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Enhancing Community Interactions with Data-Driven Chatbots - The DBpedia Chatbot

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Authors

In this demo, we introduce the DBpedia chatbot, a knowledge-graph-driven chatbot designed to optimize community interaction. The bot was designed for integration into community software to facilitate the answering of recurrent questions. Four main challenges were addressed when building the chatbot, namely (1) understanding user queries, (2) fetching relevant information based on the queries, (3) tailoring the responses based on the standards of each output platform (i.e. Web, Slack, Facebook) as well as (4) developing subsequent user interactions with the DBpedia chatbot. With this demo, we will showcase our solutions to these four challenges.

Original languageEnglish
Title of host publicationThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
EditorsPierre-Antoine Champin, Fabien Gandon, Lionel Medini
Number of pages4
PublisherAssociation for Computing Machinery, Inc
Publication date23.04.2018
Pages143-146
ISBN (print)978-1-4503-5640-4
DOIs
Publication statusPublished - 23.04.2018
Externally publishedYes
Event27th International World Wide Web, WWW 2018: Bridging natural and artificial intelligence worldwide - Universität Lyon, Lyon, France
Duration: 23.04.2018 → 27.04.2018
https://archives.iw3c2.org/www2018/

Bibliographical note

This work has been supported by the BMVI projects LIMBO (project no. 19F2029C) and OPAL (project no. 19F20284) as well as by the German Federal Ministry of Education and Research (BMBF) within ’KMU-innovativ: Forschung für die zivile Sicherheit’ in particular ’Forschung für die zivile Sicherheit’ and the project SOLIDE (no. 13N14456).

Publisher Copyright:
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.

Other publications by the same author(s)

ShortPathQA: A Dataset for Controllable Fusion of Large Language Models with Knowledge Graphs

Salnikov, M., Sakhovskiy, A., Nikishina, I., Usmanova, A., Kraft, A., Möller, C., Banerjee, D., Huang, J., Jiang, L., Abdullah, R., Yan, X., Tutubalina, E., Usbeck, R. & Panchenko, A., 2026, Natural Language Processing and Information Systems: 30th International Conference on Applications of Natural Language to Information Systems, NLDB 2025, Proceedings. Ichise, R. (ed.). Springer Science and Business Media Deutschland, p. 95-110 16 p. (Lecture Notes in Computer Science; vol. 15836 LNCS).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Analyzing the Influence of Knowledge Graph Information on Relation Extraction

Möller, C. & Usbeck, R., 2025, The Semantic Web: 22nd European Semantic Web Conference, ESWC 2025 Portoroz, Slovenia, June 1–5, 2025 Proceedings, Part I. Curry, E., Acosta, M., Poveda-Villalón, M., van Erp, M., Ojo, A., Hose, K., Shimizu, C. & Lisena, P. (eds.). Cham: Springer Nature Switzerland AG, Vol. 1. p. 460-480 21 p. (Lecture Notes in Computer Science ; vol. 15718).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Bridge-Generate: Scholarly Hybrid Question Answering

Taffa, T. A. & Usbeck, R., 23.05.2025, WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025: Companion Proceedings of the ACM Web Conference 2025, April 28-May 2, 2025 Sydney, NSW, Australia. Long, G., Blumestein, M., Chang, Y., Lewin-Eytan, L., Huang, H. & Yom-Tov, E. (eds.). New York: Association for Computing Machinery, Inc, p. 1321-1325 5 p.

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Junior fellows and distinguished dissertation of the GI and AI for crisis

Usbeck, R., Kraft, A. & Westphal, P., 01.02.2025, In: IT - Information Technology. 67, 1, p. 1-2 2 p.

Research output: Journal contributions › Other (editorial matter etc.) › Research

Knowledge Graph Question Answering and Large Language Models

Banerjee, D., Hu, N., Tan, Y., Min, D., Wu, Y., Usbeck, R. & Qi, G., 17.03.2025, Handbook on Neurosymbolic AI and Knowledge Graphs. Hitzler, P., Dalal, A., Mahdavinejad, M. S. & Norouzi, S. S. (eds.). IOS Press BV, p. 466-531 66 p. ( Frontiers in Artificial Intelligence and Applications; vol. 400).

Research output: Contributions to collected editions/works › Chapter

DOI

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