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Entity-fishing: A DARIAH Entity Recognition and Disambiguation Service

Authors: Luca Foppiano; Laurent Romary;

Entity-fishing: A DARIAH Entity Recognition and Disambiguation Service

Abstract

International audience; This paper presents an attempt to provide a generic named-entity recognition and disambiguation module (NERD) called entity-fishing as a stable online service that demonstrates the possible delivery of sustainable technical services within DARIAH, the European digital research infrastructure for the arts and humanities. Deployed as part of the national infrastructure Huma-Num in France, this service provides an efficient state-of-the-art implementation coupled with standardised interfaces allowing an easy deployment on a variety of potential digital humanities contexts. Initially developed in the context of the FP9 EU project CENDARI, the software was well received by the user community and continued to be further developed within the H2020 HIRMEOS project where several open access publishers have integrated the service to their collections of published monographs as a means to enhance retrieval and access. entity-fishing implements entity extraction as well as disambiguation against Wikipedia and Wikidata entries. The service is accessible through a REST API which allows easier and seamless integration, language independent and stable convention and a widely used service-oriented architecture (SOA) design. Input and output data are carried out over a query data model with a defined structure providing flexibility to support the processing of partially annotated text or the repartition of text over several queries. The interface implements a variety of functionalities, like language recognition, sentence segmentation and modules for accessing and looking up concepts in the knowledge base. The API itself integrates more advanced contextual parametrisation or ranked outputs, allowing for the resilient integration in various possible use cases. The entity-fishing API has been used as a concrete use case to draft the experimental stand-off proposal, which has been submitted for integration into the TEI guidelines. The representation is also compliant with the Web Annotation Data Model (WADM). In this paper we aim at describing the functionalities of the service as a reference contribution to the subject of web-based NERD services. In this paper, we detail the workflow from input to output and unpack each building box in the processing flow. Besides, with a more academic approach, we provide a transversal schema of the different components taking into account non-functional requirements in order to facilitate the discovery of bottlenecks, hotspots and weaknesses. We also describe the underlying knowledge base, which is set up on the basis of Wikipedia and Wikidata content. We conclude the paper by presenting our solution for the service deployment: how and which the resources where allocated. The service has been in production since Q3 of 2017, and extensively used by the H2020 HIRMEOS partners during the integration with the publishing platforms.

Country
France
Subjects by Vocabulary

Microsoft Academic Graph classification: business.industry Computer science World Wide Web Annotation Software Workflow Knowledge base Software deployment Schema (psychology) Use case Architecture business

Keywords

[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [INFO]Computer Science [cs]

20 references, page 1 of 2

Brando, Carmen, Francesca Frontini, and Jean-Gabriel Ganascia. 2016. “REDEN: Named Entity Linking in Digital Literary Editions Using Linked Data Sets.” Complex Systems Informatics and Modeling Quarterly, no. 7: 60-80. doi:10.7250/csimq.2016- 7.04. [OpenAIRE]

Buddenbohm, Stefan, and Raisa Barthauer. 2017. “D 4.1 - Gap Analysis of DARIAH Research Infrastructure.” DARIAH research report.. https://hal.archivesouvertes.fr/hal-01663594.

Cucerzan, Silviu. 2007. “Large-Scale Named Entity Disambiguation Based on Wikipedia Data.” In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLPCoNLL), 708-16. Stroudsburg, PA: Association for Computational Linguistics. https://www.aclweb.org/anthology/volumes/D07-1/.

Edwards, Paul N. 2003. “Infrastructure and Modernity: Force, Time, and Social Organization in the History of Sociotechnical Systems.” In Modernity and Technology, edited by Thomas J. Misa, Philip Brey, and Andrew Feenberg, 185-225. Cambridge, MA: MIT Press.

Lopez, Patrice. 2009. “GROBID: Combining Automatic Bibliographic Data Recognition and Term Extraction for Scholarship Publications.” In Research and Advanced Technology for Digital Libraries: 13th European Conference, ECDL 2009…: Proceedings, edited by Maristella Agosti, José Borbinha, Sarantos Kapidakis, Christos Papatheodorou, and Giannis Tsakonas, 473-74. Lecture Notes in Computer Science 5714. Berlin, Heidelberg: Springer.

Lopez, Patrice. 2017. “Entity-Fishing.” Slides presented at WikiDataCon 2017, Berlin, Germany, October 28-29. Last revised 8 February 2018, https://www.wikidata.org/wiki/Wikidata:WikidataCon_2017/Documentation; accessed July 11, 2020, https://grobid.s3.amazonaws.com/presentations/29-10- 2017.pdf.

Lopez, Patrice, Alexander Meyer, and Laurent Romary. 2014. “CENDARI Virtual Research Environment & Named Entity Recognition Techniques.” Poster presented at the conference Grenzen überschreiten - Digitale Geisteswissenschaft heute und morgen, Berlin, Germany, February 28, 2014. Einstein-Zirkel Digital Humanities. https://hal.inria.fr/hal-01577975.

Milne, David N., Ian H. Witten, and David M. Nichols. 2007. “Extracting Corpus Specific Knowledge Bases from Wikipedia.” Working paper series, no. 03/2007, Department of Computer Science, University of Waikato, Hamilton, New Zealand. https://hdl.handle.net/10289/69.

Nadeau, David, and Satoshi Sekine. 2007. “A Survey of Named Entity Recognition and Classification.” In Named Entities: Recognition, Classification and Use, edited by Satoshi Sekine and Elisabete Ranchhod [Lingvisticae Investigationes 30:1], 3-26. [Amsterdam and Philadelphia]: John Benjamins. doi:10.1075/li.30.1.03nad.

Pellissier Tanon, Thomas, Denny Vrandečić, Sebastian Schaffert, Thomas Steiner, and Lydia Pintscher. 2016. “From Freebase to Wikidata: The Great Migration.” In WWW '16: Proceedings of the 25th International Conference on World Wide Web, 1419-28. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi:10.1145/2872427.2874809.

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Funded by
EC| HIRMEOS
Project
HIRMEOS
High Integration of Research Monographs in the European Open Science infrastructure
  • Funder: European Commission (EC)
  • Project Code: 731102
  • Funding stream: H2020 | RIA
Related to Research communities
DARIAH EU
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