short-paper

Contextual Entity Disambiguation in Domains with Weak Identity Criteria: Disambiguating Golden Age Amsterdamers

Authors:

Al Idrissou,

Veruska Zamborlini,

Frank Van Harmelen,

Chiara LatronicoAuthors Info & Claims

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

Pages 259 - 262

https://doi.org/10.1145/3360901.3364440

Published: 23 September 2019 Publication History

Get Access

Abstract

Entity disambiguation is a widely investigated topic, and many matching algorithms have been proposed. However, this task has not yet been satisfactorily addressed when the domain of interest provides poor or incomplete data with little discriminating power. In these cases, the use of content fields such as name and date is not enough and the simple use of relations with other entities is not of much help when these related entities also need disambiguation before they can be used. Therefore, we propose an approach for the disambiguation of clustered resources using context (related entities that are also clustered) as evidence for reconciling matched entities. We test the proposed method on datasets of historical records from Amsterdam in the 17th century for which context is available, and we compare the results of the proposed approach to a gold standard generated by three experts, which we make available online. The results show that the proposed approach manages to meaningfully use context for isolating identity sub-clusters with higher quality by eliminating potentially false positive matches.

References

[1]

Manel Achichi, Zohra Bellahsene, and Konstantin Todorov. 2017. Legato results for OAEI 2017. In OM@ ISWC. 146--152.

Google Scholar

[2]

Xin Dong, Alon Halevy, and Jayant Madhavan. 2005. Reference reconciliation in complex information spaces. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data. ACM, 85--96.

Digital Library

Google Scholar

[3]

Daniel Faria, Booma S Balasubramani, Vivek R Shivaprabhu, Isabela Mott, Catia Pesquita, Francisco M Couto, and Isabel F Cruz. 2017. Results of AML in OAEI 2017. In OM@ ISWC. 122--128.

Google Scholar

[4]

Al Idrissou, Frank van Harmelen, and Peter van den Besselaar. 2019. Network Metrics for Assessing the Quality of Entity Resolution Between Multiple Datasets (Invited submission as extension of citeIdrissouEKAW2018, Under Revision, Open Access) . Semantic Web Journal (2019). http://semantic-web-journal.org/content/network-metrics-assessing-quality-entity-resolution-between-multiple-datasets

Google Scholar

[5]

Al Koudous Idrissou, Frank van Harmelen, and Peter van den Besselaar. 2018. Network Metrics for Assessing the Quality of Entity Resolution Between Multiple Datasets. In Knowledge Engineering and Knowledge Management, Catherine Faron Zucker, Chiara Ghidini, Amedeo Napoli, and Yannick Toussaint (Eds.). Springer International Publishing, Cham, 147--162.

Google Scholar

[6]

Ernesto Jiménez-Ruiz, Bernardo Cuenca Grau, and Valerie Cross. 2017. LogMap family participation in the OAEI 2017. In CEUR Workshop Proceedings .

Google Scholar

[7]

Abderrahmane Khiat and Maximilian Mackeprang. 2017. I-Match and OntoIdea results for OAEI 2017. In OM@ ISWC. 135--137.

Google Scholar

[8]

Xinze Lyu, Qingheng Zhang, Wei Hu, Zequn Sun, and Yuzhong Qu. 2017. njuLink: results for instance matching at OAEI 2017. In OM@ ISWC. 158--165.

Google Scholar

[9]

Joe Raad, Wouter Beek, Frank Van Harmelen, Nathalie Pernelle, and Fatiha Sa"is. 2018. Detecting Erroneous Identity Links on the Web using Network Metrics. In ISWC. Springer, 391--407.

Google Scholar

[10]

Hossein Rahmani, Bijan Ranjbar-Sahraei, Gerhard Weiss, and Karl Tuyls. 2016. Entity resolution in disjoint graphs: an application on genealogical data. Intelligent Data Analysis, Vol. 20, 2 (2016), 455--475.

Crossref

Google Scholar

[11]

Shu Rong, Xing Niu, Evan Wei Xiang, Haofen Wang, Qiang Yang, and Yong Yu. 2012. A machine learning approach for instance matching based on similarity metrics. In International Semantic Web Conference. Springer, 460--475.

Digital Library

Google Scholar

[12]

Alieh Saeedi, Eric Peukert, and Erhard Rahm. 2018. Using Link Features for Entity Clustering in Knowledge Graphs. In ESWC. Springer, 576--592.

Google Scholar

Cited By

View all

Idrissou AZamborlini VKuhn T(2022)Documenting the Creation, Manipulation and Evaluation of Links for Reuse and ReproducibilityKnowledge Engineering and Knowledge Management10.1007/978-3-031-17105-5_6(81-96)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17105-5_6

Index Terms

Contextual Entity Disambiguation in Domains with Weak Identity Criteria: Disambiguating Golden Age Amsterdamers
1. Applied computing
  1. Arts and humanities
2. Information systems
  1. Information systems applications
    1. Decision support systems

Recommendations

Location-Aware Named Entity Disambiguation
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Named Entity Disambiguation (NED) and linking has been traditionally evaluated on natural language content that is both well-written and contextually rich. However, many NED approaches display poor performance on text sources that are short and noisy. ...
Context Aware Named Entity Disambiguation
WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

Recently, named entity recognition tools tend to disambiguate recognized named entities on a very detailed level. Instead of elementary types (e.g. Person or Location), they assign concrete identifiers, trying to distinguish even different entities ...
Entity Disambiguation with Linkless Knowledge Bases
WWW '16: Proceedings of the 25th International Conference on World Wide Web

Named Entity Disambiguation is the task of disambiguating named entity mentions in natural language text and link them to their corresponding entries in a reference knowledge base (e.g. Wikipedia). Such disambiguation can help add semantics to plain ...

Comments

Information & Contributors

Information

Published In

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

September 2019

281 pages

ISBN:9781450370080

DOI:10.1145/3360901

General Chairs:
Mayank Kejriwal
University of Southern California Information Sciences Institute, USA
,
Pedro Szekely
University of Southern California Information Sciences Institute, USA
,
Program Chair:
Raphaël Troncy
EURECOM, France

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

NWO Large 2015-2016
RISIS

Conference

K-CAP '19

Sponsor:

SIGAI

K-CAP '19: Knowledge Capture Conference

November 19 - 21, 2019

CA, Marina Del Rey, USA

Acceptance Rates

Overall Acceptance Rate 55 of 198 submissions, 28%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
111
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Idrissou AZamborlini VKuhn T(2022)Documenting the Creation, Manipulation and Evaluation of Links for Reuse and ReproducibilityKnowledge Engineering and Knowledge Management10.1007/978-3-031-17105-5_6(81-96)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17105-5_6

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Location-Aware Named Entity Disambiguation

Context Aware Named Entity Disambiguation

Entity Disambiguation with Linkless Knowledge Bases