Towards Process-Based Ontology for Representing Dynamic Geospatial Phenomena

Transcription

1 Towards Process-Based Ontology for Representing Dynamic Geospatial Phenomena Anusuriya Devaraju Institute for Geoinformatics, University of Muenster, Germany 1 Introduction Continuous geo-phenomena such as hurricanes, wildfires, water pollution and so on are complex by nature as they evolve in over space and time. According to (Peuquet, 1994; Yuan, 2001), one of the greatest challenges to design a framework which can track information about the what, where, and when of geographic phenomena is the representation of dynamic aspects of the phenomena; such representation requires a better understanding of the underlying change constructs such as process and event. Recent technological advances have facilitated Environmental Sensors Networks or ESNs as a new way to understand geo-phenomena and their interactions (Andrew et al., 2008; Hart and Martinez, 2006; Kuhn, 2005; Neal et al., 2008). ESNs are networks of sensors that monitor phenomena in geographic space. Process and event, by nature, are associated with some kind of change; and the ESNs provide key information about changes in the real world. For example, geo-sensor data takes the form of sequences of quantitative values (such as gage height) of some time-varying observable. Ongoing changes in any of these quantities created by processes such as stream flow that results from rainfall events. Based on the general picture, one can conclude that the key to improve the understanding of geo-phenomena dynamics is the understanding of space-time interactions of variables and processes (Galton, 2008). The nature of geo-sensors is they return huge amount of data which comes with different format, structures and at different states of processing. These heterogeneities problem can be a formidable challenge to the sensor data consumers who may want to integrate the data into a form that can be fed into decision support modelling and simulation tools. Therefore, we require a comprehensive model to bridge the gap between the low-level sensor measurements and user s high-level domain conceptualizations in terms of change, process and events (Worboys and Duckham, 2006). 2 Motivation and Problem Statements a. Lack of principled ways of describing geo-phenomena key concepts. According to (Galton and Worboys, 2005) it is generally agreed that the key concepts required for modelling dynamic phenomena include object, state, process and event, but there is little consensus, especially in GI Science, on exactly how these should be defined or distinguished from one another. As a result, the conceptual modelling of geophenomena has mostly proceeded in ad hoc fashion, with no solid basis in theory (Galton, 2008). The failure to distinguish between processes and events has led to confusion in the literature. Existing work on the relationship between categories of process, and event can be found here: (Galton, 2006; Galton, 2008; Galton and Worboys, 2005; James, 1984; Mourelatos, 2004; Yuan, 2001).

2 b. From an object-oriented to a process-oriented view of the world. Worboys (2004) describes the three stages of development in modelling dynamic phenomena. The Stage 1 (temporal sequence of snapshots) is a collection of temporal snapshots, usually all of the same spatial region, indexed by a temporal variable. In Stage 2, the emphasis is on the changes themselves with respect to states of existence and nonexistence for identifiable objects (Hornsby and Egenhofer, 2000). The shortcomings of Stage 1 and Stage 2 approaches are any assessment of the processes or events that led to the object evolution are not detectable; these approaches provide no answer for questions like how and why the changes happen. Stage 3 focuses on modelling events and processes, the way objects may participate in them and relationship between them. Existing work shows that in many application domains, the Stage 3 modelling approach is essential for modelling dynamic geospatial phenomena (Claramunt and Theriault, 1996; Galton and Worboys, 2005; Mau et al., 2008; Peuquet and Duan, 1995; Worboys and Hornsby, 2004; Yuan, 2001). In such work, both processes and events are regarded as primary entities to model geo-phenomena; however a formal approach to support process and event semantics is still missing in GI domain. c. Integrating various sources into a single description of the environment. Where data collected from environmental sensor networks are capable of providing users a more-or-less continuous record of measured values with respect to geo-phenomena, these data streams by themselves do not necessarily give a context for the data that includes semantics (Hornsby and King, 2008). The sensor data is gathered from multiple sources, which comes in multiple data formats, and in numerous temporal and spatial resolutions. Currently, there are several standard models and encoding schemas (Observations and Measurements Encoding Standard and TransducerML, Common Alerting Protocol (CAP) and Emergency Data Exchange Language (EDXL), WaterML from CUAHSI) that allow for seamless interchange of sensor data to applications that require such data. These syntax-based specifications do not specify the intended meaning of their terms in machine-readable form. Although the data can be obtained, there is no guarantee that data carry identical or compatible meanings. Such semantic heterogeneity problem can be a formidable challenge to the sensor data consumers who may want to compile the data into a form that can be fed into decision support modelling and simulation tools. 3 Research Questions The research will be developed based on the following questions: What are basic entities required for modelling dynamic geospatial phenomena and how are they classified? What are the cross categorical relations that hold between those entities? The first and second question concerned with the identification of key concepts required to model dynamic geographic phenomena. Clearly, any useful model will neither be pure object nor pure process; thus a hybrid approach is vital (Worboys, 2005). There are a number of top-level ontologies formalizing the notions such as object, process and events; providing categorical distinctions, and some of them becoming accepted standards (Natalya, 2004). Analyzing the top-level ontology is necessary to identify foundational entities needed to characterize dynamic phenomena before we start exploring the possible barriers that impede a seamless integration between sensor data and environmental models. The results of questions 1 and 2 lead into the development of formal data model of dynamic geo-spatial phenomena in the chosen domain, in this case the surface hydrology domain.

3 What are the potential semantic barriers that impede interoperating sensor data and environmental models? To answer the third question, a case study based on hydrological monitoring and forecasting is identified. Here, we examine the sensor standard models and encoding schemas, as well as related hydrological models used in the case study. The semantic heterogeneity in both inputs as well as the representation of underlying physical processes will be identified. The third question leads to the main focus of the research - how an ontology that classifies processes and events associated with geo-phenomena can be used to hide differences amongst sensor data sources and present unified view? 4 Approach According to (Hornsby and King, 2008; Ram et al., 2000), an ontology is a desirable solution for achieving semantic interoperability since it captures concepts of a domain and provides a foundation for discovering and resolving semantic conflicts in the underlying sensor datasets. In similar, Galton (2008) recommends that an ontology that classifies processes and events, refined by careful consideration of the spatial dimension, can be used to derive from sensor observations an understanding of processes associated with geo-phenomena. This study takes ontological approach (Kuhn, 2005; Probst, 2007) to represent explicitly the processes and events that will be used as tool to capture domain concepts, to interoperate sensor data and environmental models. The study proposes to use DOLCE top-level ontology to ease and guide the representation of foundational entities needed to represent dynamic phenomena. The domain for investigation is based on the river flow forecast research at South Esk catchment, Tasmania an ongoing project by CSIRO Tasmanian ICT Centre (Guru et al., 2008). The project aims to improve water planning and management through continuous monitoring and forecasting of river flow. A hydrology process-based ontology is required to overcome problems associated with the distributed nature of hydrological data and the semantic heterogeneities in the input data required for short-term river flow forecast modelling. The knowledge and ontological commitments about the chosen domain will be extracted from related publications and existing hydrological models. The entities will be identified and assigned to perdurant, endurant and quality notions in the top-level ontology. To reduce mistakes in assigning a concept to a wrong category, a cross-check between entities definitions and relations between categories in the top-level ontology will be performed. Finally, the ontology will be implemented and tested across hydrology sensor-web test bed. 4 Expected Outcomes The study is expected to produce two-tiered ontology which is aligned to top-level ontology that classifies complex and non-linear processes involved in rainfall-runoff phenomena. The ontology will be used to allow semantic-based geo-sensor data discovery and retrieval. An application programming interface and graphical user interface will be developed to enable users to execute queries involving the interplay between process and events. The incorporation of existing Sensor Web Enablement (SWE) services and the proposed ontology into hydrology sensor web testbed will be demonstrated.

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