Ontology Matching with CIDER: Evaluation Report for the OAEI 2008
|
|
- Kenneth Potter
- 1 years ago
- Views:
Transcription
1 Ontology Matching with CIDER: Evaluation Report for the OAEI 2008 Jorge Gracia, Eduardo Mena IIS Department, University of Zaragoza, Spain Abstract. Ontology matching, the task of determining relations that hold among terms of two different ontologies, is a key issue in the Semantic Web and other related fields. In order to compare the behaviour of different ontology matching systems, the Ontology Alignment Evaluation Initiative (OAEI) has established a periodical controlled evaluation that comes in a yearly event. We present here our participation in the 2008 initiative. Our schema-based alignment algorithm compares each pair of ontology terms by, firstly, extracting their ontological contexts up to a certain depth (enriched by using transitive entailment) and, secondly, combining different elementary ontology matching techniques (e.g., lexical distances and vector space modelling). Benchmark results show a very good behaviour in terms of precision, while preserving an acceptable recall. Based on our experience, we have also included some remarks about the nature of benchmark test cases that, in our opinion, could help improving the OAEI tests in the future. 1 Presentation of the system In [7] we presented a system that analyzes a keyword-based user query, in order to automatically extract and make explicit, without ambiguities, its semantics. Firstly, it discovers and extracts candidate senses (expressed as ontology terms) for each keyword, by harvesting the Semantic Web. Local ontologies or lexical resources, as WordNet [6], can also be accessed. Then, an alignment and integration step is carried out in order to reduce redundancies (many terms from different ontologies could describe the same intended meaning, so we integrate them as a single sense). Finally, a disambiguation process is run to pick up the most probable sense for each keyword, according to the context. The result can be eventually used in the construction of a well-defined semantic query (expressed in a formal language) to make explicit the intended meaning of the user. We realized that the alignment component of our system is general enough to be used for many other tasks so, based on it, we have developed an independent aligner to be evaluated in the OAEI contest 1. The latest version of our alignment 1
2 service is called CIDER (Context and Inference based aligner), which is the subject of this study. It relies on a modified version of the semantic similarity measure described in [7]. 1.1 State, purpose, general statement According to the high level classification given in [3], our method is a schemabased system (opposite to others which are instance-based, or mixed), because it relies mostly on schema-level input information for performing ontology matching. As it was mentioned in the previous section, the initial purpose of our algorithm was to discover similarities among possible senses of user keywords, in order to integrate them when they were similar enough (to be later disambiguated and used in semantic query construction). Therefore, our alignment algorithm was initially applied to a previously discovered set of ontological terms, describing possible senses of a keyword. For this study we have generalized the method, to admit any two ontologies, and a threshold value, as input. Comparisons among all pairs of ontology terms (not only the ones that could refer to a same user keyword) are established, producing as output an RDF document with the obtained alignments. 1.2 Specific techniques used Our alignment process takes as basis a modified version of the semantic similarity measure described in [7]. A detailed discussion of the introduced improvements is out of the scope of this paper. However, here is a brief summary of them: 1. Addition of a transitive entailment mechanism during the extraction step, which has remarkably improved our results in terms of quality. 2. Enrichment of our initially naive comparisons between instances, by considering also their properties and corresponding values. 3. Optimization of the initially costly comparisons among properties of concepts, substituting their recursive focus with the use of vector space modelling. We have found that it preserves quality, while reduces time significantly. Figure 1 shows a schematic view of the way our matcher works. O 1 and O 2 represent the input ontologies. M is the matrix of resultant comparisons among ontology terms, and A is the extracted alignment. The first step is to extract the ontological context of each involved term, up to a certain depth. That is (depending on the type of term), their synonyms, textual descriptions, hypernyms, hyponyms, properties, domains, roles, associated concepts, etc. This process is enriched by applying a transitive inference mechanism, in order to add more semantic information that is not explicit in the asserted ontologies. The second step is the computation of similarity for each pair of terms. It is carried out differently, depending on the type of ontology term (concept, property or individual). Without entering into details, comparisons are performed like this:
3 Fig. 1. Scheme of the CIDER process. 1. Linguistic similarity between terms, considering labels and descriptions, is computed. 2. A subsequent computation explores the structural similarity of the terms, exploiting their ontological contexts and using vector space modelling in comparisons. It comprises comparison of taxonomies and relationships among terms (e.g. properties of concepts). 3. The different contributions are weighted, and a final similarity degree is provided. After that, a matrix M with all similarities is obtained. The final alignment A is then extracted, finding the highest rated one-to-one relationships among terms, and filtering out the ones that are below the given threshold. In terms of implementation, CIDER prototype has been developed in Java, extending the Alignment API [2]. The input are ontologies expressed in OWL or RDF, and the output is served as a file expressed in the alignment format [2], although it can be easily translated to other formats as well. 1.3 Adaptations made for the evaluation As the benchmark test does not consider mappings between instances, we have not computed instance alignment for this particular test. No other adaptations have been needed. 1.4 Link to the system and parameters file The version of CIDER used for this evaluation can be found at Link to the set of provided alignments (in align format) The obtained alignments for the contest can be found at
4 2 Results The following subsections describe the participation of our system in two tracks of the contest: benchmark and directory. Some remarks specific to each test are described, as well as a tentative explanation of the obtained results. Further information about the whole results of the contest can be found at [1]. 2.1 Benchmark The target of this experiment is the alignment of bibliographic ontologies. A reference ontology is proposed, and many comparisons with other ontologies of the same domain are performed. The tests are systematically generated, modifying differently the reference ontology in order to evaluate how the algorithm behaves when the aligned ontologies differ in some particular aspects. A total of 111 test cases have to be evaluated. They are grouped in three sets: 1. Concept test (cases 1xx: 101, 102,...), that explore comparisons between the reference ontology and itself, described with different expressivity levels. 2. Systematic (cases 2xx). It alters systematically the reference ontology to compare different modifications or different missing information. 3. Real ontology (cases 3xx), where comparisons with other real world bibliographic ontologies are explored. We cannot provide results for benchmark cases 202 and , because our system does not deal with ontologies in which syntax is not significant at all (these cases present a total absence or randomization of labels and comments). Consequently, we expect a result with a low recall in this experiment, as the benchmark test unfavours methods that are not based on graph structure analysis (or similar techniques). In Table 1 we show the obtained results, grouped by type of cases. We have obtained a very high precision (97%), which is in the top-three best values obtained in the contest (out of 13 participants), while recall has been lower (62%), due to the above mentioned reason. The extended results for the complete dataset has been published separately by the organizers 2. 1xx 2xx 3xx Average H-Mean Precision Recall Table 1. Averaged results for the benchmark dataset. Alternatively to the official results, we have computed the precision and recall of the benchmark test excluding the cases 202 and (and their variations 2
5 248-2, 248-4, etc.), in which ontology terms are described with non expressive texts. This is an internal exercise, which does not let us direct comparisons with other methods in the contest, but gives us another point of view (more accurate, according to the final usage of our system) of the behaviour of our method. Results are given in Table 2. 1xx 2xx 3xx Average H-Mean Precision Recall Table 2. Results for the benchmark dataset omitting cases with no significant texts. 2.2 Directory The objective of this experiment is to match terms from plain hierarchies, extracted from web directories. It consist of more than 4 thousand elementary alignments. We consider that our method cannot show all its strengths in this, because the available information is extremely sparse, lacking in semantic descriptions beyond hierarchical relationships (no instances, no properties, no comments, no synonyms,...). Results have been: 60% precision, 38% recall and 47% F-measure, which has been the second best result in this year competition (out of seven participants). A detailed comparison has been published by organizers 3. We see that, even directory alignment is not the target of our system, it behaves reasonably well when matching plain hierarchies. 3 General comments The following subsections contain some remarks and comments about the results obtained, as well as about the test cases and evaluation process. 3.1 Comments on the results As expected, we obtained better precision than recall in the benchmark test (due to the reasons mentioned in Section 2.1). Also in the directory experiment precision was higher than recall. However, it is consistent with the fact that our alignment is targeted to be used in an automatic way, minimizing human intervention. In this conditions, precision have to be promoted over recall. That is, maybe our system does not discover all correspondences, but we have to be sure that, in case it discovers an equivalence mapping between two terms, they 3 pane/oaei/2008/directory/result/
6 are most likely referring to the same meaning. Otherwise their later integration would be erroneous, and the mistake would eventually be propagated to the other steps of the system. 3.2 Discussions on the way to improve the proposed system Our method does not consider extensional information when comparing concepts, focusing only on the semantic description of the terms in the corresponding ontologies. Its inclusion could improve results in some cases where this information is available. Additionally, although our system considers many features of ontologies, their richness vary a lot from one case to another. We consider that the addition of mechanisms to auto-adjust weights to the characteristics of ontologies (as they do in [5]) could largely benefit our method. Finally, although our similarity measure has been much optimized, in terms of time response, the overall alignment process can still be subject of further improvement. 3.3 Comments on the OAEI 2008 test cases We have found the benchmark test very useful as a guideline for our internal improvements of the method, as well as to establish a certain degree of comparisons with other existing methods. On the other hand, we have missed some important issues that are not taken into account in the systematic benchmark series: 1. Benchmark tests only consider positive matchings, not measuring the ability of different methods to avoid links among barely related ontologies (only case 102 of benchmark goes in that direction). 2. For our purposes, we try to emulate the human behaviour when mapping ontological terms. As human experts cannot properly identify mappings between ontologies with scrambled texts, neither does our system. However, reference alignments provided in the benchmark evaluation for cases 202 and , do not follow this intuition. We hope this bias will be reduced in future contests. 3. Related to the latter, cases in which equal topologies, but containing different semantics, lead to false positives, are not explicitly taken into account in the benchmark. 4. How ambiguities can affect the method is not considered either in the test cases. It is a consequence of using ontologies belonging to the same domain. For example, it would be interesting to evaluate how film in an ontology about movies, is mapped to film as a thin layer in another ontology. Therefore it is difficult to evaluate the benefits of including certain disambiguation techniques in ontology matching [4].
7 3.4 Comments on the OAEI 2008 measures Unsuitability of precision and recall measures for ontology matching evaluation is a well known problem [3]. We encourage organizers to try different measures that count all correct found correspondences, even when they are not explicit in the reference alignment. 4 Conclusion We have presented here some results of our first participation in the OAEI 2008 contest. We have limited to two tracks: benchmark and directory, but we hope to extend our participation in the future. Our schema-based alignment algorithm compares the ontological contexts of each pair of terms (enriched with transitive inference) by combining different elementary ontology matching techniques (comparing vocabulary, taxonomies, relations,...). Benchmark results show a very good behaviour of our system in terms of precision, while keeping an acceptable recall. It confirms the validity of the measure we have conceived, and its suitability to be applied in ontology matching tasks. It encourages us to tackle further improvements, and to extend the scope and applicability of our techniques. We have also included, based on our experience, some considerations about the nature of benchmark test cases that, in our opinion, could help improving future contests. Acknowledgments. This work is supported by the CICYT project TIN C References 1. C. Caracciolo, J. Euzenat, L. Hollink, R. Ichise, A. Isaac, V. Malaisé, C. Meilicke, J. Pane, P. Shvaiko, H. Stuckenschmidt, O. Šváb-Zamaza, and V. Svátek. First results of the ontology alignment evaluation initiative In In Proc. ISWC-2008 Workshop on Ontology Matching, J. Euzenat. An API for ontology alignment. In 3rd International Semantic Web Conference (ISWC 04), Hiroshima (Japan). Springer, November J. Euzenat and P. Shvaiko. Ontology matching. Springer-Verlag, J. Gracia, V. López, M. d Aquin, M. Sabou, E. Motta, and E. Mena. Solving semantic ambiguity to improve semantic web based ontology matching. In Proc. of 2nd Ontology Matching Workshop (OM 07), at 6th International Semantic Web Conference (ISWC 07), Busan (Korea), November Y. Jean-Mary and M. Kabuka. ASMOV: Ontology alignment with semantic validation. In Proc. of Joint SWDB-ODBIS Workshop on Semantics, Ontologies, Databases, Vienna (Austria), September G. Miller. WordNet: A Lexical Database for English. Communications of the ACM, 38(11), nov R. Trillo, J. Gracia, M. Espinoza, and E. Mena. Discovering the semantics of user keywords. Journal on Universal Computer Science. Special Issue: Ontologies and their Applications, November 2007.
Solving Semantic Ambiguity to Improve Semantic Web based Ontology Matching
Solving Semantic Ambiguity to Improve Semantic Web based Ontology Matching Jorge Gracia 1, Vanessa López 2, Mathieu d Aquin 2, Marta Sabou 2, Enrico Motta 2, and Eduardo Mena 1 1 IIS Department, University
RiMOM Results for OAEI 2009
RiMOM Results for OAEI 2009 Xiao Zhang, Qian Zhong, Feng Shi, Juanzi Li and Jie Tang Department of Computer Science and Technology, Tsinghua University, Beijing, China zhangxiao,zhongqian,shifeng,ljz,tangjie@keg.cs.tsinghua.edu.cn
Cluster-based Similarity Aggregation for Ontology Matching
Cluster-based Similarity Aggregation for Ontology Matching Quang-Vinh Tran 1, Ryutaro Ichise 2, and Bao-Quoc Ho 1 1 Faculty of Information Technology, Ho Chi Minh University of Science, Vietnam {tqvinh,hbquoc}@fit.hcmus.edu.vn
Evolva: A Comprehensive Approach to Ontology Evolution
Evolva: A Comprehensive Approach to Evolution Fouad Zablith Knowledge Media Institute (KMi), The Open University Walton Hall, Milton Keynes, MK7 6AA, United Kingdom f.zablith@open.ac.uk Abstract. evolution
Using AgreementMaker to Align Ontologies for OAEI 2010
Using AgreementMaker to Align Ontologies for OAEI 2010 Isabel F. Cruz, Cosmin Stroe, Michele Caci, Federico Caimi, Matteo Palmonari, Flavio Palandri Antonelli, Ulas C. Keles ADVIS Lab, Department of Computer
University of Rome Tor Vergata GENOMA. GENeric Ontology Matching Architecture
University of Rome Tor Vergata GENOMA GENeric Ontology Matching Architecture Maria Teresa Pazienza +, Roberto Enea +, Andrea Turbati + + ART Group, University of Rome Tor Vergata, Via del Politecnico 1,
FOAM Framework for Ontology Alignment and Mapping Results of the Ontology Alignment Evaluation Initiative
FOAM Framework for Ontology Alignment and Mapping Results of the Ontology Alignment Evaluation Initiative Marc Ehrig Institute AIFB University of Karlsruhe 76128 Karlsruhe, Germany ehrig@aifb.uni-karlsruhe.de
Although it s far from fully deployed, Semantic Heterogeneity Issues on the Web. Semantic Web
Semantic Web Semantic Heterogeneity Issues on the Web To operate effectively, the Semantic Web must be able to make explicit the semantics of Web resources via ontologies, which software agents use to
A Tagging Approach to Ontology Mapping
A Tagging Approach to Ontology Mapping Colm Conroy 1, Declan O'Sullivan 1, Dave Lewis 1 1 Knowledge and Data Engineering Group, Trinity College Dublin {coconroy,declan.osullivan,dave.lewis}@cs.tcd.ie Abstract.
TOAST Results for OAEI 2012
TOAST Results for OAEI 2012 Arkadiusz Jachnik, Andrzej Szwabe, Pawel Misiorek, and Przemyslaw Walkowiak Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie
ALIN Results for OAEI 2017
ALIN Results for OAEI 2017 Jomar da Silva 1, Fernanda Araujo Baião 1, and Kate Revoredo 1 Graduated Program in Informatics, Department of Applied Informatics Federal University of the State of Rio de Janeiro
WEB services have received much attention and interest
Discovering Web Services Using Semantic Keywords Mauricio Espiza and Eduardo Mena Abstract With the increasing growth in popularity of Web services, the discovery of relevant services becomes a significant
Anchor-Profiles for Ontology Mapping with Partial Alignments
Anchor-Profiles for Ontology Mapping with Partial Alignments Frederik C. Schadd Nico Roos Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands Abstract. Ontology mapping
InsMT / InsMTL Results for OAEI 2014 Instance Matching
InsMT / InsMTL Results for OAEI 2014 Instance Matching Abderrahmane Khiat 1, Moussa Benaissa 1 1 LITIO Lab, University of Oran, BP 1524 El-Mnaouar Oran, Algeria abderrahmane_khiat@yahoo.com moussabenaissa@yahoo.fr
Measuring The Degree Of Similarity Between Web Ontologies Based On Semantic Coherence
Measuring The Degree Of Similarity Between Web Ontologies Based On Semantic Coherence ABHIK BANERJEE, HAREENDRA MUNIMADUGU, SRINIVASA RAGHAVAN VEDANARAYANAN, LAWRENCE J. MAZLACK Applied Computational Intelligence
Boosting Annotated Web Services in SAWSDL
Boosting Annotated Web Services in SAWSDL Antonio J. Roa-Valverde, Jorge Martinez-Gil, and José F. Aldana-Montes University of Málaga, Department of Computer Languages and Computing Sciences Boulevard
ALIN Results for OAEI 2016
ALIN Results for OAEI 2016 Jomar da Silva, Fernanda Araujo Baião and Kate Revoredo Department of Applied Informatics Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Brazil {jomar.silva,fernanda.baiao,katerevoredo}@uniriotec.br
Testing the Impact of Pattern-Based Ontology Refactoring on Ontology Matching Results
Testing the Impact of Pattern-Based Ontology Refactoring on Ontology Matching Results Ondřej Šváb-Zamazal 1, Vojtěch Svátek 1, Christian Meilicke 2, and Heiner Stuckenschmidt 2 1 University of Economics,
Towards Rule Learning Approaches to Instance-based Ontology Matching
Towards Rule Learning Approaches to Instance-based Ontology Matching Frederik Janssen 1, Faraz Fallahi 2 Jan Noessner 3, and Heiko Paulheim 1 1 Knowledge Engineering Group, TU Darmstadt, Hochschulstrasse
Ontology matching benchmarks: generation and evaluation
Ontology matching benchmarks: generation and evaluation Maria Rosoiu, Cássia Trojahn dos Santos, Jérôme Euzenat To cite this version: Maria Rosoiu, Cássia Trojahn dos Santos, Jérôme Euzenat. Ontology matching
Towards On the Go Matching of Linked Open Data Ontologies
Towards On the Go Matching of Linked Open Data Ontologies Isabel F. Cruz ADVIS Lab Department of Computer Science University of Illinois at Chicago ifc@cs.uic.edu Matteo Palmonari DISCo University of Milan-Bicocca
Refining Ontologies by Pattern-Based Completion
Refining Ontologies by Pattern-Based Completion Nadejda Nikitina and Sebastian Rudolph and Sebastian Blohm Institute AIFB, University of Karlsruhe D-76128 Karlsruhe, Germany {nikitina, rudolph, blohm}@aifb.uni-karlsruhe.de
POMap results for OAEI 2017
POMap results for OAEI 2017 Amir Laadhar 1, Faiza Ghozzi 2, Imen Megdiche 1, Franck Ravat 1, Olivier Teste 1, and Faiez Gargouri 2 1 Paul Sabatier University, IRIT (CNRS/UMR 5505) 118 Route de Narbonne
A Tool for Storing OWL Using Database Technology
A Tool for Storing OWL Using Database Technology Maria del Mar Roldan-Garcia and Jose F. Aldana-Montes University of Malaga, Computer Languages and Computing Science Department Malaga 29071, Spain, (mmar,jfam)@lcc.uma.es,
Semantic Web. Ontology Alignment. Morteza Amini. Sharif University of Technology Fall 95-96
ه عا ی Semantic Web Ontology Alignment Morteza Amini Sharif University of Technology Fall 95-96 Outline The Problem of Ontologies Ontology Heterogeneity Ontology Alignment Overall Process Similarity (Matching)
Using the Semantic Web as Background Knowledge for Ontology Mapping
Using the Semantic Web as Background Knowledge for Ontology Mapping Marta Sabou, Mathieu d Aquin, and Enrico Motta Knowledge Media Institute (KMi) The Open University, Milton Keynes, United Kingdom {r.m.sabou,
Semantic-Based Information Retrieval for Java Learning Management System
AENSI Journals Australian Journal of Basic and Applied Sciences Journal home page: www.ajbasweb.com Semantic-Based Information Retrieval for Java Learning Management System Nurul Shahida Tukiman and Amirah
Bibster A Semantics-Based Bibliographic Peer-to-Peer System
Bibster A Semantics-Based Bibliographic Peer-to-Peer System Peter Haase 1, Björn Schnizler 1, Jeen Broekstra 2, Marc Ehrig 1, Frank van Harmelen 2, Maarten Menken 2, Peter Mika 2, Michal Plechawski 3,
Cluster-based Instance Consolidation For Subsequent Matching
Jennifer Sleeman and Tim Finin, Cluster-based Instance Consolidation For Subsequent Matching, First International Workshop on Knowledge Extraction and Consolidation from Social Media, November 2012, Boston.
HotMatch Results for OEAI 2012
HotMatch Results for OEAI 2012 Thanh Tung Dang, Alexander Gabriel, Sven Hertling, Philipp Roskosch, Marcel Wlotzka, Jan Ruben Zilke, Frederik Janssen, and Heiko Paulheim Technische Universität Darmstadt
IJCSC Volume 5 Number 1 March-Sep 2014 pp ISSN
Movie Related Information Retrieval Using Ontology Based Semantic Search Tarjni Vyas, Hetali Tank, Kinjal Shah Nirma University, Ahmedabad tarjni.vyas@nirmauni.ac.in, tank92@gmail.com, shahkinjal92@gmail.com
Query Difficulty Prediction for Contextual Image Retrieval
Query Difficulty Prediction for Contextual Image Retrieval Xing Xing 1, Yi Zhang 1, and Mei Han 2 1 School of Engineering, UC Santa Cruz, Santa Cruz, CA 95064 2 Google Inc., Mountain View, CA 94043 Abstract.
38050 Povo Trento (Italy), Via Sommarive 14 DISCOVERING MISSING BACKGROUND KNOWLEDGE IN ONTOLOGY MATCHING
UNIVERSITY OF TRENTO DEPARTMENT OF INFORMATION AND COMMUNICATION TECHNOLOGY 38050 Povo Trento (Italy), Via Sommarive 14 http://www.dit.unitn.it DISCOVERING MISSING BACKGROUND KNOWLEDGE IN ONTOLOGY MATCHING
RESULTS OF THE ONTOLOGY ALIGNMENT EVALUATION INITIATIVE 2009
DISI Via Sommarive 14 38123 Povo Trento (Italy) http://www.disi.unitn.it RESULTS OF THE ONTOLOGY ALIGNMENT EVALUATION INITIATIVE 2009 Jérôme Euzenat, Alfio Ferrara, Laura Hollink, Antoine Isaac, Cliff
Java Learning Object Ontology
Java Learning Object Ontology Ming-Che Lee, Ding Yen Ye & Tzone I Wang Laboratory of Intelligent Network Applications Department of Engineering Science National Chung Kung University Taiwan limingche@hotmail.com,
DSSim Results for OAEI 2009
DSSim Results for OAEI 2009 Miklos Nagy 1, Maria Vargas-Vera 1, and Piotr Stolarski 2 1 The Open University Walton Hall, Milton Keynes, MK7 6AA, United Kingdom m.nagy@open.ac.uk;m.vargas-vera@open.ac.uk
A GML SCHEMA MAPPING APPROACH TO OVERCOME SEMANTIC HETEROGENEITY IN GIS
A GML SCHEMA MAPPING APPROACH TO OVERCOME SEMANTIC HETEROGENEITY IN GIS Manoj Paul, S. K. Ghosh School of Information Technology, Indian Institute of Technology, Kharagpur 721302, India - (mpaul, skg)@sit.iitkgp.ernet.in
Ontology Creation and Development Model
Ontology Creation and Development Model Pallavi Grover, Sonal Chawla Research Scholar, Department of Computer Science & Applications, Panjab University, Chandigarh, India Associate. Professor, Department
Semantic Interactive Ontology Matching: Synergistic Combination of Techniques to Improve the Set of Candidate Correspondences
Semantic Interactive Ontology Matching: Synergistic Combination of Techniques to Improve the Set of Correspondences Jomar da Silva 1, Fernanda Araujo Baião 1, Kate Revoredo 1, and Jérôme Euzenat 2 1 Graduated
Dealing with Uncertain Entities in Ontology Alignment using Rough Sets
IEEE TRANSACTIONS ON SYSTEMS,MAN AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS, SMCC-2-0-005 Dealing with Uncertain Entities in Ontology Alignment using Rough Sets Sadaqat Jan, Maozhen Li, Hamed Al-Raweshidy,
An FCA Framework for Knowledge Discovery in SPARQL Query Answers
An FCA Framework for Knowledge Discovery in SPARQL Query Answers Melisachew Wudage Chekol, Amedeo Napoli To cite this version: Melisachew Wudage Chekol, Amedeo Napoli. An FCA Framework for Knowledge Discovery
Identifying Relevant Sources for Data Linking using a Semantic Web Index
Identifying Relevant Sources for Data Linking using a Semantic Web Index Andriy Nikolov a.nikolov@open.ac.uk Knowledge Media Institute Open University Milton Keynes, UK Mathieu d Aquin m.daquin@open.ac.uk
Ylvi - Multimedia-izing the Semantic Wiki
Ylvi - Multimedia-izing the Semantic Wiki Niko Popitsch 1, Bernhard Schandl 2, rash miri 1, Stefan Leitich 2, and Wolfgang Jochum 2 1 Research Studio Digital Memory Engineering, Vienna, ustria {niko.popitsch,arash.amiri}@researchstudio.at
ODGOMS - Results for OAEI 2013 *
ODGOMS - Results for OAEI 2013 * I-Hong Kuo 1,2, Tai-Ting Wu 1,3 1 Industrial Technology Research Institute, Taiwan 2 yihonguo@itri.org.tw 3 taitingwu@itri.org.tw Abstract. ODGOMS is a multi-strategy ontology
Racer: An OWL Reasoning Agent for the Semantic Web
Racer: An OWL Reasoning Agent for the Semantic Web Volker Haarslev and Ralf Möller Concordia University, Montreal, Canada (haarslev@cs.concordia.ca) University of Applied Sciences, Wedel, Germany (rmoeller@fh-wedel.de)
0.1 Knowledge Organization Systems for Semantic Web
0.1 Knowledge Organization Systems for Semantic Web 0.1 Knowledge Organization Systems for Semantic Web 0.1.1 Knowledge Organization Systems Why do we need to organize knowledge? Indexing Retrieval Organization
An Ontology Based Question Answering System on Software Test Document Domain
An Ontology Based Question Answering System on Software Test Document Domain Meltem Serhatli, Ferda N. Alpaslan Abstract Processing the data by computers and performing reasoning tasks is an important
Web Services Annotation and Reasoning
Web Services Annotation and Reasoning, W3C Workshop on Frameworks for Semantics in Web Services Web Services Annotation and Reasoning Peter Graubmann, Evelyn Pfeuffer, Mikhail Roshchin Siemens AG, Corporate
Effect of log-based Query Term Expansion on Retrieval Effectiveness in Patent Searching
Effect of log-based Query Term Expansion on Retrieval Effectiveness in Patent Searching Wolfgang Tannebaum, Parvaz Madabi and Andreas Rauber Institute of Software Technology and Interactive Systems, Vienna
The Role of Design Rationale in the Ontology Matching Step during the Triplification of Relational Databases
The Role of Design Rationale in the Ontology Matching Step during the Triplification of Relational Databases Rita Berardi 1, Marcelo Schiessl 2, Matthias Thimm 3, Marco A. Casanova 1 1 Departamento de
Ontology Matching Techniques: a 3-Tier Classification Framework
Ontology Matching Techniques: a 3-Tier Classification Framework Nelson K. Y. Leung RMIT International Universtiy, Ho Chi Minh City, Vietnam nelson.leung@rmit.edu.vn Seung Hwan Kang Payap University, Chiang
A Semantic Web-Based Approach for Harvesting Multilingual Textual. definitions from Wikipedia to support ICD-11 revision
A Semantic Web-Based Approach for Harvesting Multilingual Textual Definitions from Wikipedia to Support ICD-11 Revision Guoqian Jiang 1,* Harold R. Solbrig 1 and Christopher G. Chute 1 1 Department of
Linking library data: contributions and role of subject data. Nuno Freire The European Library
Linking library data: contributions and role of subject data Nuno Freire The European Library Outline Introduction to The European Library Motivation for Linked Library Data The European Library Open Dataset
jcel: A Modular Rule-based Reasoner
jcel: A Modular Rule-based Reasoner Julian Mendez Theoretical Computer Science, TU Dresden, Germany mendez@tcs.inf.tu-dresden.de Abstract. jcel is a reasoner for the description logic EL + that uses a
Creating Large-scale Training and Test Corpora for Extracting Structured Data from the Web
Creating Large-scale Training and Test Corpora for Extracting Structured Data from the Web Robert Meusel and Heiko Paulheim University of Mannheim, Germany Data and Web Science Group {robert,heiko}@informatik.uni-mannheim.de
Linked Data and cultural heritage data: an overview of the approaches from Europeana and The European Library
Linked Data and cultural heritage data: an overview of the approaches from Europeana and The European Library Nuno Freire Chief data officer The European Library Pacific Neighbourhood Consortium 2014 Annual
Discovering the Semantics of User Keywords
Journal of Universal Computer Science, vol. 13, no. 12 (2007), 1908-1935 submitted: 15/4/07, accepted: 31/7/07, appeared: 1/12/07 J.UCS Discovering the Semantics of User Keywords Raquel Trillo, Jorge Gracia,
PECULIARITIES OF LINKED DATA PROCESSING IN SEMANTIC APPLICATIONS. Sergey Shcherbak, Ilona Galushka, Sergey Soloshich, Valeriy Zavgorodniy
International Journal "Information Models and Analyses" Vol.2 / 2013, Number 2 139 PECULIARITIES OF LINKED DATA PROCESSING IN SEMANTIC APPLICATIONS Sergey Shcherbak, Ilona Galushka, Sergey Soloshich, Valeriy
Performance Evaluation of Semantic Registries: OWLJessKB and instancestore
Service Oriented Computing and Applications manuscript No. (will be inserted by the editor) Performance Evaluation of Semantic Registries: OWLJessKB and instancestore Simone A. Ludwig 1, Omer F. Rana 2
Improving Semantic Interoperability of Distributed Geospatial Web Services:
Improving Semantic Interoperability of Distributed Geospatial Web Services: G-MAP SEMANTIC MAPPING SYSTEM Mohamed Bakillah (Ph.D Candidate) Mir Abolfazl Mostafavi, Center for Research in Geomatic Laval
RaDON Repair and Diagnosis in Ontology Networks
RaDON Repair and Diagnosis in Ontology Networks Qiu Ji, Peter Haase, Guilin Qi, Pascal Hitzler, and Steffen Stadtmüller Institute AIFB Universität Karlsruhe (TH), Germany {qiji,pha,gqi,phi}@aifb.uni-karlsruhe.de,
Kandidat 113. INFO310 0 Advanced Topics in Model-Based Information Systems. Oppgaver Oppgavetype Vurdering Status
INFO310 0 Advanced Topics in Model-Based Information Systems Kandidat 113 Oppgaver Oppgavetype Vurdering Status Introduction Dokument Automatisk poengsum Levert Plagiarism and Declaration Dokument Automatisk
Semantic text features from small world graphs
Semantic text features from small world graphs Jurij Leskovec 1 and John Shawe-Taylor 2 1 Carnegie Mellon University, USA. Jozef Stefan Institute, Slovenia. jure@cs.cmu.edu 2 University of Southampton,UK
An Architecture for Semantic Enterprise Application Integration Standards
An Architecture for Semantic Enterprise Application Integration Standards Nenad Anicic 1, 2, Nenad Ivezic 1, Albert Jones 1 1 National Institute of Standards and Technology, 100 Bureau Drive Gaithersburg,
Results of the Ontology Alignment Evaluation Initiative 2010
Results of the Ontology Alignment Evaluation Initiative 2010 Jérôme Euzenat, Alfio Ferrara, Christian Meilicke, Andriy Nikolov, Juan Pane, François Scharffe, Pavel Shvaiko, Heiner Stuckenschmidt, Ondrej
Knowledge Retrieval. Franz J. Kurfess. Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A.
Knowledge Retrieval Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. 1 Acknowledgements This lecture series has been sponsored by the European
Using Linked Data to Reduce Learning Latency for e-book Readers
Using Linked Data to Reduce Learning Latency for e-book Readers Julien Robinson, Johann Stan, and Myriam Ribière Alcatel-Lucent Bell Labs France, 91620 Nozay, France, Julien.Robinson@alcatel-lucent.com
Semantic Exploitation of Engineering Models: An Application to Oilfield Models
Semantic Exploitation of Engineering Models: An Application to Oilfield Models Laura Silveira Mastella 1,YamineAït-Ameur 2,Stéphane Jean 2, Michel Perrin 1, and Jean-François Rainaud 3 1 Ecole des Mines
SCOS-2000 Technical Note
SCOS-2000 Technical Note MDA Study Prototyping Technical Note Document Reference: Document Status: Issue 1.0 Prepared By: Eugenio Zanatta MDA Study Prototyping Page: 2 Action Name Date Signature Prepared
Collaborative Ontology Construction using Template-based Wiki for Semantic Web Applications
2009 International Conference on Computer Engineering and Technology Collaborative Ontology Construction using Template-based Wiki for Semantic Web Applications Sung-Kooc Lim Information and Communications
SmartMatcher How Examples and a Dedicated Mapping Language can Improve the Quality of Automatic Matching Approaches
SmartMatcher How Examples and a Dedicated Language can Improve the Quality of Automatic Matching Approaches Horst Kargl and Manuel Wimmer Business Informatics Group Vienna University of Technology Favoritenstrasse
Ontology matching techniques: a 3-tier classification framework
University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 2009 Ontology matching techniques: a 3-tier classification framework Nelson
Cross-Fertilizing Data through Web of Things APIs with JSON-LD
Cross-Fertilizing Data through Web of Things APIs with JSON-LD Wenbin Li and Gilles Privat Orange Labs, Grenoble, France gilles.privat@orange.com, liwb1216@gmail.com Abstract. Internet of Things (IoT)
Knowledge Sharing and Reuse in Digital Forensics
Knowledge Sharing and Reuse in Digital Forensics Jingshan Huang 1 and Alec Yasinsac School of Computer and Information Sciences University of South Alabama Mobile, AL, U.S.A. {huang, yasinsac}@usouthal.edu
Multilabel Classification Evaluation using Ontology Information
Multilabel Classification Evaluation using Ontology Information Stefanie Nowak, Hanna Lukashevich Fraunhofer Institute for Digital Media Technology IDMT Ehrenbergstrasse 31, 98693 Ilmenau, Germany, {nwk,lkh}@idmt.fraunhofer.de
Semantic Searching. John Winder CMSC 676 Spring 2015
Semantic Searching John Winder CMSC 676 Spring 2015 Semantic Searching searching and retrieving documents by their semantic, conceptual, and contextual meanings Motivations: to do disambiguation to improve
The Open University s repository of research publications and other research outputs
Open Research Online The Open University s repository of research publications and other research outputs Exploring the Semantic Web as Background Knowledge for Ontology Matching Journal Article How to
OntoEval Assessment Tool for OWL Ontology based Application
OntoEval Assessment Tool for OWL Ontology based Application Bekka Fatiha Computer Science Department University Mohamed El-Bachir El- Ibrahimi Bordj Bou Arreridj, Algeria Maache Salah Computer Science
Ontology matching: state of the art and future challenges
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. X, NO. X, JANUARY 201X 1 Ontology matching: state of the art and future challenges Pavel Shvaiko and Jérôme Euzenat Abstract After years of research
A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet
A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet Joerg-Uwe Kietz, Alexander Maedche, Raphael Volz Swisslife Information Systems Research Lab, Zuerich, Switzerland fkietz, volzg@swisslife.ch
A LEXICAL APPROACH FOR TAXONOMY MAPPING
Journal of Web Engineering, Vol. 0, No. 0 (2015) 000 000 c Rinton Press A LEXICAL APPROACH FOR TAXONOMY MAPPING LENNART NEDERSTIGT, DAMIR VANDIC, and FLAVIUS FRASINCAR Econometric Institute, Erasmus University
XETA: extensible metadata System
XETA: extensible metadata System Abstract: This paper presents an extensible metadata system (XETA System) which makes it possible for the user to organize and extend the structure of metadata. We discuss
Extraction of Semantic Text Portion Related to Anchor Link
1834 IEICE TRANS. INF. & SYST., VOL.E89 D, NO.6 JUNE 2006 PAPER Special Section on Human Communication II Extraction of Semantic Text Portion Related to Anchor Link Bui Quang HUNG a), Masanori OTSUBO,
The Semantic Web DEFINITIONS & APPLICATIONS
The Semantic Web DEFINITIONS & APPLICATIONS Data on the Web There are more an more data on the Web Government data, health related data, general knowledge, company information, flight information, restaurants,
Matching Techniques for Resource Discovery in Distributed Systems Using Heterogeneous Ontology Descriptions
Matching Techniques for Discovery in Distributed Systems Using Heterogeneous Ontology Descriptions S. Castano, A. Ferrara, S. Montanelli, G. Racca Università degli Studi di Milano DICO - Via Comelico,
Theme Identification in RDF Graphs
Theme Identification in RDF Graphs Hanane Ouksili PRiSM, Univ. Versailles St Quentin, UMR CNRS 8144, Versailles France hanane.ouksili@prism.uvsq.fr Abstract. An increasing number of RDF datasets is published
INFORMATION RETRIEVAL AND THE THESAURUS R.M. Needham T. Joyce Cambridge Language Research Unit.
Introduction INFORMATION RETRIEVAL AND THE THESAURUS R.M. Needham T. Joyce Cambridge Language Research Unit. This paper describes various developments of the retrieval system devised in Cambridge last
Learning Disjointness for Debugging Mappings between Lightweight Ontologies
Learning Disjointness for Debugging Mappings between Lightweight Ontologies Christian Meilicke 1, Johanna Völker 2, Heiner Stuckenschmidt 1 1 Computer Science Institute Universität Mannheim, Germany {christian,heiner}@informatik.uni-mannheim.de
THE GETTY VOCABULARIES TECHNICAL UPDATE
AAT TGN ULAN CONA THE GETTY VOCABULARIES TECHNICAL UPDATE International Working Group Meetings January 7-10, 2013 Joan Cobb Gregg Garcia Information Technology Services J. Paul Getty Trust International
On the Reduction of Dublin Core Metadata Application Profiles to Description Logics and OWL
On the Reduction of Dublin Core Metadata Application Profiles to Description Logics and OWL Dimitrios A. Koutsomitropoulos High Performance Information Systems Lab, Computer Engineering and Informatics
Semantic Web Company. PoolParty - Server. PoolParty - Technical White Paper.
Semantic Web Company PoolParty - Server PoolParty - Technical White Paper http://www.poolparty.biz Table of Contents Introduction... 3 PoolParty Technical Overview... 3 PoolParty Components Overview...
Semantic Web. Tahani Aljehani
Semantic Web Tahani Aljehani Motivation: Example 1 You are interested in SOAP Web architecture Use your favorite search engine to find the articles about SOAP Keywords-based search You'll get lots of information,
Europeana update: aspects of the data
Europeana update: aspects of the data Robina Clayphan, Europeana Foundation European Film Gateway Workshop, 30 May 2011, Frankfurt/Main Overview The Europeana Data Model (EDM) Data enrichment activity
Extracting knowledge from Ontology using Jena for Semantic Web
Extracting knowledge from Ontology using Jena for Semantic Web Ayesha Ameen I.T Department Deccan College of Engineering and Technology Hyderabad A.P, India ameenayesha@gmail.com Khaleel Ur Rahman Khan
EFFICIENT INTEGRATION OF SEMANTIC TECHNOLOGIES FOR PROFESSIONAL IMAGE ANNOTATION AND SEARCH
EFFICIENT INTEGRATION OF SEMANTIC TECHNOLOGIES FOR PROFESSIONAL IMAGE ANNOTATION AND SEARCH Andreas Walter FZI Forschungszentrum Informatik, Haid-und-Neu-Straße 10-14, 76131 Karlsruhe, Germany, awalter@fzi.de
Efficient Querying of Web Services Using Ontologies
Journal of Algorithms & Computational Technology Vol. 4 No. 4 575 Efficient Querying of Web Services Using Ontologies K. Saravanan, S. Kripeshwari and Arunkumar Thangavelu School of Computing Sciences,
A Unified Approach for Aligning Taxonomies and Debugging Taxonomies and Their Alignments
A Unified Approach for Aligning Taxonomies and Debugging Taxonomies and Their Alignments Valentina Ivanova and Patrick Lambrix,, Sweden And The Swedish e-science Research Center Outline Defects in Ontologies
An interactive, asymmetric and extensional method for matching conceptual hierarchies
An interactive, asymmetric and extensional method for matching conceptual hierarchies Jérôme David, Fabrice Guillet, Régis Gras, and Henri Briand LINA CNRS FRE 2729, Polytechnic School of Nantes University,
ResPubliQA 2010
SZTAKI @ ResPubliQA 2010 David Mark Nemeskey Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary (SZTAKI) Abstract. This paper summarizes the results of our first
First results of the Ontology Alignment Evaluation Initiative 2011
First results of the Ontology Alignment Evaluation Initiative 2011 Jérôme Euzenat 1, Alfio Ferrara 2, Willem Robert van Hage 3, Laura Hollink 4, Christian Meilicke 5, Andriy Nikolov 6, François Scharffe