---
_id: '29032'
abstract:
- lang: eng
  text: Large amounts of geo-spatial information have been made available with the
    growth of the Web of Data. While discovering links between resources on the Web
    of Data has been shown to be a demanding task, discovering links between geo-spatial
    resources proves to be even more challenging. This is partly due to the resources
    being described by the means of vector geometry. Especially, discrepancies in
    granularity and error measurements across data sets render the selection of appropriate
    distance measures for geo-spatial resources difficult. In this paper, we survey
    existing literature for point-set measures that can be used to measure the similarity
    of vector geometries. We then present and evaluate the ten measures that we derived
    from literature. We evaluate these measures with respect to their time-efficiency
    and their robustness against discrepancies in measurement and in granularity.
    To this end, we use samples of real data sets of different granularity as input
    for our evaluation framework. The results obtained on three different data sets
    suggest that most distance approaches can be led to scale. Moreover, while some
    distance measures are significantly slower than other measures, distance measure
    based on means, surjections and sums of minimal distances are robust against the
    different types of discrepancies.
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: Sherif M, Ngonga Ngomo A-C. A Systematic Survey of Point Set Distance Measures
    for Link Discovery. <i>Semantic Web Journal</i>. Published online 2017.
  apa: Sherif, M., &#38; Ngonga Ngomo, A.-C. (2017). A Systematic Survey of Point
    Set Distance Measures for Link Discovery. <i>Semantic Web Journal</i>.
  bibtex: '@article{Sherif_Ngonga Ngomo_2017, title={A Systematic Survey of Point
    Set Distance Measures for Link Discovery}, journal={Semantic Web Journal}, author={Sherif,
    Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2017} }'
  chicago: Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “A Systematic Survey of
    Point Set Distance Measures for Link Discovery.” <i>Semantic Web Journal</i>,
    2017.
  ieee: M. Sherif and A.-C. Ngonga Ngomo, “A Systematic Survey of Point Set Distance
    Measures for Link Discovery,” <i>Semantic Web Journal</i>, 2017.
  mla: Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “A Systematic Survey of Point
    Set Distance Measures for Link Discovery.” <i>Semantic Web Journal</i>, 2017.
  short: M. Sherif, A.-C. Ngonga Ngomo, Semantic Web Journal (2017).
date_created: 2021-12-17T10:00:03Z
date_updated: 2023-08-16T09:18:34Z
keyword:
- 2017 group\_aksw slipo sys:relevantFor:infai sys:relevantFor:bis ngonga simba DICE
  sherif geo-distance limes
language:
- iso: eng
publication: Semantic Web Journal
status: public
title: A Systematic Survey of Point Set Distance Measures for Link Discovery
type: journal_article
user_id: '67234'
year: '2017'
...