So What Does it All Mean? Geospatial Semantics and Ontologies Dr Kristin Stock.

Slides:



Advertisements
Similar presentations
Three-Step Database Design
Advertisements

CS570 Artificial Intelligence Semantic Web & Ontology 2
IPY and Semantics Siri Jodha S. Khalsa Paul Cooper Peter Pulsifer Paul Overduin Eugeny Vyazilov Heather lane.
Knowledge Representation
Of 27 lecture 7: owl - introduction. of 27 ece 627, winter ‘132 OWL a glimpse OWL – Web Ontology Language describes classes, properties and relations.
Using the Semantic Web to Construct an Ontology- Based Repository for Software Patterns Scott Henninger Computer Science and Engineering University of.
Ontology Notes are from:
The Semantic Web Week 13 Module Website: Lecture: Knowledge Acquisition / Engineering Practical: Getting to know.
Use of Ontologies in the Life Sciences: BioPax Graciela Gonzalez, PhD (some slides adapted from presentations available at
COMP 6703 eScience Project Semantic Web for Museums Student : Lei Junran Client/Technical Supervisor : Tom Worthington Academic Supervisor : Peter Strazdins.
Description Logics. Outline Knowledge Representation Knowledge Representation Ontology Language Ontology Language Description Logics Description Logics.
Semantics For the Semantic Web: The Implicit, the Formal and The Powerful Amit Sheth, Cartic Ramakrishnan, Christopher Thomas CS751 Spring 2005 Presenter:
The Semantic Web Week 12 Term 1 Recap Lee McCluskey, room 2/07 Department of Computing And Mathematical Sciences Module Website:
From SHIQ and RDF to OWL: The Making of a Web Ontology Language
Semantic Web Technologies Lecture # 2 Faculty of Computer Science, IBA.
In The Name Of God. Jhaleh Narimisaei By Guide: Dr. Shadgar Implementation of Web Ontology and Semantic Application for Electronic Journal Citation System.
Chapter 4 System Models A description of the various models that can be used to specify software systems.
System models Abstract descriptions of systems whose requirements are being analysed Abstract descriptions of systems whose requirements are being analysed.
Knowledge representation
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.
Ontologies for the Integration of Geospatial Data Michael Lutz Workshop: Semantics and Ontologies for GI Services, 2006 Paper: Lutz et al., Overcoming.
OWL and SDD Dave Thau University of Kansas
RDF and OWL Developing Semantic Web Services by H. Peter Alesso and Craig F. Smith CMPT 455/826 - Week 6, Day Sept-Dec 2009 – w6d21.
Ming Fang 6/12/2009. Outlines  Classical logics  Introduction to DL  Syntax of DL  Semantics of DL  KR in DL  Reasoning in DL  Applications.
Building an Ontology of Semantic Web Techniques Utilizing RDF Schema and OWL 2.0 in Protégé 4.0 Presented by: Naveed Javed Nimat Umar Syed.
Metadata. Generally speaking, metadata are data and information that describe and model data and information For example, a database schema is the metadata.
Dimitrios Skoutas Alkis Simitsis
Chapter 7 System models.
ISBN Chapter 3 Describing Semantics -Attribute Grammars -Dynamic Semantics.
Software Engineering, 8th edition Chapter 8 1 Courtesy: ©Ian Somerville 2006 April 06 th, 2009 Lecture # 13 System models.
Umi Laili Yuhana December, Context Aware Group - Intelligent Agent Laboratory Computer Science and Information Engineering National Taiwan University.
Coastal Atlas Interoperability - Ontologies (Advanced topics that we did not get to in detail) Luis Bermudez Stephanie Watson Marine Metadata Interoperability.
Semantic Web - an introduction By Daniel Wu (danielwujr)
Advanced topics in software engineering (Semantic web)
Database Systems: Enhanced Entity-Relationship Modeling Dr. Taysir Hassan Abdel Hamid.
Semantic web course – Computer Engineering Department – Sharif Univ. of Technology – Fall Knowledge Representation Semantic Web - Fall 2005 Computer.
U.S. Department of the Interior U.S. Geological Survey A Consideration of Geospatial Feature Formation in Linked Open Vocabularies Workshop on Linked Open.
EEL 5937 Ontologies EEL 5937 Multi Agent Systems Lecture 5, Jan 23 th, 2003 Lotzi Bölöni.
Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture 13 of 41 Monday, 20 September.
SKOS. Ontologies Metadata –Resources marked-up with descriptions of their content. No good unless everyone speaks the same language; Terminologies –Provide.
Artificial Intelligence 2004 Ontology
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.
Issues in Ontology-based Information integration By Zhan Cui, Dean Jones and Paul O’Brien.
Knowledge Representation. Keywordsquick way for agents to locate potentially useful information Thesaurimore structured approach than keywords, arranging.
Ontologies for the Integration of Geospatial DataTU Wien, April 24-28, 2006 Ontologies for the Integration of Geospatial Data Michael Lutz Semantics and.
Presented by: Yuhana 12/17/2007 Context Aware Group - Intelligent Agent Laboratory Computer Science and Information Engineering National Taiwan University.
CS621 : Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 12 RDF, OWL, Minimax.
Working with XML. Markup Languages Text-based languages based on SGML Text-based languages based on SGML SGML = Standard Generalized Markup Language SGML.
A Portrait of the Semantic Web in Action Jeff Heflin and James Hendler IEEE Intelligent Systems December 6, 2010 Hyewon Lim.
Semantic Interoperability in GIS N. L. Sarda Suman Somavarapu.
Ontology Technology applied to Catalogues Paul Kopp.
Knowledge Engineering. Sources of Knowledge - Books - Journals - Manuals - Reports - Films - Databases - Pictures - Audio and Video Tapes - Flow Diagram.
Definition and Technologies Knowledge Representation.
16 April 2011 Alan, Edison, etc, Saturday.. Knowledge, Planning and Robotics 1.Knowledge 2.Types of knowledge 3.Representation of knowledge 4.Planning.
Knowledge Representation Part I Ontology Jan Pettersen Nytun Knowledge Representation Part I, JPN, UiA1.
OWL (Ontology Web Language and Applications) Maw-Sheng Horng Department of Mathematics and Information Education National Taipei University of Education.
The Semantic Web By: Maulik Parikh.
Information Organization
ece 720 intelligent web: ontology and beyond
ece 627 intelligent web: ontology and beyond
Lecture #11: Ontology Engineering Dr. Bhavani Thuraisingham
Web Ontology Language for Service (OWL-S)
Semantic Web - Ontologies
Ontology.
ece 720 intelligent web: ontology and beyond
Object-Oriented Knowledge Representation
COMPASS: A Geospatial Knowledge Infrastructure Managed with Ontologies
Presentation transcript:

So What Does it All Mean? Geospatial Semantics and Ontologies Dr Kristin Stock

Exercise Write a precise definition of: Write a precise definition of: A hill or A hill or A beach A beach (choose one). Write it as if to: Write it as if to: a person who has no prior knowledge or a person who has no prior knowledge or a computer. a computer. Must allow a person who doesn’t know the concept to determine whether something they encounter is an example of that concept. Must allow a person who doesn’t know the concept to determine whether something they encounter is an example of that concept.

Introduction Semantics are about describing and interpreting the meaning of geographic feature types. Semantics are about describing and interpreting the meaning of geographic feature types. Needed whenever data is to be shared or exchanged. Needed whenever data is to be shared or exchanged. Especially to do with helping computers to understand meaning. Especially to do with helping computers to understand meaning.

Example: data supply

Lecture Outline What are semantics used for? What are semantics used for? Why do semantics differ? Why do semantics differ? Methods for representing semantics. Methods for representing semantics. What are ontologies? What are ontologies? Reasoning. Reasoning. Current research. Current research.

What are semantics used for? allowing users unfamiliar with data to interpret and use it correctly; allowing users unfamiliar with data to interpret and use it correctly; automatically determining semantic similarity (which can be used in integration, translation, etc.); automatically determining semantic similarity (which can be used in integration, translation, etc.); querying (intelligent querying, NLP) and querying (intelligent querying, NLP) and Web Services. Web Services.

Why do semantics differ? A database is an abstraction of reality created by an individual using his or her cognitive model (world view). A database is an abstraction of reality created by an individual using his or her cognitive model (world view). Cognitive models differ depending on a number of characteristics, including education, experiences, theoretical assumptions, language. Cognitive models differ depending on a number of characteristics, including education, experiences, theoretical assumptions, language. Spatial data is used by a wide range of different people AND different cognitive models  databases with different semantics. Spatial data is used by a wide range of different people AND different cognitive models  databases with different semantics.

Methods for representing semantics (1) Methods based on existing information: Methods based on existing information: Schema characteristics. Schema characteristics.

Methods for representing semantics (2) Methods using additional representations: Methods using additional representations: Semantic networks and hierarchies (e.g. synonyms); Semantic networks and hierarchies (e.g. synonyms); Frames (from KR) - complex descriptions linked to create taxonomies; Frames (from KR) - complex descriptions linked to create taxonomies; Logic and Logic and Ontologies (combine semantic networks, frames and/or logic). Ontologies (combine semantic networks, frames and/or logic).

Semantic Networks Nodes are concepts/classes/nouns. Nodes are concepts/classes/nouns. Links between are relationships between concepts: Links between are relationships between concepts: is-a; is-a; part-of. part-of. Simple, understandable. Simple, understandable. Not semantically rich (no properties). Not semantically rich (no properties).

Example: Semantic networks water body ocean river creek is-a flows-to bay valley runs-through is-part-of beach is-in

Example: Wordnet

Frames A frame is a concept. A frame is a concept. Each frame has slots that are filled with values (properties or attributes). Each frame has slots that are filled with values (properties or attributes). Hierarchical relationships may be created between frames (using a kind-of slot). Hierarchical relationships may be created between frames (using a kind-of slot). Concept frames and instance frames. Concept frames and instance frames. Richer semantics, but not precisely defined. Richer semantics, but not precisely defined.

Example: Frames OCEAN sovereignty averageTemp WATERBODY name is-a RIVER length CREEK intermittent? waterQuality is-a

Logic A collection of propositions. A collection of propositions. Use predefined symbols: Use predefined symbols: AND AND OR OR THERE EXISTS THERE EXISTS FOR EVERY… FOR EVERY… Simple, understandable, reasoning mechanisms available Simple, understandable, reasoning mechanisms available Lacks structure Lacks structure

Example: Logic Town Town Rule = dimension [property] Rule = dimension [property] Property can be value, range, enumerated, predicate Property can be value, range, enumerated, predicate Predicate = R1 AND R8 AND R9 AND ( R101 OR R102 ) Predicate = R1 AND R8 AND R9 AND ( R101 OR R102 ) R1 = material [EARTH] R1 = material [EARTH] R8 = requirement [DEFINITION OF POSITION AND EXTENTS] R8 = requirement [DEFINITION OF POSITION AND EXTENTS] R9 = function [ADMINISTRATION] R9 = function [ADMINISTRATION] R101 = RR447 [R1 AND R8 AND R223 AND ( R183 OR R184 ) AND R226] R101 = RR447 [R1 AND R8 AND R223 AND ( R183 OR R184 ) AND R226] R102 = PR47 [R1 AND R8 AND R223 AND ( R183 OR R184 ) AND R226 R102 = PR47 [R1 AND R8 AND R223 AND ( R183 OR R184 ) AND R226

What are Ontologies? An ontology is a formal specification of a shared conceptualisation. An ontology is a formal specification of a shared conceptualisation. Based on the idea of a group or information community with a common world view. Based on the idea of a group or information community with a common world view. Formal specification = machine readable. Formal specification = machine readable. Can be: Can be: lightweight, including concepts, taxonomies, relationships between concepts and properties or lightweight, including concepts, taxonomies, relationships between concepts and properties or heavyweight, add axioms and constraints to clarify the meaning of concepts. heavyweight, add axioms and constraints to clarify the meaning of concepts.

Types of Ontology Language Logic. Logic. Frames. Frames. Networks. Networks. Description Logics (frame, network + logic). Description Logics (frame, network + logic). Use subsumption hierarchies Use subsumption hierarchies Including limited reasoning on those hierarchies. Including limited reasoning on those hierarchies. Tutorial/practical. Tutorial/practical.

Example: OWL Ontology

Reasoning Use semantics to perform automated reasoning. Use semantics to perform automated reasoning. Examples: Examples: If suburb in city and city in region, then suburb must be in region. If suburb in city and city in region, then suburb must be in region. If high density suburbs have high crime rates and suburb x is high density, then suburb x has a high crime rate. If high density suburbs have high crime rates and suburb x is high density, then suburb x has a high crime rate. If rivers flow to oceans, and the Trent River is a river, then the Trent River flows to the ocean. If rivers flow to oceans, and the Trent River is a river, then the Trent River flows to the ocean. Can be used for Can be used for Direct inference; Direct inference; Service chaining; Service chaining; Dynamic spatial analysis? Dynamic spatial analysis?

Spatial Semantics vs. the Semantics of Spatial Features: What is the Difference? 1. The semantics of geometries and spatial representations. 2. The semantics of features that have spatial representations.

What are semantics used for? allowing users unfamiliar with data to interpret and use it correctly; allowing users unfamiliar with data to interpret and use it correctly; automatically determining semantic similarity (which can be used in integration, translation, etc.); automatically determining semantic similarity (which can be used in integration, translation, etc.); querying (intelligent querying, NLP) and querying (intelligent querying, NLP) and Web Services. Web Services.

Future research directions Geo-ontology languages. Geo-ontology languages. Methods for determining semantics based on context. Methods for determining semantics based on context. Similarity assessment. Similarity assessment. Ontology learning. Ontology learning. Use of semantics with reasoning to automate processes and geospatial analysis. Use of semantics with reasoning to automate processes and geospatial analysis.

Tutorial (1) Download Protégé Full. load/release/full/ Download Protégé Full. load/release/full/ load/release/full/ load/release/full/ Copy from t drive to c drive Copy from t drive to c drive Double click to install – select ‘Everything’ when asked about the type of installation. Double click to install – select ‘Everything’ when asked about the type of installation. Start up the software. Start up the software. Create a new OWL Ontology. Create a new OWL Ontology.

Tutorial (2) Go through the tutorial at ode.org/resources/tutorials/ProtegeO WLTutorial.pdf Go through the tutorial at ode.org/resources/tutorials/ProtegeO WLTutorial.pdf ode.org/resources/tutorials/ProtegeO WLTutorial.pdf ode.org/resources/tutorials/ProtegeO WLTutorial.pdf Use a spatial domain of your choice. For example: Use a spatial domain of your choice. For example: Transportation networks; Transportation networks; Administrative areas; Administrative areas; Natural landscape features; Natural landscape features; Human settlement. Human settlement. Identify some hypothetical classes in the domain and build an ontology following the tutorial. Identify some hypothetical classes in the domain and build an ontology following the tutorial.

Notes In Section 4.3, Exercise 5, Step 2, use Tools > QuickOWL > Create Multiple Subclasses In Section 4.3, Exercise 5, Step 2, use Tools > QuickOWL > Create Multiple Subclasses Go to the end of Section 4.7 Go to the end of Section 4.7 Look at Code > Show RDF/XML Source Code to see the Actual OWL syntax. Look at Code > Show RDF/XML Source Code to see the Actual OWL syntax. Continue if you want to, but it is not required. Continue if you want to, but it is not required.