Literatura académica sobre el tema "RDF-To-Text"
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Artículos de revistas sobre el tema "RDF-To-Text"
Chellali, Mustapha y Nader Jafari Rad. "Trees with independent Roman domination number twice the independent domination number". Discrete Mathematics, Algorithms and Applications 07, n.º 04 (diciembre de 2015): 1550048. http://dx.doi.org/10.1142/s1793830915500482.
Texto completoGryaznov, Yevgeny y Pavel Rusakov. "Analysis of RDF Syntaxes for Semantic Web Development". Applied Computer Systems 18, n.º 1 (1 de diciembre de 2015): 33–42. http://dx.doi.org/10.1515/acss-2015-0017.
Texto completoMeddah, Nacéra y Mustapha Chellali. "Roman domination and 2-independence in trees". Discrete Mathematics, Algorithms and Applications 09, n.º 02 (abril de 2017): 1750023. http://dx.doi.org/10.1142/s1793830917500239.
Texto completoSamodivkin, Vladimir. "Roman domination in graphs: The class ℛUV R". Discrete Mathematics, Algorithms and Applications 08, n.º 03 (agosto de 2016): 1650049. http://dx.doi.org/10.1142/s179383091650049x.
Texto completoCui, Hong, Kenneth Yang Jiang y Partha Pratim Sanyal. "From text to RDF triple store: An application for biodiversity literature". Proceedings of the American Society for Information Science and Technology 47, n.º 1 (noviembre de 2010): 1–2. http://dx.doi.org/10.1002/meet.14504701415.
Texto completoKhoeilar, R. y S. M. Sheikholeslami. "Rainbow reinforcement numbers in digraphs". Asian-European Journal of Mathematics 10, n.º 01 (marzo de 2017): 1750004. http://dx.doi.org/10.1142/s1793557117500048.
Texto completoDosso, Dennis y Gianmaria Silvello. "Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System". IEEE Access 8 (2020): 14089–111. http://dx.doi.org/10.1109/access.2020.2966823.
Texto completoDong, Ngan T. y Lawrence B. Holder. "Natural Language Generation from Graphs". International Journal of Semantic Computing 08, n.º 03 (septiembre de 2014): 335–84. http://dx.doi.org/10.1142/s1793351x14500068.
Texto completoDevi, Runumi, Deepti Mehrotra y Hajer Baazaoui-Zghal. "RDF Model Generation for Unstructured Dengue Patients' Clinical and Pathological Data". International Journal of Information System Modeling and Design 10, n.º 4 (octubre de 2019): 71–89. http://dx.doi.org/10.4018/ijismd.2019100104.
Texto completoMountantonakis, Michalis y Yannis Tzitzikas. "Linking Entities from Text to Hundreds of RDF Datasets for Enabling Large Scale Entity Enrichment". Knowledge 2, n.º 1 (24 de diciembre de 2021): 1–25. http://dx.doi.org/10.3390/knowledge2010001.
Texto completoTesis sobre el tema "RDF-To-Text"
Faille, Juliette. "Data-Based Natural Language Generation : Evaluation and Explainability". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0305.
Texto completoRecent Natural Language Generation (NLG) models achieve very high average performance. Their output texts are generally grammatically and syntactically correct which makes them sound natural. Though the semantics of the texts are right in most cases, even the state-of-the-art NLG models still produce texts with partially incorrect meanings. In this thesis, we propose evaluating and analyzing content-related issues of models used in the NLG tasks of Resource Description Framework (RDF) graphs verbalization and conversational question generation. First, we focus on the task of RDF verbalization and the omissions and hallucinations of RDF entities, i.e. when an automatically generated text does not mention all the input RDF entities or mentions other entities than those in the input. We evaluate 25 RDF verbalization models on the WebNLG dataset. We develop a method to automatically detect omissions and hallucinations of RDF entities in the outputs of these models. We propose a metric based on omissions or hallucination counts to quantify the semantic adequacy of the NLG models. We find that this metric correlates well with what human annotators consider to be semantically correct and show that even state-of-the-art models are subject to omissions and hallucinations. Following this observation about the tendency of RDF verbalization models to generate texts with content-related issues, we propose to analyze the encoder of two such state-of-the-art models, BART and T5. We use the probing explainability method and introduce two probing classifiers (one parametric and one non-parametric) to detect omissions and distortions of RDF input entities in the embeddings of the encoder-decoder models. We find that such probing classifiers are able to detect these mistakes in the encodings, suggesting that the encoder of the models is responsible for some loss of information about omitted and distorted entities. Finally, we propose a T5-based conversational question generation model that in addition to generating a question based on an input RDF graph and a conversational context, generates both a question and its corresponding RDF triples. This setting allows us to introduce a fine-grained evaluation procedure automatically assessing coherence with the conversation context and the semantic adequacy with the input RDF. Our contributions belong to the fields of NLG evaluation and explainability and use techniques and methodologies from these two research fields in order to work towards providing more reliable NLG models
Capítulos de libros sobre el tema "RDF-To-Text"
Rezk, Martín, Jungyeul Park, Yoon Yongun, Kyungtae Lim, John Larsen, YoungGyun Hahm y Key-Sun Choi. "Korean Linked Data on the Web: Text to RDF". En Semantic Technology, 368–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37996-3_31.
Texto completoDraicchio, Francesco, Aldo Gangemi, Valentina Presutti y Andrea Giovanni Nuzzolese. "FRED: From Natural Language Text to RDF and OWL in One Click". En Advanced Information Systems Engineering, 263–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41242-4_36.
Texto completoRazia y Tanwir Uddin Haider. "Natural Language Text to RDF Schema Conversion and OWL Mapping for an e-Recruitment Domain". En Algorithms for Intelligent Systems, 49–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4862-2_5.
Texto completoZheng, Yuan, Olli Seppänen, Sebastian Seiß y Jürgen Melzner. "Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval". En CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 751–60. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.75.
Texto completoZheng, Yuan, Olli Seppänen, Sebastian Seiß y Jürgen Melzner. "Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval". En CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 751–60. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.75.
Texto completo"Path-Based Approximate Matching of Spatiotemporal RDF Data". En Advances in Systems Analysis, Software Engineering, and High Performance Computing, 81–101. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9108-9.ch005.
Texto completoNunes, Ronnie Carlos Tavares, Márcio Clemes y Rogério Cid Bastos. "Use of public domain knowledge bases: A case study on the RDF language". En UNITING KNOWLEDGE INTEGRATED SCIENTIFIC RESEARCH FOR GLOBAL DEVELOPMENT. Seven Editora, 2023. http://dx.doi.org/10.56238/uniknowindevolp-048.
Texto completoCorcoglioniti, Francesco, Marco Rospocher, Roldano Cattoni, Bernardo Magnini y Luciano Serafini. "Managing Large Volumes of Interlinked Text and Knowledge With the KnowledgeStore". En Innovations, Developments, and Applications of Semantic Web and Information Systems, 32–61. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5042-6.ch002.
Texto completoMartinez-Rodriguez, Jose L., Ivan Lopez-Arevalo, Jaime I. Lopez-Veyna, Ana B. Rios-Alvarado y Edwin Aldana-Bobadilla. "NLP and the Representation of Data on the Semantic Web". En Handbook of Research on Natural Language Processing and Smart Service Systems, 393–426. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4730-4.ch019.
Texto completoStöhr, Mark R., Andreas Günther y Raphael W. Majeed. "ISO 21526 Conform Metadata Editor for FAIR Unicode SKOS Thesauri". En German Medical Data Sciences: Bringing Data to Life. IOS Press, 2021. http://dx.doi.org/10.3233/shti210056.
Texto completoActas de conferencias sobre el tema "RDF-To-Text"
Xiaoyue, Wang y Bai Rujiang. "Applying RDF Ontologies to Improve Text Classification". En 2009 International Conference on Computational Intelligence and Natural Computing (CINC). IEEE, 2009. http://dx.doi.org/10.1109/cinc.2009.115.
Texto completoGao, Hanning, Lingfei Wu, Po Hu y Fangli Xu. "RDF-to-Text Generation with Graph-augmented Structural Neural Encoders". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/419.
Texto completoLi, Ziran, Zibo Lin, Ning Ding, Hai-Tao Zheng y Ying Shen. "Triple-to-Text Generation with an Anchor-to-Prototype Framework". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/523.
Texto completoSalgueiro, Mariana D. A., Veronica dos Santos, André L. C. Rêgo, Daniel S. Guimarães, Edward H. Haeusler, Jefferson B. dos Santos, Marcos V. Villas y Sérgio Lifschitz. "Quem@PUC - A tool to find researchers at PUC-Rio". En Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd_estendido.2021.18169.
Texto completoKanuri, Neelima, Ian R. Grosse, Jack C. Wileden y Wei-Shan Chiang. "Ontologies and Fine-Grained Control Over Sharing of Engineering Modeling Knowledge in a Web Based Engineering Environment". En ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81373.
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