University of Toronto Michael Gruninger University of Toronto, Canada Leo Obrst MITRE, McLean, VA, USA February 6, 2014February 6, 2014February 6, 2014.

Slides:



Advertisements
Similar presentations
Chapter 7 System Models.
Advertisements

Ontology Assessment – Proposed Framework and Methodology
Dr. Leo Obrst MITRE Information Semantics Information Discovery & Understanding Command & Control Center February 6, 2014February 6, 2014February 6, 2014.
Dr. Leo Obrst Information Semantics Command & Control Center July 17, 2007 Ontologies Can't Help Records Management Or Can They?
Ontology Assessment – Proposed Framework and Methodology.
Database Design: ER Modelling (Continued)
The 20th International Conference on Software Engineering and Knowledge Engineering (SEKE2008) Department of Electrical and Computer Engineering
IPY and Semantics Siri Jodha S. Khalsa Paul Cooper Peter Pulsifer Paul Overduin Eugeny Vyazilov Heather lane.
Copyright Irwin/McGraw-Hill Data Modeling Prepared by Kevin C. Dittman for Systems Analysis & Design Methods 4ed by J. L. Whitten & L. D. Bentley.
So What Does it All Mean? Geospatial Semantics and Ontologies Dr Kristin Stock.
Ontology From Wikipedia, the free encyclopedia In philosophy, ontology (from the Greek oν, genitive oντος: of being (part. of εiναι: to be) and –λογία:
Of 27 lecture 7: owl - introduction. of 27 ece 627, winter ‘132 OWL a glimpse OWL – Web Ontology Language describes classes, properties and relations.
Ontology… A domain ontology seeks to reduce or eliminate conceptual and terminological confusion among the members of a user community who need to share.
Using the Semantic Web to Construct an Ontology- Based Repository for Software Patterns Scott Henninger Computer Science and Engineering University of.
A Framework for Ontology-Based Knowledge Management System
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 8 The Enhanced Entity- Relationship (EER) Model.
Four Dark Corners of Requirements Engineering
Creating Architectural Descriptions. Outline Standardizing architectural descriptions: The IEEE has published, “Recommended Practice for Architectural.
Formal Ontology and Information Systems Nicola Guarino (FOIS’98) Presenter: Yihong Ding CS652 Spring 2004.
Describing Syntax and Semantics
University of Toronto Michael Gruninger University of Toronto, Canada Leo Obrst MITRE, McLean, VA, USA July 15, 2015July 15, 2015July 15, 2015 Ontology.
Semantic Web Technologies Lecture # 2 Faculty of Computer Science, IBA.
MDC Open Information Model West Virginia University CS486 Presentation Feb 18, 2000 Lijian Liu (OIM:
RDF (Resource Description Framework) Why?. XML XML is a metalanguage that allows users to define markup XML separates content and structure from formatting.
Ontology Development Kenneth Baclawski Northeastern University Harvard Medical School.
Knowledge representation
Protege OWL Plugin Short Tutorial. OWL Usage The world wide web is a natural application area of ontologies, because ontologies could be used to describe.
Of 39 lecture 2: ontology - basics. of 39 ontology a branch of metaphysics relating to the nature and relations of being a particular theory about the.
INF 384 C, Spring 2009 Ontologies Knowledge representation to support computer reasoning.
School of Computing FACULTY OF ENGINEERING Developing a methodology for building small scale domain ontologies: HISO case study Ilaria Corda PhD student.
Ontology Summit2007 Survey Response Analysis -- Issues Ken Baclawski Northeastern University.
Of 33 lecture 10: ontology – evolution. of 33 ece 720, winter ‘122 ontology evolution introduction - ontologies enable knowledge to be made explicit and.
Metadata. Generally speaking, metadata are data and information that describe and model data and information For example, a database schema is the metadata.
LOGIC AND ONTOLOGY Both logic and ontology are important areas of philosophy covering large, diverse, and active research projects. These two areas overlap.
Sommerville 2004,Mejia-Alvarez 2009Software Engineering, 7th edition. Chapter 8 Slide 1 System models.
Semantic Web - an introduction By Daniel Wu (danielwujr)
Advanced topics in software engineering (Semantic web)
©Ferenc Vajda 1 Semantic Grid Ferenc Vajda Computer and Automation Research Institute Hungarian Academy of Sciences.
Semantic web course – Computer Engineering Department – Sharif Univ. of Technology – Fall Knowledge Representation Semantic Web - Fall 2005 Computer.
A Context Model based on Ontological Languages: a Proposal for Information Visualization School of Informatics Castilla-La Mancha University Ramón Hervás.
EEL 5937 Ontologies EEL 5937 Multi Agent Systems Lecture 5, Jan 23 th, 2003 Lotzi Bölöni.
10/24/09CK The Open Ontology Repository Initiative: Requirements and Research Challenges Ken Baclawski Todd Schneider.
SKOS. Ontologies Metadata –Resources marked-up with descriptions of their content. No good unless everyone speaks the same language; Terminologies –Provide.
Trustworthy Semantic Webs Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #4 Vision for Semantic Web.
Metadata Common Vocabulary a journey from a glossary to an ontology of statistical metadata, and back Sérgio Bacelar
Of 33 lecture 1: introduction. of 33 the semantic web vision today’s web (1) web content – for human consumption (no structural information) people search.
Topic Maps introduction Peter-Paul Kruijsen CTO, Morpheus software ISOC seminar, april 5 th 2005.
Some Thoughts to Consider 8 How difficult is it to get a group of people, or a group of companies, or a group of nations to agree on a particular ontology?
Knowledge Representation. Keywordsquick way for agents to locate potentially useful information Thesaurimore structured approach than keywords, arranging.
Achieving Semantic Interoperability at the World Bank Designing the Information Architecture and Programmatically Processing Information Denise Bedford.
A Portrait of the Semantic Web in Action Jeff Heflin and James Hendler IEEE Intelligent Systems December 6, 2010 Hyewon Lim.
Modeling Security-Relevant Data Semantics Xue Ying Chen Department of Computer Science.
Chapter 7 K NOWLEDGE R EPRESENTATION, O NTOLOGICAL E NGINEERING, AND T OPIC M APS L EO O BRST AND H OWARD L IU.
Enable Semantic Interoperability for Decision Support and Risk Management Presented by Dr. David Li Key Contributors: Dr. Ruixin Yang and Dr. John Qu.
International Workshop 28 Jan – 2 Feb 2011 Phoenix, AZ, USA Ontology in Model-Based Systems Engineering Henson Graves 29 January 2011.
Introduction: Databases and Database Systems Lecture # 1 June 19,2012 National University of Computer and Emerging Sciences.
Ontologies COMP6028 Semantic Web Technologies Dr Nicholas Gibbins
Semantic Web. P2 Introduction Information management facilities not keeping pace with the capacity of our information storage. –Information Overload –haphazardly.
Knowledge Representation Part I Ontology Jan Pettersen Nytun Knowledge Representation Part I, JPN, UiA1.
COP Introduction to Database Structures
The Enhanced Entity- Relationship (EER) Model
The Semantic Web By: Maulik Parikh.
COMP6215 Semantic Web Technologies
Ontology Framework: Results of Ontology Summit 2007
ece 627 intelligent web: ontology and beyond
Ontology From Wikipedia, the free encyclopedia
ece 720 intelligent web: ontology and beyond
ece 627 intelligent web: ontology and beyond
Ontology-Based Approaches to Data Integration
Chapter 5 Advanced Data Modeling
Presentation transcript:

University of Toronto Michael Gruninger University of Toronto, Canada Leo Obrst MITRE, McLean, VA, USA February 6, 2014February 6, 2014February 6, 2014 Ontology Summit: An Ontology Framework NIST, Gaithersburg, MD April 22-23, 2007

2 Agenda A Brief Inclusive Characterization of Ontologies A Common Set of Characteristics for Ontologies Ontology Dimensions: –Semantic Degree of Structure and Formality Expressiveness of the Knowledge Representation Language Representational granularity –Pragmatic Intended Use Role of Automated Reasoning Descriptive vs. Prescriptive Design Methodology Conclusions

3 A Brief Inclusive Characterization of Ontologies (in Computer Science & Engineering) Ontologies are used to support sharable and reusable representations of knowledge –An early definition of Ontology: a specification of a conceptualization (Gruber, 1994) Nevertheless, the sheer range of current work in ontologies: –Including taxonomies, thesauri, topic maps, conceptual models, and formal ontologies specified in various logical languages –Raises the possibility of ontologies being developed without a common understanding of their definition, implementation and applications Our objective: –To provide a framework that ensures that we can support diversity without divergence –So that we can maintain sharability and reusability among the different approaches to ontologies

4 A Common Set of Characteristics for Ontologies An ontology includes: –A vocabulary together with a specification of –The meanings of the terms in the vocabulary This specification includes: –Identification of the fundamental categories in the domain –Identification of the ways in which members of the categories are related to each other –Constraining the ways in which the relationships can be used

5 Ontology Dimensions: Semantic & Pragmatic We propose a set of dimensions that can be used to distinguish among different approaches –Semantic –Pragmatic Semantic Dimensions: These constrain how a given approach specifies the meaning of the terms 1)Degree of Structure and Formality 2)Expressiveness of the Knowledge Representation Language 3)Representational granularity Pragmatic Dimensions: These cover the context in which the ontology is designed and used 1)Intended Use 2)Role of Automated Reasoning 3)Descriptive vs. Prescriptive 4)Design Methodology

6 Ontology Semantic Dimension: (1) Degree of Formality (Structure) Related to but not the same as the expressive power required of a representation language used to specify the ontology Informal: An ontology can be specified in English or some other natural language in a document –This is an informal ontology, although it can be rich, unambiguous, precise More Formal: A taxonomy can be term or concept based –Term-based: A topic hierarchy from more general terms at the top of the hierarchy to more specific terms as one descends through the hierarchy; –Concept-based: A hierarchy of classes in which the necessary and distinguishing properties of classes and their subclasses are represented –But, even if formalized, these models are very simply structured, i.e., structured with a subsumption relation (narrower_than or subclass) Very Formal: An ontology for engineering equations –Would specify the formal semantics of its terms (such as quantity and unit of measure) in a language enabling precision & unambiguous expression Degree of Structure: that by which the vocabulary in an ontology is constrained and can support computation

7 Ontology Semantic Dimension: (2) Expressiveness of the Knowledge Representation Language This is a dimension partially dependent on the first –Since an informal ontology will be expressed only as a list of terms or enumerated definitions in a natural language such as English Although not a characteristic in itself for a given content, the language or framework that the content is modeled in: –Greatly affects, and in fact –Is the primary constraint on what can be expressed by the content Predicates and individuals/instances, for example cannot be expressed in propositional logic Many of the notions termed ontology cannot be expressed formally Similarly, relationships besides subclass or narrower-than cannot be expressed in a strict or strong (i.e., at least partially formalized) taxonomy, although they can be in a less strict (informal) taxonomy In general, only if the KR language is logic-based will the ontology being machine-interpretable

8 Ontology Semantic Dimension: (3) Representational Granularity: An ontology may contain terms and limited inter- relationship representation –Example: a simple taxonomy –Example: a very formal ontology expressed in Common Logic but which contains only 3 classes and 2 properties Or it may contain much more detail including many restrictions concerning how terms can relate to each other –Example: a very detailed English description of biological classes and discriminating properties –Example: a very formal ontology expressed in KIF, CL, Cyc-L, or OWL+SWRL which contains thousands of classes, thousands of properties, thousands of rules, and billions of instances/individuals Some quantifiable metrics may give indications of the representational granularity: –Examples: average subclass/subproperty depth, average density/bushiness, number of axioms, etc.

9 Ontology Pragmatic Dimension: (1) Intended Use, Application Focus The intended use may be: To share knowledge bases To enable communication among software agents, To help integrate disparate data sets To represent a natural language vocabulary To help provide knowledge-enhanced search To provide a starting-point for building knowledge systems To provide a conceptual framework for indexing content, etc. The intended use often means that: Typically, there is some application that is envisioned for which the ontology is being developed Categorization: one might want to situate documents within a framing topic taxonomy that roughly characterizes the primary content of the document and helps one semantically loosely organize document collections Search Enhancement: one might want to use a thesaurus, its synonyms and narrower-than terms, to enhance a search engine that can employ query term expansion, expanding the users text search terms to include synonyms or more specific terms and thus increasing the recall (i.e., total set of relevant items) of retrieved documents Enterprise Modeling, Question-Answering, Semantic Web Service Discovery Semantic Search: one might to use concept based rather than term based search in specific domains or across domains Complex Decision-Making, Intelligence Analysis: requiring machine reasoning

10 Ontology Pragmatic Dimension: (2) Role of Automated Reasoning Automated reasoning can range from simple to complex Simple automated reasoning can mean: Machine semantic interpretability of the content, which only requires that the language that the content is modeled in is a logic This is a principled or standards-based approach Or it can mean that a special interpreter/inference engine has been constructed that knows how to interpret the content This is an ad hoc and often proprietary approach

11 Ontology Pragmatic Dimension: (2) Role of Automated Reasoning (contd) Simple automated reasoning: The machine may be able to make inferences using the subclass relation by which properties defined at the parent class are inherited down to the children classes; this is the property of transitivity More complex reasoning: The machine may be able to take an arbitrary assertion in the KR language and classify it with respect to the taxonomic backbone of the ontology; e.g., description logics perform classificational reasoning Very complex automated reasoning: The use of deductive rules, i.e., inference rules or expressions that combine information from across the ontology These characterize dependencies much like if-then-else statements in programming languages Business rules that try to characterize things that have to hold in an enterprise but which cant typically be expressed in relational databases or object models A logic-based KR language needed: validly concludes or X is consistent with Y are not expressible generally in ad hoc implementations Theorem-proving, theorem-generation, etc.

12 Ontology Pragmatic Dimension: (3) Descriptive vs. Prescriptive Descriptive: Does the content describe, i.e., characterize the entities and their relationships as a user or an expert might characterize those objects? Descriptive often takes a looser notion of characterization, perhaps allowing arbitrary objects into the model, which might not exist in the real world but which are significant conceptual items for the given user community Potential partial synonym: Multiplicative, i.e., concepts can include anything that reality seems to require or any distinction that is useful to make Prescriptive: Does the content prescribe, i.e., mandate the way that those entities and their relationships are characterized? Prescriptive often takes a stricter notion of characterization, stating that only objects which actually exist or that represent natural kinds or types of things in the real world should be represented in the content of the engineering model Potential partial synonym: Reductionist, i.e., concepts are reduced to the fewest primitives from which it is possible to generate complex reality

13 Ontology Pragmatic Dimension: (4) Design Methodology Was there a methodology employed in the construction of the ontology? Possible ranges of methodology include: Bottom-up: A bottom-up (sometimes called empirical) methodology places strong emphasis on: Either solely analyzing the data sources so that the resulting ontology covers their semantics Or enabling arbitrary persons to characterize their content as they personally see fit, using terminology or metadata and whatever structuring relations (or not) that they desire to use, with perhaps an auxiliary notion or assumption that in by doing so, patterns of characterizations may emerge or be preferred by a large group or community of persons Can be conceptually profligate, tolerable of redundancy and partial overlap Top-down: A top-down (sometimes called rationalist) methodology places strong emphasis on: Developing the ontology using known notions about the world or domain Independent of existing data sources whose semantics will be covered by the resulting ontology Considering a range of questions that a domain expert might want to ask about the domain Typically preferring a rigorous methodology focused on consistency, parsimony, etc.

14 Other Dimensions? Semantic Focus (originally: Concept-based?): –Term (0) – Concept (1) – Real World Referent (3) –Example: thesauri & some taxonomies: Term –Example: some conceptual models, some taxonomies, some ontologies: Concept –Example: some ontologies: RW Referent Precision: subsumed by Granularity? Scope: subsumed by composition of Application Foci? Human-coded vs. Machine-generated Range of values for the Ontology Types? –Scale? –Description only? –Both?

15 Conclusions We want to avoid positing definition, allow definition to emerge The framework is intended to offer dimensions for comparison –How our ontologies are alike, how they are different but comparable For too long we have been embroiled in endless argumentation Lets cooperate, find correspondences, figure out how we can semantically interoperate Well have a number of presentations & discussions to help bring the communities together Goal: Converge!