Towards a Collaborative Knowledge Discovery System for Enriching Semantic Information About Risks of Geospatial Data

Abstract

ABSTRACT:The aim of this research is to design and implement a knowledge discovery system that facilitates, using a web 2.0 collaborative approach, the identification of new risks of geospatial data misuse based on a contributed knowledge repository fed by application domain experts. [Context/Motivation] This research is motivated by the irregularity of risk analysis efforts and the poor semantic of the collected information about risks. In the context of risk analysis during geospatial database design, the knowledge about risks of geospatial data misuse is typically held by domain application experts. The collection and record of that knowledge are usually considered as optional activities. It is usually performed through face-to-face risk assessment meetings and reports. Such techniques end up by restricting the scope of risk analysis to a set of obvious risks usually already identified. Besides, little consideration is devoted to the storage of risk information in an appropriate format for automatic reasoning and new risk information discovery. As a consequence, many foreseeable risky aspects inherent to the data remain overlooked leading to ill-defined specification and faulty decisions. [Principal ideas/results] In this paper, we present a contributed knowledge discovery system that aims at enriching the semantic information about risks of geospatial data misuse in order to identify foreseeable risks. The proposed web-based system relies on a systematic and more active involvement of users in risk analysis. The approach consists of 1) providing an overview of the related work in the domains of risk analysis within the context of geospatial database design, 2) presenting an ontology-based knowledge discovery system that helps experts in risks identification based on an upper-level risk ontology and on a structured representation of the domain-specific knowledge and, 3) presenting the components of the proposed system architecture and how it may be implemented and used in practice, and finally 4) we conclude by discussing the approach. [Contribution] A major outcome is that the proposed platform can help discovering implicit domain knowledge, and facilitating the identification of foreseeable risks of geospatial data misuse in a way to preventively improve the resulting fitness-for-use.

A Method of Ontology Evolution and Concept Evaluation Based on Knowledge Discovery in the Heavy Haul Railway Risk System

“…[18] used a comprehensive flood ontology with a scalable structure to develop a network-based emergency preparedness and response knowledge system that embodies concepts/rules to update by establishing a number of extensible interfaces. [19] designed a framework for the simultaneous involvement of users and experts in the design process of geospatial data risk identification to avoid risk about improper use of spatial data. [20] used ontology-based weighted data normalized transduction neural fuzzy reasoning to combine personal portraits with existing ontology to establish a personal diabetes risk model, and vice versa [21].…”

Visualisation of Trust and Quality Information for Geospatial Dataset Selection and Use: Drawing Trust Presentation Comparisons with B2C e-Commerce

“…To address issues of geospatial data quality, international organisations, initiatives, and working groups such as the International Organisation for Standardization (ISO) [1], the Open GIS Consortium (OGC) [2], INSPIRE [3], and many more, are actively working to establish, improve and extend geospatial data and metadata standards. Despite the detailed recommendations of standardisation bodies, and despite the existence of formal metadata standards such as ISO 19115:2003, data quality information is, however, often not communicated to users in a consistent and standardised way [4]. While standardisation efforts have significantly improved metadata interoperability, an increasing choice of metadata standards poses a number of unresolved questions: Which standards are best to follow?…”

“…Since metadata standards are mostly focused on data production rather than potential data use and application, a typical metadata document is not sufficient to effectively communicate dataset fitness for purpose to users from a variety of domains and expertise levels [4,6]. Geospatial data users are presented with an increasing choice of data available from various data portals, repositories, and clearinghouses [4]. This means that the intercomparison of dataset quality and the evaluation of a dataset's fitness for purpose can present a major challenge for geospatial data users.…”

“…Researchers argued the case for enriching metadata records with: references to relevant literature (citations information); less formal opinions from the data producers; expert opinions of data quality; and user feedback regarding previous data use [8]. Recent reviews, however, suggest that these recommendations have not yet been put into practice, with no practical means for collating and searching user-focused metadata, added to which many metadata records that are available are incomplete [4,7,9,10].…”