E. Zimina, J. Nummenmaa, K. Jarvelin, J. Peltonen, K. Stefanidis
{"title":"RDF数据的多语言语法问答","authors":"E. Zimina, J. Nummenmaa, K. Jarvelin, J. Peltonen, K. Stefanidis","doi":"10.1109/PIC.2018.8706310","DOIUrl":null,"url":null,"abstract":"We introduce Multilingual Grammatical Question Answering (MuG-QA), a system for answering questions in the English, German, Italian and French languages over DBpedia. The natural language modelling and parsing is implemented using Grammatical Framework (GF), a grammar formalism having natural support for multilinguality. The question analysis is based on forming an abstract conceptual grammar from the questions, and then using linearisation of the abstract grammar into different languages to parse the questions. Once a natural language question is parsed, the resulting abstract grammar tree is matched with the knowledge base schema and contents to formulate a SPARQL query. A particular strength of our approach is that once the abstract grammar has been designed, implementation for a new concrete language is relatively quick, supposing that the language has basic support in the GF Resource Grammar Library. MuG-QA has been tested with data from the QALD-7 benchmark and showed competitive results.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"441 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"MuG-QA: Multilingual Grammatical Question Answering for RDF Data\",\"authors\":\"E. Zimina, J. Nummenmaa, K. Jarvelin, J. Peltonen, K. Stefanidis\",\"doi\":\"10.1109/PIC.2018.8706310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce Multilingual Grammatical Question Answering (MuG-QA), a system for answering questions in the English, German, Italian and French languages over DBpedia. The natural language modelling and parsing is implemented using Grammatical Framework (GF), a grammar formalism having natural support for multilinguality. The question analysis is based on forming an abstract conceptual grammar from the questions, and then using linearisation of the abstract grammar into different languages to parse the questions. Once a natural language question is parsed, the resulting abstract grammar tree is matched with the knowledge base schema and contents to formulate a SPARQL query. A particular strength of our approach is that once the abstract grammar has been designed, implementation for a new concrete language is relatively quick, supposing that the language has basic support in the GF Resource Grammar Library. MuG-QA has been tested with data from the QALD-7 benchmark and showed competitive results.\",\"PeriodicalId\":236106,\"journal\":{\"name\":\"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)\",\"volume\":\"441 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PIC.2018.8706310\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PIC.2018.8706310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MuG-QA: Multilingual Grammatical Question Answering for RDF Data
We introduce Multilingual Grammatical Question Answering (MuG-QA), a system for answering questions in the English, German, Italian and French languages over DBpedia. The natural language modelling and parsing is implemented using Grammatical Framework (GF), a grammar formalism having natural support for multilinguality. The question analysis is based on forming an abstract conceptual grammar from the questions, and then using linearisation of the abstract grammar into different languages to parse the questions. Once a natural language question is parsed, the resulting abstract grammar tree is matched with the knowledge base schema and contents to formulate a SPARQL query. A particular strength of our approach is that once the abstract grammar has been designed, implementation for a new concrete language is relatively quick, supposing that the language has basic support in the GF Resource Grammar Library. MuG-QA has been tested with data from the QALD-7 benchmark and showed competitive results.