Solving inconsistent ontologies with heuristics Joey Lam, Derek Sleeman, Wamberto Vasconcelos 23 Jan 2006.

Slides:

Advertisements

Similar presentations

Inquiry-Based Instruction

Advertisements

Ontology Assessment – Proposed Framework and Methodology.

Reconstructing Ontologies from Fragments Derek Sleeman, Sik Chun (Joey) Lam, Wamberto Vasconcelos 2 Oct 2005.

The Cost of Authoring with a Knowledge Layer Judy Kay and Lichao Li School of Information Technologies The University of Sydney, Australia.

Scientific Enquiry, Scientific Process or Problem Solving?

Department of Mathematics and Science

Ontological Logic Programming by Murat Sensoy, Geeth de Mel, Wamberto Vasconcelos and Timothy J. Norman Computing Science, University of Aberdeen, UK 1.

Marzano Training May 24, 2013.

Chapter 11 Artificial Intelligence and Expert Systems.

The USE Project: Usability Evaluation and Software Design: Bridging the Gap University of Copenhagen Aalborg University Use Case Evaluation (UCE): A Method.

Domain-Specific Software Engineering (DSSE). Software Engineering Concerns  There are many of them  “Classical” software architecture research has focused.

Knowledge Acquisitioning. Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program.

EXPERT SYSTEMS Part I.

Chapter Three THE RESEARCH PROCESS

Assignment 2 Case Study. Criteria Weightage - 60 % Due Date – 11 th October 2012 Length of Analysis – 2500 words Leverage % including appendices,

Scientific Investigation vs. Technological Design

Chapter 6 Using Indirect Teaching Methods. The Discussion Method w Classroom goals: Questions that explore controversial issues (with no simple answer)

The six-step problem-solving process

Thesis Report Chapter 1 (Introduction)

Reasoning the FMA Ontologies with TrOWL Jeff Z. Pan, Yuan Ren, Nophadol Jekjantuk, and Jhonatan Garcia University of Aberdeen, UK ORE2013.

WRITTEN ABILITY TEST(WAT) GUIDE An initiative of MPRC, IIM Kashipur.

5 E’s Lesson Model.

11 C H A P T E R Artificial Intelligence and Expert Systems.

Scientific Inquiry Mr. Wai-Pan Chan Scientific Inquiry Research & Exploratory Investigation Scientific inquiry is a way to investigate things, events.

The student will demonstrate an understanding of how scientific inquiry and technological design, including mathematical analysis, can be used appropriately.

Ms.Mehwish Rafi The City School Darakhshan Campus Mathematics, Grade 3

The 5 E’s Science Lesson Inquiry-Based Instruction.

INTERACTIVE ANALYSIS OF COMPUTER CRIMES PRESENTED FOR CS-689 ON 10/12/2000 BY NAGAKALYANA ESKALA.

Web Usage Mining for Semantic Web Personalization جینی شیره شعاعی زهرا.

Benchmarking Methodology. Sep 27th, 2004 © R. García-Castro, A. Gómez-Pérez Raúl García-Castro Asunción Gómez-Pérez September 27th, 2004 Benchmarking.

Ontology Evolution and Regression Analysis Insights into Ontology Regression Testing Maria Copeland Rafael Goncalvez Robert Stevens Bijan Parsia Uli Sattler.

Project Report. Suggested TOC Executive Summary Project Background and Assumptions Vision and Mission Statements Objectives SWOT Analysis Recommended.

Finnish-Russian Doctoral Seminar on Multicriteria Decision Aid and Optimization Iryna Yevseyeva Niilo Mäki Institute University of Jyväskylä, Finland

Welcome Science 5 and Science 6 Implementation Workshop.

Inquiry Refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.

Approaches to Translation Ju Miao Nankai University.

AS Biology Completing your your coursework. Planning  This has already been completed.  Must be a clearly defined section of your final report – write.

Research Methodology For AEP Assoc. Prof. Dr. Nguyen Thi Tuyet Mai HÀ NỘI 12/2015.

Investment decision making

THE SCIENTIFIC METHOD. THE SCIENTIFIC METHOD: is a process used to find answers to questions about the world around us is an organized series of steps.

CpSc 810: Machine Learning Analytical learning. 2 Copy Right Notice Most slides in this presentation are adopted from slides of text book and various.

Ontology domain & modeling extensions. Modeling enhancements: overview Enhancements: – Increased expressivity in ontology – Increased expressivity in.

Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

EVALUATION AND SELFASSESSMENT SYSTEMS FOR THE VIRTUAL TEACHING: MULTIPLE CHOICE QUESTIONNAIRES Claudio Cameselle, Susana Gouveia Departamento de Enxeñería.

Prepared By: Razif Razali 1 TMK 264: COMPUTER SECURITY CHAPTER SIX : ADMINISTERING SECURITY.

Introduction to research

What Is Science?. 1. Science is limited to studying only the natural world. 2. The natural world are those phenomena that can be investigated, discovered,

1 Ontology Evolution within Ontology Editors Presentation at EKAW, Sigüenza, October 2002 L. Stojanovic, B. Motik FZI Research Center for Information Technologies.

Copyright © 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 1 Research: An Overview.

© 2006 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Chapter 1 Introduction to Research in Communication Research: –Process of asking questions.

Understanding Problem Solving. Next Generation Science/Common Core Standards Addressed! CCSS.Math.Content.8.EE.B.5 Graph proportional relationships, interpreting.

Research Report Writing Presentation

WonderWeb. Ontology Infrastructure for the Semantic Web. IST WP4: Ontology Engineering Heiner Stuckenschmidt, Michel Klein Vrije Universiteit.

© 2001 Laura Snodgrass, Ph.D.1 Experimental Psychology Introduction.

A focus group is actually gathering of people who are customers or users representatives for a product to gain its feedback. The feedback can be collected.

Design for a generic knowledge base for autonomic QoE optimization in multimedia access networks September 9, 2008 Bong-Kyun Lee Dept. of Information and.

Investigate Plan Design Create Evaluate (Test it to objective evaluation at each stage of the design cycle) state – describe - explain the problem some.

Introduction Ms. Binns.  Distinguish between qualitative and quantitative data  Explain strengths and limitations of a qualitative approach to research.

Distinguish between an experiment and other types of scientific investigations where variables are not controlled,

Chapter 1 An Introduction to Assurance and Financial Statement Auditing.

Leacock, Warrican and Rose (2009)

<Student’s name>

Architecture Components

THE BUSINESS ANALYSIS PROCESS MODEL

Ontology Evolution: A Methodological Overview

Scientific Inquiry Standard B – 1.7.

Evaluation Report Investigates a problem or a set of options.

Test-Driven Ontology Development in Protégé

Kasper Hornbæk Department of Computer Science University of Copenhagen

Scientific Inquiry Standards B – 1.7 and B – 1.8.

Presentation transcript:

Solving inconsistent ontologies with heuristics Joey Lam, Derek Sleeman, Wamberto Vasconcelos 23 Jan 2006

Overview Motivation Heuristic Approach Basic Heuristic General Heuristic Individual Heuristic Ranking solution Conclusion and Future Work

Motivation Existing ontology editors Detect the inconsistencies Indicate the inconsistent concepts Pinpoint the problematic axioms No functionalities are provided for users to correct the problems.

A Heuristic Approach Provide optimal ways for users to correct the detected inconsistencies using a heuristic approach Three types of heuristics: Basic – common errors made by humans General – heuristics made by ontology engineers Individual – heuristics used by a specific user when working on a particular ontology

Related Work OntoTrack – provides on-demand explanation of subsumption between classes Protégé-OWL (a) tracks down the reasons for inconsistencies in OWL classes (b) explains the reasons for unsatisfiable OWL classes SWOOP (a) pinpoints the problematic axioms (b) distinguishes root from derived unsatisfiable classes by detecting their dependencies Summary: focus on the explanations of unsatisfiability, but none of the work supports correcting the inconsistency.

Make use of the common errors made by humans to generate a set of heuristics For example: Asserted: C1 C2 Meant: C1 C2 Reason: Misunderstanding different between primitive and defined classes Suggest the meant axiom to the user as a replacement Basic Heuristics

General Heuristic Conduct an empirical study to identify the strategies used by ontology users when facing inconsistent ontologies What changes are needed with reasons Why not other options Adopt the think-aloud protocol elicitation Record and analyse the verbal reports produced by the subjects

Individual Heuristic Individual users may have different perspectives on a problem depending on their knowledge backgrounds. Support personalised repairing strategies Provide individual options by making use of past behaviors to predict future behaviors.

Ranking Solutions Rank the solutions based on the degree of confidence Evaluate the cost and benefits of the heuristics Cost: the number of operations required to implement it Benefit: the number of inconsistent and consistent entities resulted by a heuristic

Conclusion and Future Work Goal: recommend optimal solutions using a heuristic approach Future work: (a) Investigate the set of heuristics from different sources (b) Optimise and evaluate the proposed solutions