General Context-Aware Data Matching and Merging Framework

07/26/2018
by   Slavko Žitnik, et al.
0

Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/07/2020

Data Management for Context-Aware Computing

We envisage future context-aware applications will dynamically adapt the...
09/13/2021

An End-to-end Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source of semantic inf...
04/16/2023

Recognizing Entity Types via Properties

The mainstream approach to the development of ontologies is merging onto...
03/21/2020

Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks

Trust can be defined as a measure to determine which source of informati...
06/10/2022

Machop: an End-to-End Generalized Entity Matching Framework

Real-world applications frequently seek to solve a general form of the E...
01/31/2022

Eris: Measuring discord among multidimensional data sources

Data integration is a classical problem in databases, typically decompos...
10/22/2018

Towards a context-dependent numerical data quality evaluation framework

This paper focuses on numeric data, with emphasis on distinct characteri...

Please sign up or login with your details

Forgot password? Click here to reset