated (6) Cultural Expectations Commitment: (1)Generate or Modify (2)Reference (3)Explain when asked Is Goal Possible Within Time Constraints? No. Report Impediments Yes. Request Overlord action Current Goal OL Add/Modify/Delete Goals

9. 9 Upper Ontology: Provides defining concepts to specify conceptual message Content Where Does The Upper Ontology Fit? NLP Understanding and generation Case-Based Reasoning Learning Probabilistic Reasoning Spatial Reasoning Long-Term Knowledge Base: Ontology uses Upper Ontology for Concept Specifications Interfaces Robotics Expert Systems Task Control: Select Processes To Solve Current Problem. Report results Short-Term Memory: Ontology uses Upper Ontology

10. 10 Is a Common Language Enough For Module Integration? Some doubt has been expressed: Merely encapsulating this machinery into modules that pass messages among each other does not allow for the internal operation of one algorithm to be influenced by another –Nicholas Cassamatis, A Cognitive Substrate for Achieving Human-Level Intelligence, AI Magazine, Summer 2006, pp. 45- 56. However, the use of the Upper Ontology not only permits effective message passing, but fuses all components into a unified knowledge base. The ontology has not only a message format, but logical inference which may be used by multiple modules.

11. 11 Upper Ontology: Provides defining concepts to specify conceptual message Content Modules May Have Tight Coupling NLP Understanding and generation Case-Based Reasoning Learning Probabilistic Reasoning Spatial Reasoning Long-Term Knowledge Base: Ontology uses Upper Ontology for Concept Specifications Interfaces Robotics Expert Systems Task Control: Select Processes To Solve Current Problem. Report results Short-Term Memory: Ontology uses Upper Ontology

12. 12 Upper Ontology: Provides defining concepts to specify conceptual message Content Where Does Upper Ontology Fit in Natural Language Understanding? ParsingDisambiguationLearning Entity Extraction Metaphoric Reasoning Knowledge Base: Ontology uses Upper Ontology for Concept Specifications Interfaces Word Experts NLP Understanding and generation TC

13. 13 What About Ontology Mapping Rather Than a Common UO? OK if: –Modest accuracy (10-80%) is acceptable –Very shallow reasoning (e.g. taxonomy only) is to be used

14. 14 First Order Logic? In a multimodule message/blackboard system, FOL is only one of potentially many reasoning mechanisms. Decidability and inference efficiency are not rate-limiting unless FOL alone is to be used on the full knowledge base.

15. 15 So, Whats the Problem with existing Upper Ontologies? It is time-consuming to learn how to use them effectively. –If an interface that uses language people already know can be developed, it will make upper ontologies easier to exploit. –Human language is easy for people to use. –A defining vocabulary can serve as the intermediate phase to improve usability.

16. 16 Outline A Common Upper Ontology is a Good Thing to have A Linguistic Defining Vocabulary that parallels the Ontological Defining Vocabulary will make the Upper Ontology a lot easier to build, understand, and exploit. To help in ontologizing the Ontolog, we can use the Linguistic Defining Vocabulary right now.

17. 17 What is a Defining Vocabulary? For lexicographers, a controlled list of words which are the only words allowed to be used in creating definitions (e.g. in LDOCE). –makes definitions easier to understand, especially for learners of a language For Ontologists, the set of basic concepts (and their ontological representations) which are sufficient to specify the meanings of any other concepts (or terms) by combinations of the basic concepts – a special type of Upper Ontology.

18. 18 How Big is the Defining Vocabulary? Longmans Dictionary of Contemporary English (LDOCE) uses about 2000 root words, some of which are used in more than one sense. With morphological variants, there are over 9000 words. For the conceptual defining vocabulary, probably at least 4000 senses will be needed. The vocabulary will probably grow over time.

19. 19 Is There a Relation Between the Linguistic Defining Vocabulary and The Conceptual Defining Vocabulary (Upper Ontology)? Hypothesis: yes, we should be able to use a linguistic controlled vocabulary like that of LDOCE and have definitions in that vocabulary translate directly and automatically into logical specifications using the conceptual inventory of the Upper Ontology.

20. 20 How Does this Differ from other Ontology Projects? By emphasizing the primary importance of developing the defining vocabulary – ontological and linguistic – and creating relations between them, before attempting representation of complex domain-specific concepts. The automatic conversion of linguistic to logical specifications is an essential element.

21. 21 So, This has no Immediate Real- World Application? Right! We do not already know the minimum set of conceptual components for representing everything; which basic concepts are required needs to be discovered by using a common upper ontology in generating other concepts.

22. 22 Is This Just Basic Research? NO! The need for information communication is immediate, and clear descriptions of information content can have immediate benefits. There are no negative side-effects to creating clear and comprehensible descriptions of information content.

23. 23 Specialists Will Want to Use Specialized Terms in Definitions The Controlled Defining Vocabulary is infinitely expandable. Probably, at least three levels will emerge: –the basic irreducible defining vocabulary –the general defining vocabulary, having terms which are defined by use of the basic vocabulary –specialized defining vocabularies, containing terms of interest to specific domains

24. 24 How Will We Know When We Have Succeeded in Building The Linguistic Conceptual Defining Vocabulary? When almost all new terms can be defined using intuitive linguistic phrases, and the words are already in the defining vocabulary.

25. 25 How Will We Know When We Have Succeeded in Building A Correct Mapping of Linguistic and Conceptual Defining Vocabularies? When people can enter and retrieve information of a basic nature using intuitive linguistic phrases. The most honest measure of correct representation is a correct answer to a straightforward question.

26. 26 Example Definitions from Longmans Raspberry –a soft sweet red berry, or the bush that this berry grows on Obligation –a moral or legal duty to do something Automobile -- a car Car –a vehicle with four wheels and an engine, that can carry a small number of passengers

27. 27 Language Logic Duty -- Longman: something that you have to do because it is morally or legally right More specific: –An action that an intelligent agent must perform or refrain from; the failure to perform or refrain from that action carries some undesirable consequence for that agent; the undesirable consequence may be enforced by the authority that assigned the duty.

28. 28 KIF Duty (=> (hasDuty ?AGENT ?DUTY)) (and (instance ?DUTY ActionOrInaction) (exists (?AUTHORITY ?CONSEQUENCE) (and (hasAttribute ?CONSEQUENCE (UndesirableFor ?AGENT)) (imposed ?AUTHORITY ?DUTY) (=> (not (performed ?AGENT ?DUTY)) (hasLiability ?AGENT (enforces ?AUTHORITY ?CONSEQUENCE)))))))

29. 29 Spatial Metaphors Orientation: Up, Down, Front, Back, Side Proximity: in Space, In time Basic shapes: compact, filament, sheet, multi-armed, network, hollow (container) Motion: origin, goal, path Edge: limit, obstacle, support Contact: Force, gravity, causing motion Body shape: Head, Foot, Arm Parts and structural relations

30. 30 What About Specialized Applications That Dont Need a High-Level Ontology? They can interoperate with other applications if they map the concepts they do use to the precisely defined concepts in the upper ontology. This principle can be applied transitively, through multiple levels of expressiveness.

31. 31 Definition Acceptance Hierarchy Executable Specification: Methods, Sequence, States Axiomatic Ontology: Quasi-2 nd Order, Function Terms OpenCyc SUMO DOLCE Restricted FOL: OWL Taxonomy/Thesaurus/Terminology accepts is used in

32. 32 What Can We Do Now? Begin immediately to define terms in Knowledge Organization Systems (ontologies, taxonomies, glossaries, etc.) using the basic English defining vocabulary. Add terms to the supplemental defining vocabulary as needed, with their definitions created from the basic terms Add terms to the community domain vocabularies, with their definitions created from the basic or supplemental defining vocabulary.

33. 33 Where is the Defining Vocabulary? A version that runs in Windows XP is on the ONTACWG web site, along with a Java utility to check definitions against the controlled vocabulary. http://colab.cim3.net/file/work/SICoP/ontac/reference/ControlledVocabulary/CheckCV.ZIP Unzip in a separate directory and run the.bat file to use the utility (Opening screen shot, next slide).

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35. 35 And if The English Logic Translation Project Lags? We will have terminologies and knowledge classifications with well-defined terms, understandable to anyone with a basic knowledge of English. –In itself, this result will be worth the effort.

36. 36 END