Domen Smole's research while affiliated with Research Centre of the Slovenian Academy of Sciences and Arts and other places

Geographic information systems have been applied on the World Wide Web with different approaches and there is a need to recognize how different groups of users conceptualize the domain of geographic space. In our research, we present an attempt to model a semantic reference system in a semantic web by the concept of global geo-ontology. Taxonomy is based on general knowledge representation as physical and conceptual shapes, simultaneously with basic and advanced human activities. Developed ontology includes 588 concepts on five levels and is extendable. Within such a structure, the rules as independence of location, time frame and culture are respected.

Spatial data sources, like the geodetic reference system, administrative spatial units, addresses and topographic maps, serve as a base for geo-referencing to the most of dependant thematic spatial databases. The marketing strategy of the surveying profession towards the users of spatial data infrastructure should be in the design of an integrative semantic reference system to be used within the Semantic Web, or so-called Web 3.0. The main motivation for our research was the representation of possibilities to automate tool development for efficient and more sensible approaches to query information within web-published spatial data. In contemporary research there are several solutions offered as upgrades of basic GIS systems with the knowledge presented in the form of ontologies. Therefore, we are faced with the new generation of GIS technology, which has been named "inteligent GIS". In this article, we present method of modelling the semantic reference system as an application of the ontology of geographic space in the subset of first order predicate calculus. Such a semantic network of geographic space represents the foundation for semantic data analyses and data integration in distributed information systems. Our application is based on the methods of machine learning and use of the Prolog programming language.

Prostorski podatkovni viri, kot so geodetski referenčni sistem, administrativne prostorske enote, naslovi in topografske teme, so v vseh prostorskih podatkovnih zbirkah podlaga za zajem podatkov in položajno umeščanje tematskih podatkov v prostoru. Temeljne strategije geodetske stroke za približevanje potrebam uporabnikov geodetskih podatkov v smislu povezovanja zbirk podatkov so v oblikovanju povezovalnega semantičnega referenčnega sistema v semantičnem spletu ali tako imenovanem spletu 3.0. V tem prispevku so prikazane možnosti za razvoj orodja za enostavnejše in bolj smiselno iskanje ter integracijo na spletu objavljenih prostorskih podatkov. Za rešitev težave je v raziskavah mogoče zaslediti predloge o nadgradnji sedanjih GIS kot sistemov z znanjem, predstavljenim v obliki ontologij. Gre torej za novo generacijo tehnologije GIS, ki jo nekateri imenujejo tudi pametni GIS. Za zdaj takšen GIS obstaja predvsem na teoretični, ne pa tudi na praktični ravni. V tem delu je predstavljena metoda za modeliranje ontologije geografskega prostora v podmnožici predikatnega računa prvega reda. Izdelano semantično omrežje prostora omogoča analize obstoječih zbirk podatkov za namene integracije v okolju porazdeljenih informacijskih sistemov. Naša izvedba temelji na metodah strojnega učenja in uporabi programskega jezika prolog ; Spatial data sources, like the geodetic reference system, administrative spatial units, addresses and topographic maps, serve as a base for geo-referencing to the most of dependant thematic spatial databases. The marketing strategy of the surveying profession towards the users of spatial data infrastructure should be in the design of an integrative semantic reference system to be used within the Semantic Web, or so-called Web 3.0. The main motivation for our research was the representation of possibilities to automate tool development for efficient and more sensible approaches to query information within web-published spatial data. In contemporary research there are several solutions offered as upgrades of basic GIS systems with the knowledge presented in the form of ontologies. Therefore, we are faced with the new generation of GIS technology, which has been named "inteligent GIS". In this article, we present method of modelling the semantic reference system as an application of the ontology of geographic space in the subset of first order predicate calculus. Such a semantic network of geographic space represents the foundation for semantic data analyses and data integration in distributed information systems. Our application is based on the methods of machine learning and use of the Prolog programming language.

In this article we analyze a well-known and extensively researched problem: how to find all datasets, on the one hand, and on the other hand only those that are of value to the user when dealing with a specific spatially oriented task. In analogy with existing approaches to a similar problem from other fields of human endeavor, we call this software solution ‘a spatial data recommendation service.’ In its final version, this service should be capable of matching requests created in the user's mind with the content of the existing datasets, while taking into account the user's preferences obtained from the user's previous use of the service. As a result, the service should recommend a list of datasets best suited to the user's needs. In this regard, we consider metadata, particularly natural language definitions of spatial entities, a crucial piece of the solution. To be able to use this information in the process of matching the user's request with the dataset content, this information must be semantically preprocessed. To automate this task we have applied a machine learning approach. With inductive logic programming (ILP) our system learns rules that identify and extract values for the five most frequent relations/properties found in Slovene natural language definitions of spatial entities. The initially established quality criterion for identifying and extracting information was met in three out of five examples. Therefore we conclude that ILP offers a promising approach to developing an information extraction component of a spatial data recommendation service.

Digital elevation model (DEM) is one of the most important datasets for the greater part of spatial-based studies and research. Generally, a high quality DEM could be used as all-purpose dataset, but unfortunately its production could be very expensive. If we know a nature of application that applies DEM, if our demands for the final result are clear, and if provider of DEM gives enough clear information for usability of DEM to potential user, then the producer can simplify its production or user can adjust the DEM selection. From the user's point of view, the selection of DEM can be greatly simplified if appropriate tools are available.

The objective of this paper is to discuss our methodology for comparing, searching and integrating geographic concepts. Searching for spatially oriented datasets could be illustrated by the complexity of the communication between the producer and user. The common vocabulary consists of a set of concepts describing the geographic space called universal ontology of geographical space (UOGS). We have defined the semantic parameters for measuring semantic similarities within the UOGS semantic framework and described our applicative approach to the similarity analyses of spatial databases. In order to test our results we have implemented the entire vocabulary as a set prolog fact. Following this we also implemented functionality such as the querying mechanism and the simple semantic similarity model, again as a set of prolog clauses. In addition to this, we applied prolog rules for the purpose of extracting semantic information describing geographic concepts and extracting it from natural language texts.

SUMMARY Experiences about solving a spatially-related problem have shown that best solutions and conclusions can be drawn when all kinds of existing spatial data are available. There are situations when data acquire has to be carried out. However, the questions that raises here are: whether the existent geodata is accessible, and whether it represents useful information to the user? This paper outlines some propositions on how to gain wider acceptance and use of geodata among professional and non- professional users. According to it at least two aspects play an important role when considering spatial data popularisation. Firstly, an infrastructure and technology that enables user-friendly access to the geodata has to be provided. In this respect, web technology and solutions based on semantic approaches and ontologies play an important role. Secondly, geodata should be useful to the users to such extent that the users return and take advantage of the geodata and geoservices again and again. In this regard, high quality and up-to-date geodata that would reflect current situation, acquired by utilizing modern and economic spatial data acquiring techniques, should be provided on regular basis.