From Geoportals to Geographic Knowledge Portals

Manfred Mittlböck,Andreas Richter,Bernhard Vockner

doi:10.3390/ijgi2020256

Manfred Mittlböck, Andreas Richter + Show 1 more

Open Access

https://doi.org/10.3390/ijgi2020256

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Abstract

We present the application of Latent Semantic Analysis (LSA) in combination with recommender systems, in order to enhance discovery in geoportals. As a basis for discovery, metadata of spatial data and services, as well as of non-spatial resources, such as documents and scientific papers, is created and registered in the catalogue of the geoportal (semi-)automatically. Links that are not inherent in the data itself are established based on the semantic similarity of its textual content using LSA. This leads to the transition from unstructured data to structured (metadata) information, serving as a basis for the generation of knowledge. The metadata information is integrated into a recommendation system that provides a ranked list showing (1) what other users viewed and (2) the related resources discovered by the LSA workflow as a result. Based on the assumptions that similar texts have something in common and that users are likely to be interested in what other users viewed, recommendations provide a broader, but also more precise, search result; on the one hand, the recommender engine considers additional information; on the other hand, it ranks resources based on the discovery experience of other users and the likeliness of the documents being related to each other.

Highlights

With the emergence of smart devices, distributed and mobile computing, as well as the broadening of application areas for information systems, the transition of our society into an information society is long complete
As we mainly deal with energy-related information in EnerGEO, and all other results we discovered in the literature showed that Latent Semantic Analysis (LSA) is among the best algorithms compared to human beings, we chose
As we face the situation that the spatial domain and geoportals do not have the same mass of users as online stores, we propose to extend the concept of recommendations based on user interactions with semantic text matching results as additional input for the calculation of recommendations

Summary

Introduction

With the emergence of smart devices, distributed and mobile computing, as well as the broadening of application areas for information systems, the transition of our society into an information society is long complete. Ever new requirements and ideas have led to a rapid development of technology that helps us to improve the speed, amount and accuracy of gaining what has become the soil of our economy and lives: data. In this context, it has become a major challenge to find relevant data in that vast and ever-increasing digital universe that in 2011 exceeded 1.8 zettabytes (1.8 × 1012 gigabytes) contained in 500 quadrillion files [2]. It has not been verified, widespread use in literature, as well as the variety of mainstream technologies using location information, prove it plausible that more than 80%

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