From Geoportals to Geographic Knowledge Portals

Abstract

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.

A Semantic Recommender Engine for Idea Generation Improvement

“…The vector itself can be created using different techniques. One of the most practical is the so called ''bag of words'' method (Vockner et al, 2013). These pre-processing techniques are important since they reduce considerably the number attributes that characterize each document attenuating its dimen sionality (Gonçalves et al, 2010).…”

“…The standard TF-IDF function can be implemented to assign weights to each term in the document and reflect their relative importance. The purpose of such transformation is to identify semantic relationships between terms, and using them to describ e the documents (Vockner et al, 2013).…”

“…At this space, words wh ich are similar in terms of meaning tend to be located close to each other.These methods are actually based on the idea that "words with similar meanings tend to occur in similar contexts " (Altszyler et al, 2016). The process of learning words that are related to each other is based on their statistical co -occurrence together in a context (Vockner et al, 2013). Latent Semantic Analysis is one of the most used methods for word meaning representation.…”

A Spatial Data Infrastructure Integrating Multisource Heterogeneous Geospatial Data and Time Series: A Study Case in Agriculture

“…In [39], an open source package named get-it has been developed to allow practitioners of public authorities and research institutions to easily deploy their GD and to support them in the manual creation of correspondent metadata semantically enriched by exploiting contextual information on the theme, source, author, and organization. Recently, [52] developed (Semi-) Automatic Metadata Extraction Tool extracting information from non-spatial (pdf, doc, and txt) and spatial resources (Shapefiles, Feature Classes (File Geodatabase and Personal Geodatabase), SDE Feature Classes, GRID and TIFF) to promote geoportal applications as geographic knowledge portals. Our proposed semi-automatic metadata creation and deploy workflow has been designed and developed by combining these last two approaches, i.e., by using the tool get-it [39] to create a metadata template for each type of product in a folder, and then by adding some metadata fields by extracting information from the products content such as date of creation and bounding box.…”

Geo-Enrichment and Semantic Enhancement of Metadata Sets to Augment Discovery in Geoportals

“…As already presented by Vockner et al [52], we extended the standard search capabilities of the catalogue component of geoplatform.at with the concept of recommendations in combination with a semantic text matching approach that identifies items that are related to each other. The recommendations are calculated based on either which resources other users viewed together, on the ratings of items and on a tool that calculates the semantic similarity of the resource abstracts with an algorithm called Latent Semantic Analysis (LSA; [53]).…”