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Real-time RDF extraction from unstructured data streams

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

Standard

Real-time RDF extraction from unstructured data streams. / Gerber, Daniel; Hellmann, Sebastian; Bühmann, Lorenz et al.
The Semantic Web, ISWC 2013: 12th International Semantic Web Conference, Proceedings. ed. / Harith Alani; Lalana Kagal; Achille Fokoue; Paul Groth; Chris Biemann; Josiane Xavier Parreira; Lora Aroyo; Natasha Noy; Chris Welty; Krzyztof Janowicz. Springer Verlag, 2013. p. 135-150 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8218 LNCS, No. PART 1).

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

Harvard

Gerber, D, Hellmann, S, Bühmann, L, Soru, T, Usbeck, R & Ngonga Ngomo, AC 2013, Real-time RDF extraction from unstructured data streams. in H Alani, L Kagal, A Fokoue, P Groth, C Biemann, JX Parreira, L Aroyo, N Noy, C Welty & K Janowicz (eds), The Semantic Web, ISWC 2013: 12th International Semantic Web Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8218 LNCS, Springer Verlag, pp. 135-150, 12th International Semantic Web Conference, ISWC 2013, Sydney, NSW, New South Wales, Australia, 21.10.13. https://doi.org/10.1007/978-3-642-41335-3_9

APA

Gerber, D., Hellmann, S., Bühmann, L., Soru, T., Usbeck, R., & Ngonga Ngomo, A. C. (2013). Real-time RDF extraction from unstructured data streams. In H. Alani, L. Kagal, A. Fokoue, P. Groth, C. Biemann, J. X. Parreira, L. Aroyo, N. Noy, C. Welty, & K. Janowicz (Eds.), The Semantic Web, ISWC 2013: 12th International Semantic Web Conference, Proceedings (pp. 135-150). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8218 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-642-41335-3_9

Vancouver

Gerber D, Hellmann S, Bühmann L, Soru T, Usbeck R, Ngonga Ngomo AC. Real-time RDF extraction from unstructured data streams. In Alani H, Kagal L, Fokoue A, Groth P, Biemann C, Parreira JX, Aroyo L, Noy N, Welty C, Janowicz K, editors, The Semantic Web, ISWC 2013: 12th International Semantic Web Conference, Proceedings. Springer Verlag. 2013. p. 135-150. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-41335-3_9

Bibtex

@inbook{bd4458c832904167a7a7c449e3f0beb6,
title = "Real-time RDF extraction from unstructured data streams",
abstract = "The vision behind the Web of Data is to extend the current document-oriented Web with machine-readable facts and structured data, thus creating a representation of general knowledge. However, most of the Web of Data is limited to being a large compendium of encyclopedic knowledge describing entities. A huge challenge, the timely and massive extraction of RDF facts from unstructured data, has remained open so far. The availability of such knowledge on the Web of Data would provide significant benefits to manifold applications including news retrieval, sentiment analysis and business intelligence. In this paper, we address the problem of the actuality of the Web of Data by presenting an approach that allows extracting RDF triples from unstructured data streams. We employ statistical methods in combination with deduplication, disambiguation and unsupervised as well as supervised machine learning techniques to create a knowledge base that reflects the content of the input streams. We evaluate a sample of the RDF we generate against a large corpus of news streams and show that we achieve a precision of more than 85%.",
keywords = "Informatics, Time Slice, Name Entry Recognition, Pattern Mapping, Link Open Data, String Similarity, Business informatics",
author = "Daniel Gerber and Sebastian Hellmann and Lorenz B{\"u}hmann and Tommaso Soru and Ricardo Usbeck and {Ngonga Ngomo}, {Axel Cyrille}",
year = "2013",
doi = "10.1007/978-3-642-41335-3_9",
language = "English",
isbn = "9783642413346",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "135--150",
editor = "Harith Alani and Lalana Kagal and Achille Fokoue and Paul Groth and Chris Biemann and Parreira, {Josiane Xavier} and Lora Aroyo and Natasha Noy and Chris Welty and Krzyztof Janowicz",
booktitle = "The Semantic Web, ISWC 2013",
address = "Germany",
note = "12th International Semantic Web Conference, ISWC 2013 ; Conference date: 21-10-2013 Through 25-10-2013",
url = "http://iswc2013.semanticweb.org",

}

RIS

TY - CHAP

T1 - Real-time RDF extraction from unstructured data streams

AU - Gerber, Daniel

AU - Hellmann, Sebastian

AU - Bühmann, Lorenz

AU - Soru, Tommaso

AU - Usbeck, Ricardo

AU - Ngonga Ngomo, Axel Cyrille

PY - 2013

Y1 - 2013

N2 - The vision behind the Web of Data is to extend the current document-oriented Web with machine-readable facts and structured data, thus creating a representation of general knowledge. However, most of the Web of Data is limited to being a large compendium of encyclopedic knowledge describing entities. A huge challenge, the timely and massive extraction of RDF facts from unstructured data, has remained open so far. The availability of such knowledge on the Web of Data would provide significant benefits to manifold applications including news retrieval, sentiment analysis and business intelligence. In this paper, we address the problem of the actuality of the Web of Data by presenting an approach that allows extracting RDF triples from unstructured data streams. We employ statistical methods in combination with deduplication, disambiguation and unsupervised as well as supervised machine learning techniques to create a knowledge base that reflects the content of the input streams. We evaluate a sample of the RDF we generate against a large corpus of news streams and show that we achieve a precision of more than 85%.

AB - The vision behind the Web of Data is to extend the current document-oriented Web with machine-readable facts and structured data, thus creating a representation of general knowledge. However, most of the Web of Data is limited to being a large compendium of encyclopedic knowledge describing entities. A huge challenge, the timely and massive extraction of RDF facts from unstructured data, has remained open so far. The availability of such knowledge on the Web of Data would provide significant benefits to manifold applications including news retrieval, sentiment analysis and business intelligence. In this paper, we address the problem of the actuality of the Web of Data by presenting an approach that allows extracting RDF triples from unstructured data streams. We employ statistical methods in combination with deduplication, disambiguation and unsupervised as well as supervised machine learning techniques to create a knowledge base that reflects the content of the input streams. We evaluate a sample of the RDF we generate against a large corpus of news streams and show that we achieve a precision of more than 85%.

KW - Informatics

KW - Time Slice

KW - Name Entry Recognition

KW - Pattern Mapping

KW - Link Open Data

KW - String Similarity

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84891950965&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/5d304550-be6f-361f-8bc5-05940fd2117e/

U2 - 10.1007/978-3-642-41335-3_9

DO - 10.1007/978-3-642-41335-3_9

M3 - Article in conference proceedings

AN - SCOPUS:84891950965

SN - 9783642413346

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 135

EP - 150

BT - The Semantic Web, ISWC 2013

A2 - Alani, Harith

A2 - Kagal, Lalana

A2 - Fokoue, Achille

A2 - Groth, Paul

A2 - Biemann, Chris

A2 - Parreira, Josiane Xavier

A2 - Aroyo, Lora

A2 - Noy, Natasha

A2 - Welty, Chris

A2 - Janowicz, Krzyztof

PB - Springer Verlag

T2 - 12th International Semantic Web Conference, ISWC 2013

Y2 - 21 October 2013 through 25 October 2013

ER -

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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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