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Future Internet 2013, 5, 585-590; doi:10. 3390/fi5040585 future internet ISSN 1999-5903 www. mdpi. com/journal/futureinternet Editorial Addressing Semantic Geographic Information Systems Salvatore F. Pileggi * and Robert Amor Department of Computer Science, The Univer sity of Auckland, Private Bag 92019, Auckland 1142, New Zealand; E-Mail: trebor@cs. auckland. ac. nz * Author to whom correspondence should be a ddressed; E-Mail: f. p ileggi@auckland. ac. nz; Tel. : +64-21-2219147. Received: 15 November 2013 / Accepted: 19 November 2013 / Published: 26 November 2013 Abstract: The progressive consolidation of info rmation technologies on a large scale has been facilitating and progressi vely increasing the production, collection, and diffusion of geographic data, as well as f acilitating the integr ation of a large amount of external information into geographic information system s (GIS). Traditional GIS is transforming into a consolidated information infrastructure. This consolidated infrastructure is affecting more and more aspects of inte rnet computing and services. Most popular systems (such as social networks, GPS, and decision support sy stems) involve comple x GIS and significant amounts of information. As a web service, GI S is affected by exactly the same problems that affect the web as a whol e. Therefore, next generation GIS solutions have to address further methodological and data engineeri ng challenges in order to accommodate new applications' extended requirements (in terms of scale, interoperability, and complexity). The conceptual and semantic modeling of GIS, as well as the integration of semantics into current GIS, provide highly expressive envir onments that are capable of meeting the needs and requirements of a wide range of applications. Keywords: semantic technologies; semantic web; geographic information system (GIS); ontology development; geographic space modeling; spatial data infrastructure (SDI) 1. Towards Semantic GIS A geographic information system (GIS) is a func tional and data infrastr ucture designed to perform complex tasks invol ving geographical data, e. g., to capt ure, store, manipulate, analyze, manage, and represent geographically-based data. OPEN ACCESS

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Future Internet 2013, 5 586 When applied to geographically-oriented computer technology and integrated systems, GIS have been generating massive interest worldwide in the c ontext of different domain s and applications [1]. Their intrinsic flexibility and diversity, their multi-disciplinary features combined with their commercial focus, have not assisted in producing a clear and unambiguous definition of GIS [1]. A satisfactory and generic set of domain or applicati on-independent fundamental pr inciples, as well as an exhaustive description and evolution of applications, are hard to convey [1]. Today GIS appears as a core concept in a wide do main of applications in volving any kind of classic geographic data (e. g., sensors [2]), rich contents (e. g., multimedia-contents [3]) as well as complex dynamics (e. g., social content [4,5]). Expectati ons around GIS's capabilitie s and performance are quickly growing. Consequently, GIS technology is c onstantly evolving and is evidencing a progressive and unrelenting convergence w ith web technologies ( Web GIS [6]). Such convergence is much more than a simple tec hnological trend: the current generation of GIS is intrinsically designed and devel oped to work at a global level on the web using web-scale data resources ( Big Data [7]). The scale and the complexity of the information on the internet have led researchers to design the next version of the internet (known as Web 3. 0 or the Semantic Web [8]): the model assumes that published data will be in tegrated with its “meaning” ( i. e., semantic descri ption) through a machine-processable specification. Such integration would potentia lly allow for the processing of contextual information by machines in a context of interoperability and w ith a lack of ambiguity. Semantic processes on the internet are not limited to data, but can al so involve web services. Indeed, Semantic Web Services [9] extend the common web service c oncept by using semantic descriptors (e. g., those regarding modeling, se rvice behavior, and capacity) to perform dynamic tasks. These involve the discovery, matchmaking, and execution of services that ar e supplied by different providers scattered throughout the global network. The next generation GIS is expected to address an extended set of issu es that reflect a new understanding of requirements in terms of scale, interoperabili ty [10] and complexity. Those requirements could propose further relevant trad eoffs and challenges if GISs are understood as integrated systems in the context of upper frameworks (e. g., Spatial Data Infrastructure (SDI) [11]). More than one technologic aspect is evidently invol ved in the GIS evolution but, in practice, if the current development of GIS are naturally suited to the consolidated technological and conceptual environment of the second version of the web ( Social Web [12]), then the next generation GIS should be reasonably designed according to the semantic web model ( Semantic GIS ([13,14]). Here is the core of the problem: regardless of the current availability of semantic technologies [15], semantics is a debatable open research issue, and the understandi ng of the third generation web is a continuously evolving concept [16]. Therefore, semantic GIS has to be designed and develo ped in the context of a not-yet-mature and consolidat ed technological framework. The next subsections deal with the possible impact of semantics on GIS and the main challenges of next generation GIS. Anticipated improvements will also be defined for the most important features of next generation applications and services of GIS.

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Future Internet 2013, 5 587 1. 1. Understanding Semantics of GIS Emerging applications involving GIS introduce strong demands in te rms of performance, capabilities and interoperability affecting most aspect s of the GIS's functional and data infrastructure. Most existing GIS are being re-designed and re-packag ed to address extended requirements, as well as in an attempt to exploit or explore ma rket opportunities as much as possible. This tendency to re-engineering, apart from not always being effective, sometimes causes some confusion in a field already characterized by a gr eat diversity of applications. Furthermore, the inability to accommodate emerging requirements into current models is quite evident in most cases (e. g. [17,18]). The fee ling is that models should evolve in acco rdance with technological environments. In fact, GIS are integrating systems which bring together knowledge and ideas developed in many different areas, as well as knowle dge-intensive processes, complex da ta models and patterns. In this context, most solutions look for a heuristic res ponse to specific problems rather than tangible advances. It is not always easy to evaluate the impact of ad-hoc solutions for a wider range of applications and to generalize a pplication-oriented approaches. Furthermore, there are many different ways to defi ne and classify subjects, objects and relations between them. The target knowledge often exists with multiple perspectives of the information; rich data models [19] are required to represent comp lex information fully supporting further steps for business processes (e. g., analysis). Heterogeneous models from diff erent domains are being produced, but it is difficult to achie ve a convergence process. Such considerations, and the cons tantly increasing systems' scale, are leading researchers to the progressive integration of semantic models and, consequently, to a design approach oriented to the semantic web. The benefits and current limitations of semantics are extensively discussed in the literature (e. g., in [8]) but ar e not the object of discussion in the context of this paper. It is possible to identify a wide range of different approaches in order to apply semantic models to GIS. Such solutions would consistently vary in function of the contex t and the purpose(s). For example, current research solutions can be design ed according to an overall approach (e. g., semantic geospatial web [13]) or could match a completely vertical approach. Hybrid approaches, based on interdependent semantic layers, are currently in deve lopment in the context of several research projects. An analysis of semantic needs for GIS architect ures identifies at least two different kinds of semantic support: Horizontal or Base support mostly refers to the geographic space itself. Current models fundamentally lack formalization which make s finding a high-level ma tch, or definition of consistent relations among the different components, difficult. By providing a rich and formalized set of concepts, an ontological approach (e. g., [ 20,21]) could provide a revolutionary solution for problems of geographical information modeling, en abling semantically interoperable frameworks, spatial data reuse, data sharing and mining, as well as the development of intelligent networks. Vertical support is a natural complement to the previous one since it should basically define a formal interoperable meta-layer for the specification of data layers and releva nt relations. Such data structures should overcome the barri ers affecting the internal mana gement of content inside the GIS, as well as enabling a cons istent data sharing model [22].

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Future Internet 2013, 5 588 1. 2. Issues and Challenges As previously mentioned, semantics could contri bute to GIS progress by pr oviding a wide range of formalizations and relations as an expression of complex conceptualizations involving space and data [23]. The formal definition of semantics targeting a co mplex data and functional infrastructure is a difficult challenge especially if, as for GIS, applic ation domains can be quite different in structure, scale and scope. A mix of generic, domain-specific and application-specific concepts and relations have to work together, requiring st rong efforts in terms of knowledge engineering in order to assure intelligible and usable models. From a strictly product-oriented perspective, the minimal set of challenges to address can be summarized as: Most emerging applications require an innovative understanding of space, overcoming the strongly physical model currently in use. An extended specification of space, including logic views and relations in the context of interoperability and reducing ambiguity, could be extremely helpful for a large number of business processes and applications involving GIS. In recent years, several projects have been proposed in order to combine themes of space and tim e using an ontological approach (e. g., [24]). Semantics are being extensively used in order to define th e contextualization of geospatial information (e. g. [25]). At the same time, an improved model for data management [26] and sharing [22] is sought. Next generation GIS should reflect data ecosystems and not simply federations of data. Rich data models are required to provide capabilities for effective da ta management and sharing. An additional effort is required to manage comp lex data on a large scale (e. g., Open Data [27] and social objects [16]) as well as to provide multi-dimensional perspectiv es of data (such as in semantic similarity measurement [28]). Semantics, able to formalize current models, w ould be an important (and in most cases also comprehensive) result. Looking at ICT architectur es and the speed of their evolution, a more consistent role for semantics is expected in the n ear future, mostly in order to provide a consistent level of interoperability [23,29]. Big Data, mobility, social trends in information [30,31] and all the other phenomena potentia lly involving the web, should provi de a deeper u nderstanding of semantics as an effective n-th dimension of space, enabling creation of innovative models for representation a nd interaction. Conflicts of Interest The authors declare no conflict of interest. References 1. Maguire, D. J. ; Goodchiled, M. ; Rhinds, D. An Overview and Definition of GIS. In Geographical Information Systems: Principals and Applications ; Longman Scientific & Technical: London, UK, 1991; pp. 9-20.

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Future Internet 2013, 5 589 2. Open Geospatial Consortium Inc. OGC® Sensor Web Enablement: Overview and High Level Architecture ; Botts, M., Percivall, G., Reed, C., Davi dson, J., Eds. ; Springer: Berlin, Germany, 2008; pp. 175-190. 3. Soomro, T. R. ; Zheng, K. ; Pan, Y. HTML and Multimedia Web GIS. In Proceedings of the 1999 3rd International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'99), New Delhi, Indi a, 23-26 September 1999; pp. 371-382. 4. Ground Truth: The Social Implications of Geographic Information Systems ; Pickles, J., Ed. ; Guilford Press: New York, NY, USA, 1995. 5. Anselin, L. Spatial Data Analysis with GIS : An Introduction to Application in the Social Sciences ; National Center for Geographic Information and Analysis: Santa Barbara, CA, USA, 1992. 6. Dragicevic, S. The poten tial of web-based GIS. J. Geogr. Syst. 2004, 6, 79-81. 7. Lohr, S. The Age of Big Data. New York Times, 11 February 2012. 8. Berners-Lee, T. ; Hendler, J. ; Lassila, O. The semantic web. Sci. Am. 2001, 284, 28-37. 9. Mc Ilraith, S. A. ; Son, T. C. ; Zeng, H. Semantic web services. IEEE Intell. Syst. 2001, 16, 46-53. 10. Stoimenov, L. ; Đorđević-Kajan, S. Framework for sema ntic GIS interoperability. FACTA Universitatis Ser. Math. Inform. 2002, 17, 107-125. 11. Budhathoki, N. R. ; Nedovic-Budic, Z. Reconceptualiz ing the role of the us er of spatial data infrastructure. Geo Journal 2008, 72, 149-160. 12. O'reilly, T. What is Web 2. 0: Design patterns a nd business models for the next generation of software. Commun. Strateg. 2007, 65, 17-37. 13. Egenhofer, M. J. Toward the Semantic Geos patial Web. In Proceedin gs of the 10th ACM International Symposium on Advances in Geogra phic Information Systems, Mc Lean, VA, USA, 8-9 November 2002. 14. Cruz, I. F. ; Rajendran, A. ; Sunna, W. Handling Semantic Heterogeneities Using Declarative Agreements. In Proceedings of the 10th AC M International Symposium on Advances in Geographic Information Systems, Mc Lean, VA, USA, 8-9 November 2002. 15. Hendler, J. Web 3. 0 emerging. Computer 2009, 42, 111-113. 16. Pileggi, S. F. ; Fernandez-Llatas, C. ; Traver, V. When the social meets the semantic: Social semantic Web or Web 2. 5. Future Internet 2012, 4, 852-864. 17. Ellul, C. ; Haklay, M. Requirements for topology in 3D GIS. Trans. GIS 2006, 10, 157-175. 18. Waters, J. ; Powers, B. J. ; Ceruti, M. G. Global inter operability using semantic s, standards, science and technology (GIS3T). Comput. Stand. Interfaces 2009, 31, 1158-1166. 19. Samet, H. The Design and Analysis of Spatial Data Structures ; Addison-Wesley: Boston, MA, USA, 1990; Volume 199. 20. Hiramatsu, K. ; Reitsma, F. Geo Referencing th e Semantic Web: Ontology based Markup of Geographically Referenced Information. In Pr oceedings of Joint Euro SDR/Euro Geographics Workshop on Ontologies and Schema Translatio n Services, Marne-la-V allee, France, 15-16 April 2004. 21. Frank, A. U. Spatial Ontology: A Geogra phical Information Point of View. In Spatial and Temporal Reasoning ; Springer: Dordrecht, The Netherlands, 1997; pp. 135-153. 22. Pundt, H. ; Bishr, Y. Domain ontologies for data sharing—An example from environmental monitoring using field GIS. Comput. Geosci. 2002, 28, 95-102.

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Future Internet 2013, 5 590 23. Staub, P. ; Gnägi, H. R. ; Morf, A. Semantic inter operability through the de finition of conceptual model transformations. Trans. GIS 2008, 12, 193-207. 24. Perry, M. ; Hakimpour, F. ; Sheth, A. Analyzing Theme, Space, and Time: An Ontology-Based Approach. In Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems, Ar lington, VA, USA, 10-11 November 2006. 25. Cai, G. Contextualization of geospatial da tabase semantics for human-GIS interaction. Geoinformatica 2007, 11, 217-237. 26. Devillers, R. ; Bédard, Y. ; Jeansoulin, R. Multidim ensional management of geospatial data quality information for its dynamic use within GIS. Photogramm. Eng. Remote Sens. 2005, 71, 205-215. 27. Tummarello, G. ; Delbru, R. ; Oren, E. Sindi ce. com: Weaving the Open Linked Data. In The Semantic Web, Proceedings of the 6th International Semantic Web Conference and of the 2nd Asian Semantic Web Conference; Busan, Ko rea, 11-15 November 2007; Springer: Berlin, Germany, 2007; pp. 552-565. 28. Schwering, A. Approaches to semantic similari ty measurement for geo-spatial data: A survey. Trans. GIS 2008, 12, 5-29. 29. Fonseca, F. T. ; Egenhofer, M. J. ; Davis, C. A., Jr. ; Borges, K. A. Ontologies and knowledge sharing in urban GIS. Comput. Environ. Urban Syst. 2000, 24, 251-272. 30. Spielman, S. E. ; Thill, J. C. Social area analysis, data mining, and GIS. Comput. Environ. Urban Syst. 2008, 32, 110-122. 31. Alibrandi, M. ; Sarnoff, H. M. Using GIS to answ er the “Whys” of “Where” in social studies. Soc. Educ. 2006, 70, 138-143. © 2013 by the authors; licensee MD PI, Basel, Switzerland. This arti cle is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons. org/licenses/by/3. 0/).

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