Ioannis Athanasiadis | Γιάννης Αθανασιάδης
Information Technologies in Environmental Engineering
Filed Under (Conference papers) by I N Athanasiadis on 10-11-2009
In Workshop on Ontology-Driven Software Engineering at OOPSLA, Orlando, FL, USA, 2009.
Abstract This paper investigates how to use an idiomatic OWL/RDF model as a specification language for delivering Domain Object Model with relational persistence. It presents a systematic translation of a subset of OWL/RDF constructs to object structures with a relational database back-end. The presented framework has been developed as a plugin for the Protege ontology editor, and it has been evaluated against a benchmark of semantic repositories with promising results.
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Filed Under (Conference papers) by I N Athanasiadis on 03-08-2009
In 3rd Intl Conf on Metadata and Semantics Research (MTSR’09), (F. Sartori, M. A. Sicilia and N. Manouselis, ed.), pp. 282-293, 2009.
Abstract This paper presents a set of ontologies developed in order to facilitate the integration of a variety of combinatorial, simulation and op- timization models related to agriculture. The developed ontologies have been exploited in the software lifecycle, by using them to specify data communication across the models, and with a relational database. The Seamless ontologies provide with definitions for crops and crop products, agricultural feasibility filters, agricultural management, and economic valuation of crop products, and agricultural and environmental policy, which are in principle the main types of data exchanged by the models. Issues related to translating data structures between model program- ming languages have been successfully tackled by employing annotations in the ontology.
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Filed Under (Conference papers) by I N Athanasiadis on 13-05-2008
In Proceedings of the Fourth International Congress on Environmental Modelling and Software (iEMSs 2008), (M. Sànchez-Marrè, J. Béjar, J. Comas, A. E. Rizzoli and G. Guariso, ed.), pp. 719-726, Barcelona, Spain, 2008.
Abstract Integrated Assessment and Modelling (IAM) can be used to assess socio-economic and environmental indicators, which generally require the linkage of models from different domains. To integrate a set of different models for an IAM, the data required by each of the models as inputs from a range of data sources also needs to be consistently integrated. This paper describes the process of development of a database integrating different data sources for an IAM project, and the human factors involved in the process of reaching consensus across peers with clashing requirements and needs. We adopted a structured process using a shared ontology as a means to one integrated relational database serving a set of models of a highly multi-disciplinary nature. The relational database covers data on agricultural systems, e.g. soil, climate, farm, agricultural management and agricultural policy data. The integrated database has been coupled to a range of quantitative models. The database schema and the shared ontology are distinct products that can be reused for or extended by other IAM projects requiring a similar set of data. It is recommended for any IAM project in which several models are coupled to adopt an explicit, collaborative and iterative process to specify an adequate data structure for storing data used in the project. For such a process to succeed it has to focus on the relevant domain knowledge captured across the data sources and this paper offers a proposal for such a process.
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Filed Under (Conference papers) by I N Athanasiadis on 08-05-2008
In Int’l Conf. on Impact Assessment of Land Use Changes, (O. Dilly and K. Helming, ed.), pp. 66, Berlin, Germany, 2008.
Abstract Integrated Assessment Modelling tackle complex problems through the integration of data-intensive models, which pose great challenges for the management of data, data sources and connections between models and their data. In SEAMLESS we have integrated different data-sources into one common data-schema, represented in both an ontology and a relational database. This paper presents four aspects of this work: (a) the use of ontologies and ontology engineering to create a shared conceptual model, (b) the generation of relational data schema from the shared conceptual model, (c) the processing of data sources to populate the database, including the adaptation of data to a common spatial framework and aggregating source data to suitable typologies. (d) the access of the models to the data in the database through the ontology. Through the use of ontology, ontology engineering and relational databases, the first Pan-European database on soil, climate, farm and agricultural management was created that is directly accessible for the models operating in SEAMLESS. The database holds data on model inputs and model outputs as well as contextual data for the assessments. For our developments only open source tools were used, so the ontology has been built in Protégé and the database schema generated through Hibernate. The data are stored in the relational database management system PostgreSQL running on a Linux server. To support visualization of results PostGIS functionality is added to a PostgreSQL database and Geoserver is used to provide Web Mapping and Web Feature services.
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Filed Under (Conference papers) by I N Athanasiadis on 03-01-2008
In MODSIM 2007 Int’l Congress on Modelling and Simulation, (L. Oxley and D. Kulasiri, ed.), pp. 2055-2061, Christchurch, New Zealand, 2007.
Abstract This paper explains our experiences with a challenging and time-consuming task, e.g. arriving at a shared understanding on the definition of projects, experiments and scenarios among researchers coming from different disciplines, who have been exposed to dissimilar education and research experience. We demonstrate the use of ontologies in building this shared set of definitions and the relationship between the ontology and the human computer interaction through a case study. With a common ontology that represents the joint conceptualization of the projects, experiments and scenarios each researcher can refer at any later stage to the semantics of the concepts used. A collaborative approach was used to build such a common ontology in the SEAMLESS-Integrated Project, funded through the EU sixth Framework Programme, which aims at developing an integrated modelling framework (SEAMLESS-IF) to assess, ex-ante, agricultural and environmentalpolicy options, allowing cross-scale analysis of a broad range of sustainability issues. As a first validation of the project ontology, a set of four fictitious sample projects were made. One of these sample projects is an integrated assessment for one region Midi-Pyrénées in the South of France concerning the impacts of the CAP2003 reform, which is described in this paper.
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Filed Under (Conference papers) by I N Athanasiadis on 03-01-2008
In MODSIM 2007 Int’l Congress on Modelling and Simulation, (L. Oxley and D. Kulasiri, ed.), pp. 1959-1965, Christchurch, New Zealand, 2007.
Abstract The challenge in integrated modeling is the conceptual integration. To achieve this, we need explicit semantics and a shared conceptualization. A participatory and collaborative approach is a key success factor for the creation of a common ontology for models, indicators and raw data. The development of the SEAMLESS common ontology was and still is a big challenge that is performed by a dedicated taskforce. By putting the ontology in a central position in the project and the systems architecture, this shared conceptualization is the basis for generating (Java) source code for the object classes representing all the concepts and representing the objects in relational database tables. The use of ontology has proved to be very useful if not essential both for the technical integration of knowledge in the SEAMLESS Integrated Framework and in understanding the meaning of communicated words of the diversity of people within the project.
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Filed Under (Conference papers) by I N Athanasiadis on 13-08-2007
In Proc. of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology – Workshops, Silicon Valey, California, USA, 2007.
Abstract Agent training techniques study methods to embed empirical, inductive knowledge representations into intelligent agents, in dynamic, recursive or semi-automated ways, expressed in forms that can be used for agent reasoning. This paper investigates how data-driven rule-sets can be transcribed into ontologies, and how semantic web technologies as OWL can be used for representing inductive systems for agent decision-making. The method presented avoids the transliteration of data-driven knowledge into conventional if-then-else systems, rather demonstrates how inferencing through description logics and Semantic Web inference engines can be incorporated into the training process of agents that manipulate categorical and/or numerical data.
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Filed Under (Conference papers) by I N Athanasiadis on 13-08-2007
In PLoS Track at 15th Annual International Conference on Intelligent Systems for Molecular Biology and & 6th European Conference on Computational Biology, pp. 4, Vienna, Austria, 2007.
Abstract This paper presents a new theoretical synthesis that stems from the realization that all system models, whether static (datasets) or dynamic, incarnate the result of an observation process that can be described logically through a single, appropriately expressive ontology. Such a conceptualization, which we have distilled into a publicly available OWL ontology and supported with open source software infrastructure, not only provides a more natural semantics for data annotation, but is also key to enabling a novel integration of data, models, and applications.
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Filed Under (Conference papers) by I N Athanasiadis on 10-07-2007
In Proc. 21st International Conference on Informatics for Environmental Protection: EnviroInfo 2007, (O. Hryniewicz, J. Studziński and M. Romaniuk, ed.), pp. 43-50, Warsaw, Poland, 2007.
Abstract Environmental informatics delivers techniques and tools for archiving and processing environmental data. The advent of the Internet had positively affected the availability and ease of access to large and diverse environmental databases, distributed all over the world. On the other hand, similar progress has not been matched by the availability of models and algorithms able to process these data, mostly because of the lack of standards in the annotation of the characteristics of environmental models. In this paper we advocate the need for the semantic annotation of environmental “knowledge”, encompassing models and data. The slow, but steady, introduction of the Semantic Web and the widespread use of ontologies for semantic annotation will allow environmental informatics to cover the gap in the access and usability of models and algorithms for environmental data processing.
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Filed Under (Conference papers) by I N Athanasiadis on 13-06-2007
In Farming Systems Design 2007: Methodologies for Integrated Analysis of Farm Production Systems, (M. Donatelli, J. Hatfield and A. Rizzoli, ed.), pp. 225-226, Catania, Italy, 2007.
Abstract Farm production planning involves the simulation and evaluation of crop succession alternatives, known also as crop rotations. A crop rotation is a succession of crops in time and space, that are applied cyclically on the same piece of land. Artificial crop rotations schemes are typically generated as all possible rearrangements of the available crops that are agronomically feasible with respect to crop frequency and succession (suitability filters). Given a set of c crops and a desired length of rotations r, the traditional approach requires the evaluation of all possible combinations of crops in a solution space, sized c to the power of r. This practice limits the length of rotations to be evaluated as the size of crop rearrangements expands exponentially. In this paper we present a more efficient and faster alternative generation algorithm that excludes cyclically equivalent rotations from the solution space. The algorithm represents each crop rotation cycle as a number in the c-based numeral system, and is capable of excluding the generation of cyclic equivalent rotations, through a single modulo operation. After the generation of all non-cyclically equivalent crop rotations, the suitability filters are applied for obtaining agronomically feasible rotations, which form the basis of followup assessments.
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Internet for peace
Environmental Modelling & Software
