A novel similarity measure for spatial entity resolution based on data granularity model: Managing inconsistencies in place descriptions

Khodizadeh-Nahari, Mohammad; Ghadiri, Nasser; Baraani-Dastjerdi, Ahmad; Sack, Jörg-Rüdiger

doi:10.1007/s10489-020-01959-y

Cited by 11 publications

(1 citation statement)

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“…Textual matching processes largely depend on measuring similarities in the text-based attributes of entities, such as the congruence of place names and the likeness of categories, to determine if multiple source place entities refer to the same real-world location [11][12][13]. Conversely, spatial matching utilizes geometric measures of spatial coordinates, including spatial distances, as significant indicators of match suitability [14][15][16]. For the task of matching place entities, traditional rule-based methods necessitate the selection of matching factors for the computation of similarities, setting thresholds for each to ensure proper alignment.…”

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confidence: 99%

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

He,

Li,

Zhang

2024

IJGI

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Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

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