@inproceedings{hopkinson-etal-2018-demand,
title = "Demand-Weighted Completeness Prediction for a Knowledge Base",
author = "Hopkinson, Andrew and
Gurdasani, Amit and
Palfrey, Dave and
Mittal, Arpit",
editor = "Bangalore, Srinivas and
Chu-Carroll, Jennifer and
Li, Yunyao",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
month = jun,
year = "2018",
address = "New Orleans - Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-3025",
doi = "10.18653/v1/N18-3025",
pages = "200--207",
abstract = "In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.",
}
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<abstract>In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.</abstract>
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%0 Conference Proceedings
%T Demand-Weighted Completeness Prediction for a Knowledge Base
%A Hopkinson, Andrew
%A Gurdasani, Amit
%A Palfrey, Dave
%A Mittal, Arpit
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F hopkinson-etal-2018-demand
%X In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.
%R 10.18653/v1/N18-3025
%U https://aclanthology.org/N18-3025
%U https://doi.org/10.18653/v1/N18-3025
%P 200-207
Markdown (Informal)
[Demand-Weighted Completeness Prediction for a Knowledge Base](https://aclanthology.org/N18-3025) (Hopkinson et al., NAACL 2018)
ACL
- Andrew Hopkinson, Amit Gurdasani, Dave Palfrey, and Arpit Mittal. 2018. Demand-Weighted Completeness Prediction for a Knowledge Base. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 200–207, New Orleans - Louisiana. Association for Computational Linguistics.