Abstract
In this study, we present an original method that enhances geocoding systems in poorly mapped areas thanks to public company data and a multi-agent system. In contrast with industrialized countries, many developing countries lack formal postal address systems assignments and usage, making the operation of translating text-based addresses to absolute spatial coordinates, known as geocoding, a big challenge. We recreated a standard of address as it is perceived and used by local people, a kind of non-official national address standard since there is no official one in these areas. Then, we designed a multi-agent system in which agents are assigned different tasks of geocoding process and can perform negotiation to achieve a global objective: find the best possible match or approximation of a location-based on current knowledge. Verification of the usefulness of the proposed approach is made in comparison with Google Geocoding API which shows that the proposed approach has great potential to geocode addresses considering local context semantic issues.
Highlights
Standardization is [1] but less in what we can call intelligent geocoding accomplished by tokenizing the address string and which deal with geocoding difficult address by using assigning meaning to each token from different control and knowledge improvements [3,5] to depart from address element types
As we draw our conclusions on the result, we find a limited success of this operation [29] due to noncompliance paneling of roads or lack of monitoring and poor maintenance, some great achievements can be recognized such as subdivision of addressing zones and street codification with the SYSCOL system (Système de Codification des Localités – Cities's Codification System)
Keyword Finder Agent (KFA): his objective is to find the different entities of the ontology, called a keyword, present in the provided address and to classify them as described by the knowledge provided by ontology
Summary
From standard service delivery to emergency system dispatching, addresses are the most common and convenient way to locate people. Through their day-to-day interaction with customers to quickly troubleshoot water supply breakdowns or electricity distribution failures, these utility companies need to have a good knowledge of the field they operate in To deal with this permanent challenge, these companies have to create their own geolocation systems that have become over the years an important source of geographical and customer address data. A verbal description of their location is given by the customer to call center officers without any account references This collected data is characterized by its vagueness and full of misspelling or unknown places from common mapping facilities. To express these challenges, we first needed to produce an address standard by text mining historical data and creating a reference dataset before being able to achieve the matching operations.
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