Discovering Semantic Links Between Synonyms, Hyponyms and Hypernyms
This proposal aims for semantic enrichment between
glossaries using the Simple Knowledge Organization System (SKOS)
vocabulary to discover synonyms, hyponyms and hyperonyms
semiautomatically, in Brazilian Portuguese, generating new semantic
relationships based on WordNet. To evaluate the quality of this
proposed model, experiments were performed by the use of two sets
containing new relations, being one generated automatically and the
other manually mapped by the domain expert. The applied evaluation
metrics were precision, recall, f-score, and confidence interval. The
results obtained demonstrate that the applied method in the field of
Oil Production and Extraction (E&P) is effective, which suggests that
it can be used to improve the quality of terminological mappings.
The procedure, although adding complexity in its elaboration, can be
reproduced in others domains.
[1] David Aumueller, Hong-Hai Do, Sabine Massmann, and Erhard Rahm.
2005. Schema and ontology matching with COMA++. InProc. ACM
SIGMOD.
[2] Jiaqing Liang, Yi Zhang, Yanghua Xiao, Haixun Wang, Wei Wang, and
PinpinZhu. 2017. On the Transitivity of Hypernym-Hyponym Relations in
Data-Driven Lexical Taxonomies. In AAAI. AAAI Press, 1185–1191.
[3] George A. Miller. 1995. WordNet: a Lexical Database for
English.Commun. ACM38, 11 (1995), 39–41.
[4] Akarajit Tanjana and Jian Qu. [n. d.]. Enabling Fuzzy Logic to Enhance
Auto-matic Schema Matching. In Science & Technology Asia, Vol. 22.
93–111.
[5] Christian Bizer, Richard Cyganiak, and Tobias Gauss. 2007. The RDF
BookMashup: From Web APIs to a Web of Data. In3rd Workshop on
Scripting forthe Semantic Web.
[6] J. M. Bland and D. G. Altman. 1996. Transformations, means, and
confidenceintervals. BMJ312, 7038 (April 1996), 1079.
[7] D. Lenat and R. V. Guha. 1990. Building Large Knowledge-Based
Systems: Representation and Inference in the Cyc Project.
Addison-Wesley.
[8] William W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. 2003.
A Com-parison of String Distance Metrics for Name-Matching Tasks. In
Proceedings of the IJCAI-2003 Workshop on. Acapulco, Mexico, 73–78.
[9] Mathieu d’Aquin, Gabriel Kronberger, and Mari Carmen
Su´arez-Figueroa. 2012. Combining Data Mining and Ontology
Engineering to Enrich Ontologies and Linked Data. InKNOW@LOD,
Vol. 868. CEUR-WS.org, 19–24.
[10] Tom Heath and Christian Bizer. 2011. Linked Data: Evolving the Web
into a Global Data Space (1st ed.). Morgan Claypool.
[11] Maria Koutraki, Nicoleta Preda, and Dan Vodislav. 2016. SOFYA:
Semanticon-the-fly Relation Alignment. In EDBT. OpenProceedings.org,
690–691.
[12] M. Lesk. 1986. Automatic sense disambiguation using machine
readabledictionaries: how to tell a pine cone from an ice cream cone. In
Proceedings of ACM SIGDOC Conference. Toronto, Ontario, 24–26.
[13] V. Levenshtein. 1966. Binary Codes Capable of Correcting Deletions,
Insertions, and Reversals. Soviet Physics-Doklady10, 8 (1966), 707–710.
[14] Juan Antonio Lossio-Ventura, Mathieu Roche, Cl´ement Jonquet, and
Maguelonne Teisseire. 2016. A Way to Automatically Enrich Biomedical
Ontologies. In EDBT, OpenProceedings.org, 676–677.
[15] Alistair Miles, Brian Matthews, Michael Wilson, and Dan Brickley.
2005. SKOScore: Simple Knowledge Organisation for the Web. In Proc.
of the 2005 international conference on DC and metadata applications.
Madrid, Spanien, 1–9.
[16] Markus Nentwig, Michael Hartung, Axel-Cyrille Ngonga Ngomo, and
Erhard Rahm. 2015. A survey of current Link Discovery frameworks.
Semantic WebPreprint (2015), 1–18.
[17] Jacco van Ossenbruggen, Michiel Hildebrand, and Viktor de Boer. 2011.
Interactive Vocabulary Alignment. InTPDL (Lecture Notes in Computer
Science), Vol. 6966. Springer, 296–307.
[18] Ziqi Zhang, Anna Lisa Gentile, Eva Blomqvist, Isabelle Augenstein,
and FabioCiravegna. 2013. Statistical Knowledge Patterns: Identifying
Synonymous Re-lations in Large Linked Datasets. In International
Semantic Web Conference(1), Vol. 8218. Springer, 703–719.
[19] Ricardo A´ vila, Saloma˜o Santos, David Arau´jo, Vaˆnia Maria Ponte
Vidal, and Jos´e Antˆonio Fernandes de Macˆedo. 2017. Semantic Links
Using SKOS Predicates. In KES (Procedia Computer Science), Vol. 112.
Elsevier, 467–473.
[1] David Aumueller, Hong-Hai Do, Sabine Massmann, and Erhard Rahm.
2005. Schema and ontology matching with COMA++. InProc. ACM
SIGMOD.
[2] Jiaqing Liang, Yi Zhang, Yanghua Xiao, Haixun Wang, Wei Wang, and
PinpinZhu. 2017. On the Transitivity of Hypernym-Hyponym Relations in
Data-Driven Lexical Taxonomies. In AAAI. AAAI Press, 1185–1191.
[3] George A. Miller. 1995. WordNet: a Lexical Database for
English.Commun. ACM38, 11 (1995), 39–41.
[4] Akarajit Tanjana and Jian Qu. [n. d.]. Enabling Fuzzy Logic to Enhance
Auto-matic Schema Matching. In Science & Technology Asia, Vol. 22.
93–111.
[5] Christian Bizer, Richard Cyganiak, and Tobias Gauss. 2007. The RDF
BookMashup: From Web APIs to a Web of Data. In3rd Workshop on
Scripting forthe Semantic Web.
[6] J. M. Bland and D. G. Altman. 1996. Transformations, means, and
confidenceintervals. BMJ312, 7038 (April 1996), 1079.
[7] D. Lenat and R. V. Guha. 1990. Building Large Knowledge-Based
Systems: Representation and Inference in the Cyc Project.
Addison-Wesley.
[8] William W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. 2003.
A Com-parison of String Distance Metrics for Name-Matching Tasks. In
Proceedings of the IJCAI-2003 Workshop on. Acapulco, Mexico, 73–78.
[9] Mathieu d’Aquin, Gabriel Kronberger, and Mari Carmen
Su´arez-Figueroa. 2012. Combining Data Mining and Ontology
Engineering to Enrich Ontologies and Linked Data. InKNOW@LOD,
Vol. 868. CEUR-WS.org, 19–24.
[10] Tom Heath and Christian Bizer. 2011. Linked Data: Evolving the Web
into a Global Data Space (1st ed.). Morgan Claypool.
[11] Maria Koutraki, Nicoleta Preda, and Dan Vodislav. 2016. SOFYA:
Semanticon-the-fly Relation Alignment. In EDBT. OpenProceedings.org,
690–691.
[12] M. Lesk. 1986. Automatic sense disambiguation using machine
readabledictionaries: how to tell a pine cone from an ice cream cone. In
Proceedings of ACM SIGDOC Conference. Toronto, Ontario, 24–26.
[13] V. Levenshtein. 1966. Binary Codes Capable of Correcting Deletions,
Insertions, and Reversals. Soviet Physics-Doklady10, 8 (1966), 707–710.
[14] Juan Antonio Lossio-Ventura, Mathieu Roche, Cl´ement Jonquet, and
Maguelonne Teisseire. 2016. A Way to Automatically Enrich Biomedical
Ontologies. In EDBT, OpenProceedings.org, 676–677.
[15] Alistair Miles, Brian Matthews, Michael Wilson, and Dan Brickley.
2005. SKOScore: Simple Knowledge Organisation for the Web. In Proc.
of the 2005 international conference on DC and metadata applications.
Madrid, Spanien, 1–9.
[16] Markus Nentwig, Michael Hartung, Axel-Cyrille Ngonga Ngomo, and
Erhard Rahm. 2015. A survey of current Link Discovery frameworks.
Semantic WebPreprint (2015), 1–18.
[17] Jacco van Ossenbruggen, Michiel Hildebrand, and Viktor de Boer. 2011.
Interactive Vocabulary Alignment. InTPDL (Lecture Notes in Computer
Science), Vol. 6966. Springer, 296–307.
[18] Ziqi Zhang, Anna Lisa Gentile, Eva Blomqvist, Isabelle Augenstein,
and FabioCiravegna. 2013. Statistical Knowledge Patterns: Identifying
Synonymous Re-lations in Large Linked Datasets. In International
Semantic Web Conference(1), Vol. 8218. Springer, 703–719.
[19] Ricardo A´ vila, Saloma˜o Santos, David Arau´jo, Vaˆnia Maria Ponte
Vidal, and Jos´e Antˆonio Fernandes de Macˆedo. 2017. Semantic Links
Using SKOS Predicates. In KES (Procedia Computer Science), Vol. 112.
Elsevier, 467–473.
@article{"International Journal of Information, Control and Computer Sciences:78890", author = "Ricardo Avila and Gabriel Lopes and Vania Vidal and Jose Macedo", title = "Discovering Semantic Links Between Synonyms, Hyponyms and Hypernyms", abstract = "This proposal aims for semantic enrichment between
glossaries using the Simple Knowledge Organization System (SKOS)
vocabulary to discover synonyms, hyponyms and hyperonyms
semiautomatically, in Brazilian Portuguese, generating new semantic
relationships based on WordNet. To evaluate the quality of this
proposed model, experiments were performed by the use of two sets
containing new relations, being one generated automatically and the
other manually mapped by the domain expert. The applied evaluation
metrics were precision, recall, f-score, and confidence interval. The
results obtained demonstrate that the applied method in the field of
Oil Production and Extraction (E&P) is effective, which suggests that
it can be used to improve the quality of terminological mappings.
The procedure, although adding complexity in its elaboration, can be
reproduced in others domains.", keywords = "Ontology matching, mapping enrichment, semantic
web, linked data, SKOS.", volume = "13", number = "6", pages = "341-6", }
