Maurice Hermans.  Ontologies  Ontology Mapping  Research Question  String Similarities  Winkler Extension  Proposed Extension  Evaluation  Results.

Slides:



Advertisements
Similar presentations
Hoai-Viet To1, Ryutaro Ichise2, and Hoai-Bac Le1
Advertisements

Angelo Augusto Frozza, Ronaldo dos Santos Mello {frozza, A Method for Defining Semantic Similarities between GML Schemas Angelo Augusto.
Using XSLT for Interoperability: DOE and The Traveling Domain Experiment Monday 20 th of October, 2003 Antoine Isaac, Raphaël Troncy and Véronique Malaisé.
S-Match: an Algorithm and an Implementation of Semantic Matching Pavel Shvaiko 1 st European Semantic Web Symposium, 11 May 2004, Crete, Greece paper with.
Improved TF-IDF Ranker
Leveraging Data and Structure in Ontology Integration Octavian Udrea 1 Lise Getoor 1 Renée J. Miller 2 1 University of Maryland College Park 2 University.
A Linguistic Approach for Semantic Web Service Discovery International Symposium on Management Intelligent Systems 2012 (IS-MiS 2012) July 13, 2012 Jordy.
Project 2 Ontology alignment. SIGNAL-ONTOLOGY (SigO) Immune Response i- Allergic Response i- Antigen Processing and Presentation i- B Cell Activation.
Matching Systems ● SAMBO ● Falcon ● DSSim ● RiMOM ● ASMOV ● Anchor-Flood ● AgreementMaker.
1 Ontology Based Extraction of RDF Data from the World Wide Web Tim Chartrand A Thesis Proposal Research Supported By NSF.
A Framework for Ontology-Based Knowledge Management System
1 CIS607, Fall 2004 Semantic Information Integration Presentation by Julian Catchen Week 3 (Oct. 13)
J. Turmo, 2006 Adaptive Information Extraction Summary Information Extraction Systems Multilinguality Introduction Language guessers Machine Translators.
TOSS: An Extension of TAX with Ontologies and Similarity Queries Edward Hung, Yu Deng, V.S. Subrahmanian Department of Computer Science University of Maryland,
Information retrieval Finding relevant data using irrelevant keys Example: database of photographic images sorted by number, date. DBMS: Well structured.
Article by: Feiyu Xu, Daniela Kurz, Jakub Piskorski, Sven Schmeier Article Summary by Mark Vickers.
Multi-Concept Alignment and Evaluation Shenghui Wang, Antoine Isaac, Lourens van der Meij, Stefan Schlobach Ontology Matching Workshop Oct. 11 th, 2007.
1 Ontology Based Extraction of RDF Data from the World Wide Web Tim Chartrand Masters Thesis Research Supported By NSF.
11/8/20051 Ontology Translation on the Semantic Web D. Dou, D. McDermott, P. Qi Computer Science, Yale University Presented by Z. Chen CIS 607 SII, Week.
QoM: Qualitative and Quantitative Measure of Schema Matching Naiyana Tansalarak and Kajal T. Claypool (Kajal Claypool - presenter) University of Massachusetts,
Ontology translation: two approaches Xiangkui Yao OntoMorph: A Translation System for Symbolic Knowledge By: Hans Chalupsky Ontology Translation on the.
DVMM Lab, Columbia UniversityVideo Event Recognition Video Event Recognition: Multilevel Pyramid Matching Dong Xu and Shih-Fu Chang Digital Video and Multimedia.
Learning Table Extraction from Examples Ashwin Tengli, Yiming Yang and Nian Li Ma School of Computer Science Carnegie Mellon University Coling 04.
Comparing ontological concepts based on recursive traversing of the ontology structure Anton Andrejko.
OMAP: An Implemented Framework for Automatically Aligning OWL Ontologies SWAP, December, 2005 Raphaël Troncy, Umberto Straccia ISTI-CNR
Copyright © 2010 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Multiple Ontologies in.
Semantic Interoperability Jérôme Euzenat INRIA & LIG France Natasha Noy Stanford University USA.
Ontology Matching Basics Ontology Matching by Jerome Euzenat and Pavel Shvaiko Parts I and II 11/6/2012Ontology Matching Basics - PL, CS 6521.
Erasmus University Rotterdam Introduction Nowadays, emerging news on economic events such as acquisitions has a substantial impact on the financial markets.
Erasmus University Rotterdam Introduction With the vast amount of information available on the Web, there is an increasing need to structure Web data in.
An Integrated Approach to Extracting Ontological Structures from Folksonomies Huairen Lin, Joseph Davis, Ying Zhou ESWC 2009 Hyewon Lim October 9 th, 2009.
BACKGROUND KNOWLEDGE IN ONTOLOGY MATCHING Pavel Shvaiko joint work with Fausto Giunchiglia and Mikalai Yatskevich INFINT 2007 Bertinoro Workshop on Information.
Machine Learning Approach for Ontology Mapping using Multiple Concept Similarity Measures IEEE/ACIS International Conference on Computer and Information.
12th of October, 2006KEG seminar1 Combining Ontology Mapping Methods Using Bayesian Networks Ontology Alignment Evaluation Initiative 'Conference'
PART IV: REPRESENTING, EXPLAINING, AND PROCESSING ALIGNMENTS & PART V: CONCLUSIONS Ontology Matching Jerome Euzenat and Pavel Shvaiko.
Based on “Semi-Supervised Semantic Role Labeling via Structural Alignment” by Furstenau and Lapata, 2011 Advisors: Prof. Michael Elhadad and Mr. Avi Hayoun.
Using String Similarity Metrics for Terminology Recognition Jonathan Butters March 2008 LREC 2008 – Marrakech, Morocco.
Interoperable Visualization Framework towards enhancing mapping and integration of official statistics Haitham Zeidan Palestinian Central.
The KOS interoperability in aquatic science field through mapping processes Carmen Reverté Reverté Aquatic Ecosystems Documentation Center. IRTA. (Sant.
Towards Distributed Information Retrieval in the Semantic Web: Query Reformulation Using the Framework Wednesday 14 th of June, 2006.
Cluster-specific Named Entity Transliteration Fei Huang HLT/EMNLP 2005.
Distance functions and IE – 4? William W. Cohen CALD.
Aligner automatiquement des ontologies avec Tuesday 23 rd of January, 2007 Rapha ë l Troncy.
Element Level Semantic Matching Pavel Shvaiko Meaning Coordination and Negotiation Workshop, ISWC 8 th November 2004, Hiroshima, Japan Paper by Fausto.
Metadata Common Vocabulary a journey from a glossary to an ontology of statistical metadata, and back Sérgio Bacelar
Shridhar Bhalerao CMSC 601 Finding Implicit Relations in the Semantic Web.
1 Aligning the Parasite Experiment Ontology and the Ontology for Biomedical Investigations Using AgreementMaker Valerie Cross, Cosmin Stroe Xueheng Hu,
A Lightweight and High Performance Monolingual Word Aligner Xuchen Yao, Benjamin Van Durme, (Johns Hopkins) Chris Callison-Burch and Peter Clark (UPenn)
Multilingual Information Retrieval using GHSOM Hsin-Chang Yang Associate Professor Department of Information Management National University of Kaohsiung.
Suggestions for Galaxy Workflow Design Using Semantically Annotated Services Alok Dhamanaskar, Michael E. Cotterell, Jessica C. Kissinger, and John Miller.
Extending the Metadata Registry for Semantic Web - Enforcing the MDR for supporting ontology concept - May 28, 2008 ISO/IEC JTC 1/SC 32 WG 2 Meeting Sydney,
Yoon kyoung-a A Semantic Match Algorithm for Web Services Based on Improved Semantic Distance Gongzhen Wang, Donghong Xu, Yong Qi, Di Hou School.
GoRelations: an Intuitive Query System for DBPedia Lushan Han and Tim Finin 15 November 2011
Of 24 lecture 11: ontology – mediation, merging & aligning.
SEMANTIC WEB Presented by- Farhana Yasmin – MD.Raihanul Islam – Nohore Jannat –
Distance functions and IE - 3 William W. Cohen CALD.
Warren Shen, Xin Li, AnHai Doan Database & AI Groups University of Illinois, Urbana Constraint-Based Entity Matching.
SERVICE ANNOTATION WITH LEXICON-BASED ALIGNMENT Service Ontology Construction Ontology of a given web service, service ontology, is constructed from service.
A German Corpus for Similarity Detection
Cross-Ontological Relationships
Using Partial Reference Alignments to Align Ontologies
Element Level Semantic Matching
Towards Evaluation of P2P-based DKMS
Result of Ontology Alignment with RiMOM at OAEI’06
Recognizing Partial Textual Entailment
Extracting Semantic Concept Relations
An Empirical Study of Property Collocation on Large Scale of Knowledge Base 龚赛赛
[jws13] Evaluation of instance matching tools: The experience of OAEI
Integrating Taxonomies
Actively Learning Ontology Matching via User Interaction
Presentation transcript:

Maurice Hermans

 Ontologies  Ontology Mapping  Research Question  String Similarities  Winkler Extension  Proposed Extension  Evaluation  Results  Conclusion Bachelor Conference

 Provide a vocabulary of terms that describe a domain of interest  There are several ways in which ontologies can differ: ◦ Encoding ◦ Lexical ◦ Syntactic ◦ Semantic ◦ Semiotic Bachelor Conference

 Knowledge systems used in the same domain can be built according to different specifications and requirements  This makes it very hard to exchange data between multiple knowledge systems which do not use the same ontology  Ontology mapping frameworks provide knowledge systems with the capacity to exchange information with other knowledge systems which use different ontologies Bachelor Conference

To what extend can string similarities, applied to concept names, be improved such that these are better suited for ontology mapping? Bachelor Conference

 Levenshtein ◦ Uses the number of edit operations required to convert string one string to another  Jaro ◦ Uses the number of matching characters between two strings and their relative position  Jaccard ◦ Compares the sets of tokens of two strings  SoftTFIDF ◦ Includes tokens which are similar according to a secondary similarity function Bachelor Conference

Bachelor Conference

Bachelor Conference

Two partial ontologies from the OAEI dataset Bachelor Conference

 Two datasets are used: ◦ 2010 Ontology Alignment Evaluation Initiative ◦ Dataset created by Cohen et al  Similarities are evaluated using precision and recall values Bachelor Conference

OAEICohen Optimal weight for both datasets is around Bachelor Conference

OAEICohen Bachelor Conference

OAEICohen Bachelor Conference

OAEICohen Bachelor Conference

Bachelor Conference