2021, Número 1
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Revista Cubana de Información en Ciencias de la Salud (ACIMED) 2021; 32 (1)
ANCORP: Procedimiento para la recuperación de información en sistemas de bibliotecas mediante grafos de conocimiento
Senso RJA, Leiva MAA, Rosell LY, Hernández QAR
Idioma: Español
Referencias bibliográficas: 47
Paginas: 1-34
Archivo PDF: 994.53 Kb.
RESUMEN
Las bibliotecas y los centros de documentación carecen de una guía metodológica para transformar sus datos RDF en grafos de conocimiento, lo que impide que puedan aprovechar las facilidades de esta herramienta en la búsqueda y recuperación de información. El artículo propone una metodología para la transformación de datos bibliográficos en grafos de conocimiento. Se presenta ANCORP, a partir del análisis de las técnicas de incrustación, limpieza y chequeo de grafos de conocimiento. Esta metodología se divide en dos partes: la parte 1, dedicada a la construcción del grafo de conocimiento, y la parte 2, dedicada a resolver los procesos de recuperación de información. Con la implementación de la metodología se corroboran saltos cualitativos en la recuperación de información y en la calidad de los datos.
REFERENCIAS (EN ESTE ARTÍCULO)
Fan M, Zhou Q, Zheng TF, Grishman R. Distributed representation learning for knowledge graphs with entity descriptions. Pat Recogn Let. 2017;93:31-7.
Faralli S, Finocchi I, Ponzetto SP, Velardi P. CrumbTrail: An efficient methodology to reduce multiple inheritance in knowledge graphs. Knowl Bas Syst. 2018;151:180-97.
Faralli S, Panchenko A, Biemann C, Ponzetto SP, editors. Linked disambiguated distributional semantic networks. International Semantic Web Conference. Springer; 2016
Qiao B, Fang K, Chen Y, Zhu X. Building thesaurus-based knowledge graph based on schema layer. Clust Comp. 2017;20(1):81-91
Maia A, Lopes JB, Martins P, Pessoa T. Authoring tools as instruments for a new approach of educational planning. In: Chova LG, Martínez AL, Torres IC, editors. INTED: 9th International Technology, Education and Development Conference. INTED Proceedings; 2015. p. 5149-58.
Suárez-Figueroa MC. NeOn Methodology for building ontology networks: specification, scheduling and reuse [Tesis Doctoral]. Universidad Politécnica de Madrid; 2010.
Lesnikova T, David J, Euzenat J, editors. Cross-lingual RDF thesauri interlinking. 10th International Conference on Language Resources and Evaluation; 2016.
Shekarpour S, Marx E, Auer S, Sheth AP, editors. RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem. Association for the Advancement of Artificial Intelligence; 2017.
Qian X, Hu Y, Pan JC. Research of Chinese Word Knowledge Graph Based on SLPA Algorithm. DEStech Transactions on Engineering and Technology Research. 2017.
Jana A, Goyal P. Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation? ArXiv preprint; 2018.
Nie B, Sun S. Knowledge graph embedding via reasoning over entities, relations, and text. Fut Gener Comp Syst. 2019;91:426-33.
Silva VS, Handschuh S, Freitas A, editors. Recognizing and justifying text entailment through distributional navigation on definition graphs. Thirty-Second AAAI Conference on Artificial Intelligence; 2018.
Martínez-Fernández S, dos Santos PSM, Ayala CP, Franch X, et al. Aggregating empirical evidence about the benefits and drawbacks of software reference architectures. ACM-IEEE International Symposium on Empirical Software Engineering and Measurement; 2015. p. 154-63.
Zhu G, Iglesias CA. Exploiting semantic similarity for named entity disambiguation in knowledge graphs. Exp Syst Appl. 2018;101:8-24.
Wang Z, Zhang J, Feng J, Chen Z, editors. Knowledge graph and text jointly embedding. Proceedings of the conference on empirical methods in natural language processing; 2014.
Wang Z, Zhang J, Feng J, Chen Z, editors. Knowledge Graph Embedding by Translating on Hyperplanes. Association for the Advancement of Artificial Intelligence; 2014.
Guo S, Wang Q, Wang B, Wang L, Guo L, editors. Semantically smooth knowledge graph embedding. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing; 2015.
Lin Y, Liu Z, Sun M, Liu Y, Zhu X, editors. Learning entity and relation embeddings for knowledge graph completion. AAAI; 2015.
Jia Y, Wang Y, Jin X, Cheng X. Path-specific knowledge graph embedding. Knowl Bas Syst. 2018;151:37-44.
Chen J, Chen Y, Zhang X, Du X, Wang K, Wen JR. Entity set expansion with semantic features of knowledge graphs. J Web Sem. 2018;52:33-44.
Tonon A, Catasta M, Prokofyev R, Demartini G, Aberer K, Cudre-Mauroux P. Contextualized ranking of entity types based on knowledge graphs. Web Sem Sci Serv Agen World Wide Web. 2016;37:170-83.
Dou J, Qin J, Jin Z, Li Z. Knowledge graph based on domain ontology and natural language processing technology for Chinese intangible cultural heritage. J Vis Lang Comp. 2018;48:19-28.
Achichi M, Lisena P, Todorov K, Troncy R, Delahousse J, editors. DOREMUS: A graph of linked musical works. International Semantic Web Conference; 2018.
de Boer V, Melgar L, Inel O, Ortiz CM, Aroyo L, Oomen J, editors. Enriching media collections for event-based exploration. Research Conference on Metadata and Semantics Research; 2017.
Shan Y, Li M, Chen Y. Constructing target-aware results for keyword search on knowledge graphs. Data Knowl Engin. 2017;110:1-23.
Arnaout H, Elbassuoni S. Effective searching of RDF knowledge graphs. J Web Sem. 2018;48:66-84.
Mantle M, Batsakis S, Antoniou G. Large scale distributed spatio-temporal reasoning using real-world knowledge graphs. Knowl Bas Syst. 2019;163:214-26.
Rodríguez Perojo K, Leyva Mederos AA, Senso Ruíz JA. Marco procedimental para facilitar la interoperabilidad en el contexto de la Biblioteca Virtual en Salud de Cuba: el modelo Ontomed. Rev Cubana Inform Cienc Salud. 2016;27(4):456-73.
Southwick SB. A guide for transforming digital collections metadata into linked data using open source technologies. J Libr Metad. 2015;15(1):1-35.
Cofield MC, Marchock A, Melanson D, Ringwood A. BIBFRAME beginnings: educating ourselves for the linked data future. UT Faculty/Researcher Works; 2017.
Jin Q, Hahn JF, Croll G. BIBFRAME transformation for enhanced discovery. Libr Resour Techn Serv. 2016 [acceso: 28/03/2021];60(4). Disponible en: Disponible en: http://hdl.handle.net/2142/90248
Christen P. A survey of indexing techniques for scalable record linkage and deduplication. IEEE Transact Knowl Data Engin. 2012;25(5).2.
Christen P, editor. An open source data cleaning, duplication and record linkage system with a graphical user interface. Nevada, EE.UU.: Proceedings of the 14th ACM SIGKDD International; 2008.
Baxter R, Christen P, Churches T, editors. A comparison of fast blocking methods for Record Linkage. Washington DC. ACM KDD'03 workshop on data cleaning, Record linkage and object consolidation; 2003.
Christen P, Goiser K, editors. Quality and complexity measures for data linkage and duplication quality measures in data mining. Berlin Heidelberg: Springer-Verlag 2007.
Bilenko M, Mooney RJ, editors. Adaptive duplicate detection using learnable string similarity measures. Washington, DC: IX ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2003.
Batini C, Scannapieco M. Data and Information Quality Cham. New York: Springer International Publishing; 2016.
Klímek J. DCAT-AP representation of Czech National Open Data Catalog and its impact. J Web Sem; 2018.
Beek W, Rietveld L, Bazoobandi HR, Wielemaker J, Schlobach S, editors. LOD laundromat: a uniform way of publishing other people's dirty data. International Semantic Web Conference; 2014.
Rietveld L, Beek W, Hoekstra R, Schlobach S. Meta-data for a lot of LOD. Sem Web. 2017;8(6):1067-80.
Heyvaert P, De Meester B, Dimou A, Verborgh R, editors. Declarative Rules for Linked Data Generation at Your Fingertips! European Semantic Web Conference; 2018:
Hidalgo-Delgado Y, Senso JA, Leiva-Mederos A, Hípola P. Gestión de fondos de archivos con datos enlazados y consultas federadas. Rev Esp Docum Cient. 2016;39(3):145.
Michalek T. Implementation of Parser for RDF Data Files [Tesis]. Checoslovaquia: Universidad Ostrava; 2016.
García A, Linaza MT, Franco J, Juaristi M. Methodology for the publication of linked open data from small and medium size DMO. Information and Communication Technologies in Tourism; 2015. p. 183-95.
Bizer C, Jentzsch A, Cyganiak R. State of the LOD Cloud. Berlín: Public Web-page; 2011 [acceso: 28/06/2012]. Disponible en: http://lod-cloud.net/state/
Morato J, Sánchez-Cuadrado S, Ruiz-Robles A, Moreiro-González JA. Visualización y recuperación de información en la web semántica. El Profes Inform. 2014;23(3):2.
Gómez-Romero J, Molina-Solana M, Oehmichen A, Guo Y. Visualizing large knowledge graphs: A performance analysis. Fut Gener Comp Syst. 2018;89:224-38.