publication . Preprint . Conference object . 2020

Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs

Elena Demidova; Simon Gottschalk; Tarcísio Souza Costa;
Open Access English
  • Published: 19 Oct 2020
Abstract
Semantic Question Answering (QA) is a crucial technology to facilitate intuitive user access to semantic information stored in knowledge graphs. Whereas most of the existing QA systems and datasets focus on entity-centric questions, very little is known about these systems' performance in the context of events. As new event-centric knowledge graphs emerge, datasets for such questions gain importance. In this paper, we present the Event-QA dataset for answering event-centric questions over knowledge graphs. Event-QA contains 1000 semantic queries and the corresponding English, German and Portuguese verbalizations for EventKG - an event-centric knowledge graph wit...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Information retrieval, Semantic information, Portuguese, language.human_language, language, Computer science, Knowledge graph, Question answering, German
Related Organizations
18 references, page 1 of 2

1. M. Dubey and et al. LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. In ISWC 2019, Cham, 2019.

2. S. Gottschalk and E. Demidova. EventKG: A Multilingual Event-Centric Temporal Knowledge Graph. In Proc. of the ESWC 2018. Springer, 2018. [OpenAIRE]

3. S. Gottschalk et al. Towards Better Understanding Researcher Strategies in CrossLingual Event Analytics. In TPDL, 2018.

4. K. Ho ner et al. Survey on Challenges of Question Answering in the Semantic Web. Semantic Web, 8(6):895{920, 2017.

5. Z. Jia, A. Abujabal, R. Saha Roy, J. Strotgen, and G. Weikum. TempQuestions: A Benchmark for Temporal Question Answering. In The Web Conference, 2018. [OpenAIRE]

6. K. Leetaru and P. A. Schrodt. GDELT: Global Data on Events, Location, and Tone, 1979-2012. In ISA annual convention, volume 2, pages 1{49. Citeseer, 2013.

7. J. Lehmann et al. DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web, 6(2), 2015.

8. A.-C. N. Ngomo et al. Sorry, I don't Speak SPARQL: Translating SPARQL Queries into Natural Language". In WWW, 2013. [OpenAIRE]

9. T. Rebele et al. YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames. In Proc. of the ISWC'16, 2016. [OpenAIRE]

10. R. Rogers. Digital Methods. MIT Press, 2013.

11. M. Rospocher et al. Building Event-Centric Knowledge Graphs from News. J. Web Sem., 37-38:132{151, 2016.

12. E. Saquete et al. Enhancing QA Systems with Complex Temporal Question Processing Capabilities. J. Artif. Intell. Res., 35:775{811, 2009.

13. S. Shekarpour et al. Question Answering on Linked Data: Challenges and Future Directions. In Companion Proc. of the WWW'16, 2016. [OpenAIRE]

14. A. Talmor and J. Berant. The web as a knowledge-base for answering complex questions. CoRR, abs/1803.06643, 2018.

15. P. Trivedi et al. LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs. In Proc. of the ISWC'17, 2017.

18 references, page 1 of 2
Abstract
Semantic Question Answering (QA) is a crucial technology to facilitate intuitive user access to semantic information stored in knowledge graphs. Whereas most of the existing QA systems and datasets focus on entity-centric questions, very little is known about these systems' performance in the context of events. As new event-centric knowledge graphs emerge, datasets for such questions gain importance. In this paper, we present the Event-QA dataset for answering event-centric questions over knowledge graphs. Event-QA contains 1000 semantic queries and the corresponding English, German and Portuguese verbalizations for EventKG - an event-centric knowledge graph wit...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Information retrieval, Semantic information, Portuguese, language.human_language, language, Computer science, Knowledge graph, Question answering, German
Related Organizations
18 references, page 1 of 2

1. M. Dubey and et al. LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. In ISWC 2019, Cham, 2019.

2. S. Gottschalk and E. Demidova. EventKG: A Multilingual Event-Centric Temporal Knowledge Graph. In Proc. of the ESWC 2018. Springer, 2018. [OpenAIRE]

3. S. Gottschalk et al. Towards Better Understanding Researcher Strategies in CrossLingual Event Analytics. In TPDL, 2018.

4. K. Ho ner et al. Survey on Challenges of Question Answering in the Semantic Web. Semantic Web, 8(6):895{920, 2017.

5. Z. Jia, A. Abujabal, R. Saha Roy, J. Strotgen, and G. Weikum. TempQuestions: A Benchmark for Temporal Question Answering. In The Web Conference, 2018. [OpenAIRE]

6. K. Leetaru and P. A. Schrodt. GDELT: Global Data on Events, Location, and Tone, 1979-2012. In ISA annual convention, volume 2, pages 1{49. Citeseer, 2013.

7. J. Lehmann et al. DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web, 6(2), 2015.

8. A.-C. N. Ngomo et al. Sorry, I don't Speak SPARQL: Translating SPARQL Queries into Natural Language". In WWW, 2013. [OpenAIRE]

9. T. Rebele et al. YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames. In Proc. of the ISWC'16, 2016. [OpenAIRE]

10. R. Rogers. Digital Methods. MIT Press, 2013.

11. M. Rospocher et al. Building Event-Centric Knowledge Graphs from News. J. Web Sem., 37-38:132{151, 2016.

12. E. Saquete et al. Enhancing QA Systems with Complex Temporal Question Processing Capabilities. J. Artif. Intell. Res., 35:775{811, 2009.

13. S. Shekarpour et al. Question Answering on Linked Data: Challenges and Future Directions. In Companion Proc. of the WWW'16, 2016. [OpenAIRE]

14. A. Talmor and J. Berant. The web as a knowledge-base for answering complex questions. CoRR, abs/1803.06643, 2018.

15. P. Trivedi et al. LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs. In Proc. of the ISWC'17, 2017.

18 references, page 1 of 2
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