Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98270
標題: 在DBpedia中基於圖遍歷的問答系統
Graph Traversal-based Question Answering System over DBpedia
作者: 賴家慧
Jia-Hui Lai
關鍵字: 問答系統;自然語言查詢;重要實體選擇;圖形探索;廣度優先搜尋法;Question Answering System;Natural Language Query;Pivot Selection;Graph Exploration;Breadth First Search
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Lange, 'Question Answering on Linked Data: Challenges and Future Directions,' in Proceedings of the 25th International Conference Companion on World Wide Web, Montréal, Québec, Canada, 2016. [11] K. Höffner, S. Walter, E. Marx, R. Usbeck, J. Lehmann and A.-C. N. Ngomo, 'Survey on Challenges of Question Answering in the Semantic Web,' Semantic Web Journal, vol. 8, no. 6, pp. 895-920, 2017. [12] C. Unger, C. Forascu, V. Lopez, N. Ngomo, Axel-Cyrille, E. Cabrio, P. Cimiano and S. Walter, 'Question Answering over Linked Data (QALD-4),' in Working Notes for CLEF 2014 Conference, Sheffield, United Kingdom, 2014. [13] K. Xu, S. Zhang, Y. Feng and D. Zhao, 'Answering Natural Language Questions via Phrasal Semantic Parsing,' Communications in Computer and Information Science, vol. 496, pp. 333-344, Jan. 2014. [14] V. Lopez, C. Unger, P. Cimiano and E. Motta, 'Evaluating question answering over linked data,' Web Semantics: Science, Services and Agents on the World Wide Web, vol. 21, pp. 3-13, Aug. 2013. [15] D. Damljanovic, M. Agatonovic and H. Cunningham, 'FREyA: An Interactive Way of Querying Linked Data Using Natural Language,' in Proceedings of the 8th international conference on The Semantic Web, Heraklion, Crete, Greece, 2011. [16] V. Lopez, M. Fernández, E. Motta and N. Stieler, 'PowerAqua: supporting users in querying and exploring the Semantic Web content,' Semantic Web, vol. 3, no. 3, pp. 249-265, Aug. 2012. [17] A. Bernstein, E. Kaufmann and C. Kaiser, 'Querying the semantic web with ginseng: A guided input natural language search engine,' in 15th Workshop on Information Technologies and Systems, Las Vegas, NV, 2005. [18] S. Park, H. Shim and G. G. Lee, 'ISOFT at QALD-4: Semantic Similarity-based Question Answering System over Linked Data,' in CLEF(Working Notes), 2014. [19] H.-Y. Li and E. J.-L. Lu, Question Answer System Based on DBpeida to Answering and Creating new Relation, Taichung: National Chung Hsing University Department of Information Management, 2016, p. 29. [20] A. Freitas, J. G. Oliveira, E. Curry, J. Carlos and P. Silva, 'Treo: Combining Entity-Search, Spreading Activation and Semantic Relatedness for Querying Linked Data,' in 1st Workshop on Question Answering over Linked Data (QALD-1) Workshop at 8th Extended Semantic Web Conference, 2011. [21] C. Zhu, K. Ren, X. Liu, H. Wang, Y. Tian and Y. Yu, 'A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia,' in Proceedings of the 5th Joint International Semantic Technology, 2015. [22] H.-Y. Kuo and E. J.-L. Lu, A Natural Language Querying System Based on Semantic Parsing, Taichung: National Chung Hsing University Department of Information Management, 2017, p. 39. [23] S. Park, S. Kown, B. Kim and G. G. Lee, 'ISOFT at QALD-5: Hybrid Question Answering System over Linked Data and Text Data,' in CLEF(Working Notes), 2015. [24] R. Usbeck, A.-C. N. Ngomo, L. Bühmann and C. Unger, 'HAWK – Hybrid Question Answering Using Linked Data,' in Proceedings of the 12th European Semantic Web Conference on The Semantic Web, New York, NY, USA, 2015. [25] P. Cimiano, V. Lopez, C. Unger, E. Cabrio, A.-C. N. Ngomo and S. Walter, 'Multilingual Question Answering over Linked Data (QALD-3): Lab Overview,' in Proceedings of the 4th International Conference on Information Access Evaluation, Valencia, Spain, 2013. [26] C. Unger, A.-C. N. Ngomo and E. Cabrio, '6th Open Challenge on Question Answering over Linked Data (QALD-6),' Semantic Web Challenges, pp. 171-177, May 2016. [27] K. Höffner and J. Lehmann, 'Towards question answering on statistical linked data,' in Proceedings of the 10th International Conference on Semantic Systems, New York, NY, USA, 2014. [28] M.-C. d. Marnee and C. D. Manning, 'Stanford typed dependencies manual,' 2008. [29] L. Zou, R. Huang, H. Wang, J. X. Yu, W. He and D. Zhao, 'Natural language question answering over RDF: a graph data driven approach,' in Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, Utah, USA, 2014. [30] C. Lange, S. Shekarpour and S. Auer, 'The WDAqua ITN: Answering Questions using Web Data,' CoRR abs/1506.04094, 2015. [31] G. M. Mazzeo and C. Zaniolo, 'CANaLI: A System for Answering Controlled Natural Language Questions on RDF Knowledge Bases UCLA CSD Technical Report Number: 160004,' 2016. [32] S. Han, L. Zou, J. X. Yu and D. Zhao, 'Keyword Search on RDF Graphs - A Query Graph Assembly Approach,' Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 227-236, 6 Nov. 2017. [33] R. Beaumont, B. Grau and A.-L. Ligozat, 'SemGraphQA@QALD-5: LIMSI participation at QALD-5@CLEF,' in CLEF 2015 Working Notes Papers, 2015. [34] S. Ruseti, A. Mirea, T. Rebedea and S. Trausan-Matu, 'QAnswer - Enhanced Entity Matching for Question Answering over Linked Data,' in CLEF 2015 Working Notes Papers, Toulouse, France, 2015. [35] C. Unger, C. Forascu, V. Lopez, A.-C. N. Ngomo, E. Cabrio, P. Cimiano and S. Walter, 'Question Answering over Linked Data (QALD-5),' in Working Notes for CLEF 2015 Conference, 2015. [36] H. Shizhu, Z. Yuanzhe, K. Liu and J. Zhao, 'CASIA@V2: A MLN-based question answering system over linked data,' in CLEF 2014 Working Notes Papers, 2014. [37] S. Ferré, 'squall2sparql: a Translator from Controlled English to Full SPARQL 1.1,' in CLEF(Working Notes), 2013. [38] S. He, S. Liu, Y. Chen, G. Zhou, K. Liu and J. Zhao, 'CASIA@QALD-3: A Question Answering System over Linked Data,' in CLEF, 2013. [39] J. Guyonvarch and S. Ferré, 'Scalewelis: a Scalable Query-based Faceted Search System on Top of SPARQL Endpoints,' in CLEF (Working Notes), 2013. [40] C. Giannone, V. Bellomaria and R. Basili, 'A HMM-based approach to question answering against linked data,' in CLEF, 2013. [41] C. Pradel, G. Peyet and O. Haemmerl, 'SWIP at QALD-3: Results, Criticisms and Lesson Learned,' in CLEF, 2013.
摘要: 
本研究開發一個查詢DBpedia的自然語言問答系統,讓使用者的自然語言問題轉換為SPARQL結構查詢關聯資料集,而轉化過程先從問句中辨識出有用的實體,接著計算其數量並依據數量來選擇重要的實體,然後以重要實體為中心重新建樹並移除不需要的字詞而產生一個圖形(Graph),接著根據子圖進行遍歷依序查詢DBpedia,然後會得到一組或多組的triple,再將triples結合其他額外的條件,最後產生完整的SPARQL語法並得到答案。
本研究我們採用QALD-7、QALD-6、QALD-5、QALD-4和QALD-3多語言問題測試資料來評估我們的方法,在完整測試集中,在QALD-7獲得平均精確度為0.19、平均召回率為0.23而平均F-measure為0.20;在QALD-6獲得平均精確度為0.31、平均召回率為0.54而平均F-measure為0.34;在QALD-5獲得平均精確度為0.33、平均召回率為0.43而平均F-measure為0.36;在QALD-4獲得平均精確度為0.25、平均召回率為0.34而平均F-measure為0.24;在QALD-3獲得平均精確度為0.40、平均召回率為0.50而平均F-measure為0.41,並在QALD-3中更進一步在問句類型中,我們採用平均F-measure值比較,在Aggregation得到0.32、List得到0.26而Other為0.48,實驗結果顯示我們方法能解決複雜問句。

We present a natural language question answering system that queries DBpedia to convert user's natural language question into a SPARQL structure queries over linked dataset. First, we identify useful entities from the question, then calculate the quantity and count the quantity to select an pivot from the question sentence, then re-create the tree with pivot and remove the stopwords to produce a graph. According to the sub-graph to traverse and query DBpedia sequentially, and then get one or more sets of triples. Then combine the triples with other additional conditions, finally generate the complete SPARQL syntax and get the answer.
In this paper, we used QALD-7, QALD-6, QALD-5, QALD-4 and QALD-3 multilingual test dataset to evaluate our method. In the complete dataset, we achieve an average precision of 0.19, an average recall of 0.23 and an average F-measure of 0.20 on the QALD-7;an average precision of 0.31, an average recall of 0.54 and an average F-measure of 0.34 on the QALD-6;an average precision of 0.33, an average recall of 0.43 and an average F-measure of 0.36 on the QALD-5;an average precision of 0.25, an average recall of 0.34 and an average F-measure of 0.24 on the QALD-4;an average precision of 0.40, an average recall of 0.50 and an average F-measure of 0.41 on the QALD-3 and further in the query type question on QALD-3, we use an average F-measure to compare and achieve 0.32 on Aggregation, 0.26 on List and 0.48 on Other. Experimental results show that our method can solve complex questions.
URI: http://hdl.handle.net/11455/98270
Rights: 同意授權瀏覽/列印電子全文服務,2019-08-22起公開。
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