Ontology-driven Problem Solving Framework for Spatial Decision Support Systems

Transcription

1 Ontology-driven Problem Solving Framework for Spatial Decision Support Systems Abstract Chin-Te Jung 1, Chih-Hong Sun 2, 1 Department of Geography National Taiwan University d @ntu.edu.tw 2 Taiwan Geographic Information Center chsun@tgic.org.tw Spatial data infrastructure development has evolved beyond a collection of spatial data portals to a web of GI services that enable not only geospatial data dissemination online but spatial function support from distributed GI services. Major challenges remain, however, to address the fundamental issues of discovering and synthesizing heterogeneous resources of data and functions across web GI services. One key challenge is effective ontological support to enable identification of useful resources that are semantically comparable, and furthermore to address information needs through inquiries expressed in a problem statement rather than in a structural query language. We propose an ontology-driven problem solving framework for GI services in four components: a web portal, an ontologies engine, spatial data cyberinfrastructure (SDCI), and a service chain mediator. The users build problem statements in the web portal. The problem statements then are checked by the ontologies engine with existing knowledge in a knowledge database. Supporting the proposed framework is a knowledge database elicited from domain experts to automate ontological reasoning. The existing knowledge contains conceptual needed GIS data and function for the statement. The concrete GI services which meet those conceptual needed GIS data and function can be automatically discovered in SDCI by the ontologies engine. The chain mediator then constructs a workflow to and consequently processing the GI services in response to the specified problem statement. The proposed framework presents a new flexible architecture to enhance cyber GIS support for spatial inquiries based on ontological problem statements to search for data and generate useful information across a SDCI of multiple web GI services. Keywords: Semantic web, SDSS, ontology, GIS, web services, cyberinfrastructure 1. INTRODUCTION In recent years, Internet GIS has blossomed into high-value and ubiquitous geospatial information services, including on-line GIS data clearinghouses, geospatial analysis functions, and sharable GIS models for the public and the professionals since late 1990s (Goodchild, Kryiakidis, Rice, & Schneider, 2004). The proper of geospatial technology advancement results in a diverse array of geospatial data formats and naming conventions across online GI services. While the Open Geospatial Consortium (OGC) and the ISO/TC 211 committee have been a leading force in 1

2 developing geospatial standard technologies, such as GML (geography markup language), ISO (metadata standard), WMS (web mapping service), WFS (web feature service), to promote interoperability, challenges of semantic interoperability remain as to properly identify correspondent meanings of different terminologies adopted by data providers over the web (Bishr, 1998; Kuhn, 2003). The challenges include standardizing spatial syntax and formalizing spatial semantics. Spatial syntax refers to the ways GIS operations and functions (web services) may be combined or chained to create well-formed spatial query (or analysis). Spatial semantics provides domain-oriented definitions and describes the relationships and behaviors among spatial datasets and GIS operations in a spatial syntax. Such challenges can hamper geospatial data sharing and information discovery and can ultimately undercut the grand vision of a cyber GIS web. Rooted in the concepts of geospatial semantics, this research aims to address geospatial semantic interoperability by developing an ontology-based framework to enable the support for problem-based spatial inquiries for data search and information generation across a cyber infrastructure of GI services. 1.1 Semantic interoperability in geospatial domain The idea of a geospatial semantic web is to capture, analyze, and tailor geospatial information beyond the purely lexical and syntactic levels of data search (Egenhofer, 2002). Similar to other semantic web efforts, realization of a geospatial semantic web needs to (1) develop spatial ontologies for different domains and identify semantic distinctions and relations among ontological specifications; (2) represent the identified semantic concepts for computer processing and for human understanding; and (3) the processing of geospatial queries against these ontological commitments and evaluate retrieval results based on the match between the semantics of the expressed information need and the available semantics of the information resources and search systems (Egenhofer, 2002). Geospatial semantic web research is of growing interest to academic communities with the premise that geospatial semantic web or geospatial ontologies is the foundation to achieve geospatial semantic interoperability (Bishr, 1998; Egenhofer, 2002; F. Fonseca & Sheth, 2002; Harvey, Kuhn, Pundt, Bishr, & Riedemann, 1999; Kuhn, 2003; Lemmens, 2006) and to improve geospatial data search (e.g. SPIRIT) and discovery GI services in a complex spatial data infrastructure (SDI) (L. Bernard, et al., 2003; Lutz, 2005, 2006) or spatial data cyber infrastructure (SDCI) (Zhang & Tsou, 2009). 1.2 Using ontologies for geospatial semantic web In philosophy, Ontology deals with the nature and the organization of reality (Guarino & Giaretta, 1995) or the science studying of being (Hendler, 2001). Since there is only one reality, there is only one Ontology that seeks what is there by identifying the types of entities that exist in reality and their relationships (Mark, Smith, Engenhofer, & Hirtle, 2004). Information scientists extend the concept of ontology to what information users will seek from information systems and 2

3 knowledge management systems. Since users in different domains are likely to look for information about different things, there are many ontologies used in domain applications. In Information Science, ontology is defined as an explicit specification of a conceptualization (Gruber, 1993), where a conceptualization is a way of thinking about a domain (Uschold, 1998) in terms of concepts and relationships relevant to that domain. Therefore, an ontology is to explicitly and formally describe terms and relationships in a domain, and by doing so, an information can incorporate the domain ontology in data integration processing, search algorithms and analytical methods. Specifically, researches in geospatial ontologies are of two tracks: one focuses on developing ontologies to manage GIS data and functions (Bernard, et al., 2003; Bernard, Einspanier, Lutz, & Protele, 2003; Klien, Lutz, & Kuhn, 2006; Lutz, 2005, 2006; Probst & Lutz, 2004), and the other focuses on identify ontologies that map to concepts in a geospatial domain (Egenhofer & Mark, 1995; F. T. Fonseca, Egenhofer, Agouris, & Camara, 2002; Mark, et al., 2004; Smith & Mark, 2003). The successful development of an information system requires ontological commitments to assure that data objects, concepts, and relationships adhere to the chosen ontological specifications. Furthermore, data integration and data sharing among information systems require a common ontology or clear mapping among ontologies adopted in these systems. Neither the development of a common ontology or resolution of ontological mapping is a trivial task because data semantics and semantic relationships are often complex and sometimes incompatible from one system to another. Input from domain experts is critical to ontological analysis leading to a common ontology or ontological mapping. 1.3 Adopting ontologies to Improve Spatial Decision Support Systems. Spatial Decision Support Systems (SDSS) are interactive and computer-based systems to support a group of users in making geospatial-related decisions (Malczewski, 1999). SDSS contain several components: spatial databases, GIS models, domain knowledge bases, map display capabilities, report capabilities, and user interfaces (M. F. GoodChild, 2000; Vijayan Sugumaran & Sugumaran, 2005). These SDSS components can be combined in two types of system architectures: loose coupling or tight coupling (Malczewski, 1999). Tight coupling SDSS means that all components are integrated into a single system. On the other hand, loose coupling SDSS means that all components may be located in different places, but can be communicated with each other. The loose coupling architecture is better for web-based SDSS applications because spatial data and GIS function can be reused, shared, and exchanged on the Internet and experts can combine multiple GI services into a workflow to generate complicated GIS analysis functions for different spatial decision scenarios. Several researchers have adopted the loose coupling architecture for the development of web-based SDSS with GI services (Dragicevic, Balram, & Lewis, 2000; Rinner & Jankowski, 2002; Sugumaran, Meyer, & Davis, 2004). 3

4 In support for problem solving, many geospatial problems share common data and analytical tools. Therefore, data, tools, and workflows may be reused across applications in cyber GI services. For example, wildfire management and air pollution impact analysis may both use census data to estimate the number of residents at risk and a buffer function to estimate the influenced areas. The workflow for using the buffer function and census data may be also the same. Thus, a knowledge base will be desirable to store workflows along with web resources for data services and function services in a standard format to automate geospatial data search and analysis to facilitate future users to discover suitable GI services for problem solving. Therefore, how to formalize workflows with a standard format and be stored in a knowledge base? How to deal with spatial semantics when selecting requested geospatial data and GIS functions? How to know the selected geospatial data and GIS functions are suitable? How to make computers automatically executing the workflows and intelligently discovering suitable GI services and data? These questions are the major focuses of this paper, and we decided to create a problem-oriented framework and prototype for Web SDSS by using ontologies to demonstrate the feasibility of these concepts. Figure 1 illustrated the major differences between traditional SDSS (tight coupling) and the ontology-enabled Web SDSS (loose coupling). In the traditional SDSS, when facing a problem, experts have to contribute their knowledge to generate a concept workflow (including GIS data and models) for the problem, and to manually discover and compose concreted GIS data and functions to generate a result. However, in the ontology-enable Web SDSS, the concept workflow contributed by experts can be formalized into a knowledge base (i.e. ontologies) in advance. When facing a problem, machines can help experts to check the existing knowledge in the ontologies first. If there are existing concept workflows for the problem that have be defined by experts, machines will be automatically discovered suitable GIS data and function web services in SDCI, and compose the discovered GI services by the predefined concept workflows in the ontologies to generate results. It there are no existing conceptual workflows responded for the problem, experts need to contribute their knowledge (i.e. concept workflows for the problem) into ontologies, which can be reused and searched by machines in the next time. 4

5 Figure 1: The comparison between traditional SDSS and ontology-enable Web SDSS Although there are already many researches focused on how to discover and composite web services by using ontologies (Klien, et al., 2006; Lemmens, 2006; Lemmens, et al., 2006; Lemmens, Wytzisk, & By, 2006; Lutz, 2006, 2007, 2008), very few research projects focus on the improvement of web-based SDSS and the creation of ontology-based workflows for GIS data and GIS models. Therefore, this research takes a step further to develop a problem-based approach that applies ontologies to package workflows of data and models across cyber GI services to free the user from data and tool search to focus on the problems on hand. Upon the premise that problem-oriented approaches are more effective means to utilize GI services in a cyber infrastructure, we propose and implement a problem-oriented approach enabled by ontologies and workflows to directly support problem solving rather than data and tools needed to solve the problem of interest. The ontologies are at the center of the proposed problem-oriented framework where data, functions, and workflows are stored in a standard and machine readable format (e.g. web ontology language, OWL) to enable automation for future problem solving requests. The remainder of the paper is structured as follows: Section 2 describes the detail of the proposed framework, Section 3 provides a scenario to illustrate the utility of the framework, Section 4 5

6 discusses the advantages and challenges of the framework, and Section 5 concludes development, highlights the case scenario, and suggests directions for future works. 2. AN ONTOLOGY-DRIVEN PROBLEM-ORIENTED FRAMEWORK FOR GI SERVICES IN A CYBERINFRASTRUCTURE The novelty of the proposed framework for GI services resides in the combined use of ontologies and workflows to facilitate acquisition of information pertinent to a user-defined problem statement. Apart from the conventional GI services, the proposed framework leads the user to structure a problem statement and automate data search and data analysis enabled by an embedded knowledgebase in cyber GI services. 2.1 The components for the framework 2.2 A Web Portal A web portal is an integrated access point for decision makers (users) to conduct different types of decision making and spatial analysis. The portal provides an input area for decision makers to select or input their spatial decision scenarios as a spatial syntax, and an interactive map for decision makers to limit a spatial area (i.e. by drawing points, lines, or polygons) as a spatial boundary. In addition, the portal also presents results generated by web services for decision makers to evaluate. 2.3 An Ontologies Engine The ontologies engine can be used to model domain knowledge from experts into ontologies. Ontologies have to be explicitly described the domain knowledge, including concepts, properties (i.e. relations between concepts), and instances (i.e. an object of the concept), in a standard and machine-readable format, which can be inferred by machines to discover relevant concepts and instances (i.e. web services). When decision makers submit a geospatial problem statement, it will trigger the ontologies engine to detect the statement for inferring relevant (geospatial) semantic terms, and for discovering existing knowledge in ontologies. We proposed several ontologies, which is revised from the architecture of ontologies suggested by Kolas(2005) for geospatial semantic web. All ontologies are described in a standard ontology web language (OWL) (W3C, 2004), which is a machine-readable language and can be inferred. Moreover, we used two ways to reason existing knowledge in ontologies: description logics (DL) (Baader & Nutt, 2003) and semantic web rule language (SWRL) (Horrocks, et al., 2004). The DL is a kind of knowledge representation language that use primitive symbols, e.g. logical connectors ( not, ^ and), quantifiers (! there exists,! for all), to represent relationships between concepts and infer implicit relationships between concepts or between individuals and concepts (Baader & Nutt, 2003). However, DL can make only limited assertions between individuals (Lutz & Kolas, 2007). For example, it is impossible to infer that one web services 6

7 (i.e. an individual) is the next sequence of other web services (i.e. an individual) by DL. Thus, we adopted SWRL to complement the limit of DL. SWRL is a combination of OWL DL with rule markup language (RuleML) (Boley, Tabet, & Wagner, 2001), which can use rules to represent the relationships between individuals. Therefore, we used both ways to reason solutions in existing knowledge in the ontologies. We used four ontologies to deal with different domain knowledge, includes: 1. Geospatial geometry ontology provides basic geospatial geometry (e.g. points, lines, and polygons). We followed OGC Simple Feature Specification (OGC, 2006a) to build the geospatial geometry ontology. For example in Figure 2, the Polygon concept is a subset of Surface concept, which is contained by Geometry concept. Figure 2: The geometry ontology 2. Geospatial web services ontology provides the definition of geospatial web services, which is defined by OGC. (2003a). For example, WFS specification (OGC, 2005), WMS specification (OGC, 2006b), and WCS specification (OGC, 2003b). For example in Figure 3, WMS concept is belonged to the OGCWebServices concept, which is the subset of the GISWebServices concept. 7

8 Figure 3: The geospatial web service ontology 3. Geospatial function ontology describes the input and output data type, precondition, effects, and synonyms of geospatial functions. For example in Figure 4, the Buffer function is organized into a hierarchical structure in the geospatial function ontology, which is based on the classification of the ArcToolbox by ESRI1. And, the data types for input data are points, lines, or polygons; the data type for output data is polygon. Moreover, the synonyms, e.g. Buffer in Chinese words ( ) are also described in the ontology for semantic interoperability

9 Figure 4: The geospatial function ontology 4. Task ontology presents the knowledge of tasks in a domain. Task ontology is focused on a task in a domain to explicitly extract and describe the knowledge of the task. According to Timpf (2001), tasks ontologies are need as well as domain ontologies for knowledge sharing, semantic interoperability, and web services re-using. Therefore, in this paper, a geospatial problem is considered as a task. The knowledge for the task is thought as a solution. The solution is similar with a workflow, which contains a series of concepts of GIS functions and data. We used GIS functions as basic units for each task, and used a property, hasnext, to connect GIS functions in each task, as shown in Figure 5. 9

10 Figure 5: The task ontology 2.4 SDCI Based on service oriented architecture (SOA), a directory in SDCI is an initiative intended to create an environment that will enable a wide variety of users (i.e. web service consumers and web service publishers) to access, retrieve and disseminate spatial data and information in an easy and secure way (Rajabifard, Mansourian, Zoej, & Williamson, 2004). However, SDCI still lacks a knowledge-oriented way to discover GI services. Thus, we provided an ontology plug-in mechanism into the directory to cooperate GI service and knowledge. While web service publishers register the metadata of GI services into the directory of SDCI, the metadata is also registered into the knowledge databases (i.e. ontologies) by the ontology plug-in mechanism. In the task ontology, we only defined conceptual solutions for each task. These conceptual solutions do not connected with any executable concrete GI services. By the ontology plug-in mechanism, we can integrate the conceptual solutions with concrete web services to generate results for the problem. Therefore, when the ontologies engine infers the solution in the ontologies for a geospatial problem, the concrete GI services in SDCI can be mapped automatically from the inferred solution. For example in Figure 5, if a geospatial problem (e.g. Task_A) is submitted by decision makers, the solution (e.g. Function1, Funtion2, and Function3) for the problem will be inferred in the task ontology. Since the detail information of inferred GIS functions concepts are defined in the geospatial function ontology, the ontologies engine can automatically infer to the geospatial function ontology to access the detail information (i.e. names and synonyms) of inferred functions in order to discover concrete GI services in SDCI. 10

11 2.5 A service chain mediator After discovering GI services in the SDCI, the discovered GI services can be assembled by a service chain mediator. Because the sequence of GIS functions has defined in the task ontology, the discovered GI services can be followed by the defined sequence to assemble a service chain, which can be used business process execution language (BPEL) to describe and execute the sequence processes (Yue, Di, Yang, Yu, & Zhao, 2007). The results of the executed service chain will be presented in the web portal for decision makers to evaluate. 2.6 The processes among the components of the framework By the framework, decision makers, domain experts, and web service publishers can be cooperated together to deal with geospatial problems. The web services publishers publish GI services into a SDCI, and register the metadata of published GI services into a SDCI catalogue (e.g. Geospatial One Stop2), where GI services can be discovered by web services consumers. Besides registering into the SDCI catalogue, by the ontology plug-in mechanism, the metadata of the GI services is registered in the ontologies for knowledge-oriented discovery. The targets for domain experts are to contribute their knowledge for different geospatial problems into the knowledge databases (i.e. ontologies) with a standard and machine-readable format. Thus, in the framework, we provide an ontology editor to help domain experts to contribute their knowledge and encode the knowledge with a standard format (i.e. OWL) into ontologies. The purposes for decision makers are to submit their geospatial problems in the web portal. Decision makers do not need to know exactly how to solve the geospatial problems or where the needed GI services are, since the framework can help decision makers to infer the suitable solutions, which are contributed by domain experts, or to discover and execute the needed GI services for the problems. The processes among the components of the framework and the users in the framework are illustrated by Figure 6 and described by the following steps: 1. Decision makers can submit geospatial problems in the web portal. The web portal assists to detect the submitted spatial syntax and transformed to a formal spatial query seeking for the answers for the problems. 2. The spatial query will transfer to the ontology engine to search suitable solutions, including what GIS data and functions will be needed, and how to compose the needed GIS data and functions, for the query. 3. By the ontologies engine, the spatial query will be checked if there are existing knowledge in ontologies have been defined by experts. If there is existing knowledge, the knowledge will be reasoned as solutions for the query

12 4. The solutions reasoned by the ontologies engine contain the name and synonyms of needed GIS data and functions web services, and the composite sequence of needed web services. 5. If there is an existing knowledge in ontologies, the reasoned information by ontologies can be used to map concrete GI services in a service catalogue of SDCI. Because the needed concrete GI services have been already registered into the ontologies when web service publishers register GI services into the SDCI catalogue. If there is no existing knowledge in ontologies, the ontologies engine will inform the information that the suitable solutions cannot be discovered to the decision makers in the web portal. 6. The discovered concrete web services will be transferred to service chain mediator to compose a service chain to generate results. 7. When composing web services as a service chain, the sequence of the web services is essential. The sequence can be obtained from the ontologies engine since domain experts have defined the sequence in the ontologies. In step 4, the reasoned solutions contain not only the needed GIS data and functions for the spatial query, but also the sequences of the web services. The sequence of the needed GI services can help the service chain mediator to compose the discovered concrete GI services into a service chain to generate outcomes. 8. After generating outcomes by the service chain mediator, the results will be presented in the web portal, including the discovered concrete web services, the sequence of the web services, and the outcomes from the composed service chain. 9. Decision makers can evaluate the results in the web portal. 12

13 Figure 6: The ontology-enable problem solving framework for GI services in a cyber infrastructure 3. PROTOTYPE AND SCENARIO We conduct a scenario on an earthquake emergency in Taipei city, Taiwan, to illustrate the problem-oriented framework and the prototype of web-based SDSS. In the earthquake emergency, geospatial problems involve different aspect of the emergency, such as how many residents in the damaged area? where are the nearest hospitals or hydrants? where is the epicenter of the earthquake? Thus, for the geospatial problems, concrete GI services and domain knowledge are needed to meet different geospatial problems. Additionally, in the earthquake emergency, we do not have much time to search and understand semantic of those needed GIS data and functions among GI services, and to build a specific SDSS for the emergency. Therefore, how to use the ontological framework to cooperate domain knowledge from domain experts, GI services from web service publishers, and submitted geospatial problems from decision makers, and how to automatically discover needed GIS data and functions among GI services in SDCI, and how to compose the discovered GI services to execute results are main purposes for the scenario. 13

14 Tom is a geography scientist whose researches are focused on earthquake emergencies. Tom has contributed his domain knowledge about the emergencies and defined several solutions (i.e. conceptual workflows) of tasks in the earthquake emergencies into the task ontology by the ontology editor, such as How many X near Y meters of a LOCATION?, Where are the nearest X?, and Where is the epicenter of X?, where X, Y, and LOCATION are variables inputted by decision makers. By using SWRL (Horrocks, et al., 2004; O'Connor, Shankar, Nyulas, Tu, & Das, 2008), Tom can define the solutions by rules, which describe what GIS functions are needed for the problem and what the sequence of needed GIS functions is. For example, the solution for the How many X near Y meters of a LOCATION problem contains three GIS functions (e.g. Buffer, Clip, and Summary Statistics ) described by the hasgisfunction relationship, and the sequence of these needed GIS functions defined by the hasnextgisfunction (e.g. Summary Statistics is the next function of the Clip function, and the Clip function is the next function of the Buffer function), as show in the rule 1. Rule1: Task (?task) ^ GISFunction: Buffer (?buffer) ^ GISFunction: Clip (?clip) ^ GISFunction: SummaryStatistics (?sum) hasgisfunction (?task,?buffer) ^ hasgisfunction (?task,?clip) ^ hasgisfunction (?task,?sum) ^ hasnextgisfunction (?buffer,?clip) ^ hasnextgisfunction (?clip,?sum), where Task is a concept in the task ontology;?task is a individual for the Task concept; GISFunction: Buffer, GISFunction: Clip, and GISFunction SummaryStatistics are concepts in the GIS Function ontology;?buffer,?clip, and?sum are individuals (i.e. GI services) for the concepts; hasgisfunction, and hasnextgisfunction are relationships in the task ontology to connect related concepts. Bill is a web service publisher who works in the Taipei government. He has already publishes several GIS data web services on the Web and register the metadata of the published GI services into a catalogue of SDCI. The published GIS data web services include Census data and The 2000 s Population data in Taipei City. While Mary is another web service publisher who publishes several GIS function web services on the Web and also register the metadata into the catalogue of SDCI. Her published GIS function web services contain Buffer, instance_buffer, instance_clip, and instance_summarystatistics GIS functions. Since the assist of the ontology plug-in on the catalogue of SDCI, the concepts in the ontologies can be selected by Bill and Mary when the metadata of published GI services are registered in the catalogue of SDCI. Thus, registered GI services can be mapped into the concepts in the ontologies. For example, Buffer and instance_buffer GI services are mapped to the Buffer concepts in the geospatial function ontology. 14

15 John is a decision maker for the earthquake emergency. When an earthquake happened in Taipei city, he wants to quickly control a damaged situation of How many residents near 500 meters of a damaged area? By the web portal of the framework, John can submit his problems by selecting predefined parameters (e.g. questions and spatial relationships, as shown in Figure 7) and by inputting a keyword and a geospatial boundary. For example, John can select How many as a question, input Residents as a keyword, select Near and input 500 meters as a spatial relationship, and input a place name or draw an area on the map as a geospatial boundary, as shown in Figure 7. The web portal does not use a search box, e.g. Google, for decision makers to input their whole geospatial problems, because using natural language processing is beyond the scope of the paper, and we focus on how to use ontologies for achieving semantic interoperability and improving web-based SDSS. Thus, we used a mixed way: (1) combo box to restrict values of questions and spatial relationships; and (2) search box to input keywords and a geospatial boundary to detect geospatial problems by decision makers. Figure 7: The portal that decision makers can submit a problem statement by selecting question and spatial relations, and inputting keywords and locations 15

16 After John submits the geospatial problem (i.e. How many residents near 500 meters of a damaged area? ), the ontologies engine will check if there is any existing knowledge match the submitted problem in the ontologies. Because Tom, domain experts, has already defined the solution of the problem in the task ontology, the ontologies engine can infer a solution of the How many X near Y meters of a Location task in the task ontology, where X is residents, Y is 500, and LOCATION is the area inputted by John. The solution contains a conceptual workflow composed by the sequence of needed GIS functions (e.g. using Buffer, Clip, and Summary Statistics functions to solve the problem), which is described by SWRL, as shown in Rule 1. For semantic interoperability, machines have to understand the semantic of the three variables (i.e. X, Y, and Location) and needed GIS functions. The semantic includes what super concepts the variables and functions are belonged to, what relationships between the concepts and the variables are, and what synonyms of the variables and functions are. For example in Figure 4, the Proximity function is the super concepts of the Buffer function, the Buffer function has hasinputtype relationships with Point, Line, or Polygon geometries, and the Buffer function has synonyms: Buffer and Buffer in Chinese words ( ). The semantic of the three variables and needed GIS functions has already been defined in our ontologies, e.g. geospatial geometry ontology and geospatial function ontology. Thus, by using ontologies, machines can understand the semantics that decision makers have submitted in the web portal, and can infer to the possible solutions in the ontologies. The solution, including needed GIS data and functions among GI services, inferred by the ontologies engine for the problem, is visualized in the web portal, as shown in Figure 8. In the Figure, the yellow parts present what concrete GIS data web services are needed for the problem, the dark blue parts show what the concepts of GIS functions are used for the problem, and the light blue parts display what concrete GIS function web services can be used. For example, there are three existing concrete GIS function web services for the Buffer concepts, one concrete web services for the Clip concepts, and one concrete web services for the Summary Statistics concepts in the SDCI. 16

17 Figure 8: Discovered GI services by the ontologies engine: (1) conrete GIS data web services (yellow squares); (2) concrete GIS function web services (light blue squars); and (3) concepts of GIS function (dark blue squars) In addition, the discovered GI services from SDCI will be automatically combined together as a service chain, which is based on the predefined sequence in the task ontology. Machines can automatically follow the sequence to generate an initial result which can be presented in the web portal for decision makers to evaluate, as shown in Figure 9. The result includes (1) the inferred semantic of the keywords that decision makers inputted (e.g. the inferred semantic of Residents is Census data); (2) the needed GIS functions, which is based on the knowledge in the task ontology (e.g. Buffer, Clip, and Sum); (3) the discovered concrete GI services from SDCI (e.g. the GIS data web services for Residents is and (4) the answers for the geospatial problem that decision maker submitted. For example, the answer for the How many Residents near 500 meters of a damaged area problem is residents who will be affected near 500 meters of a damaged area. 17

18 Figure 9: The results includes (1) the inferred semantic of keywords and needed GIS functions; (2) the discovered GI services from SDCI by the ontologies engine; and (3) the answer for the geospatial problem Therefore, back to our scenario, although John does not know any specific knowledge about GIS and web services, he can still use the prototype to discover useful GI services, to know the solution, and to get the answer for his geospatial problems. Additionally, GIS domain experts can create more tasks for different geospatial problems in ontologies to meet more needs. 4. DISCUSSIONS By the problem-oriented framework and the prototype, there are several advantages that can improve traditional SDSS and Web-based SDSS as listed in the followings: 1. Decision makers can submit geospatial problems into the web portal without specific GIS knowledge or techniques, and can understand what GIS data and functions are needed for answering the problems in a visualized way, as shown in Figure 9. Additionally, decision makers can quickly access the answers in the web portal. 2. Domain experts can contribute their domain knowledge into a knowledge database (i.e. 18

19 ontologies) by a standard, structure, and manageable format (i.e. OWL). Thus, the knowledge can be shared and reused. In the framework, tasks are the basic units to extract knowledge from domain experts. The knowledge in a task can be considered as a solution for the task, including what GIS data and functions are needed and how to compose needed GIS data and function to generate results, as shown in the rule 1. Therefore, tasks can be expanded, reused, and revised by different domain experts for different specific geospatial problems. We do not need to build specific SDSS for different geospatial problems. We only need to do is to use the ontological framework to submit geospatial problems, and the framework can automatically discover corresponding solutions in the ontologies to answer the problems. 3. By the ontological framework, GI services can be discovered automatically in a knowledge-oriented way, and the discovered results can be more specific than keyword search. Because GI services are not only registered in the SDCI, but also mapped to the corresponding concepts in the ontologies (e.g. instance_buffer concrete web service is mapped to the Buffer concepts in the GIS Function ontology). However, the framework still remains some challenges needed to be overcome as the lists in the following: 1. Matchmaking: when web services are discovered by ontologies, a matchmaking procedure is needed to rank discovered web services by comparing input and output type, semantics, and web services type (e.g. WMS, WFS, or WSDL). Rodrigues (2000) have presented a algorithm to calculate the semantic similarities among ontologies. The algorithm can be included in the framework to deal with web services matchmaking in the future. 2. Composing and executing web services: Although the sequence of web services can be defined in ontologies, how to compose concrete web services and execute results is still not completed in the prototype. However, some researches have presented ways to compose and executing web services by using BPEL (Di, Zhao, Yang, & Yue, 2006; Koehler & Srivastava, 2003) and OWL-S (Koehler & Srivastava, 2003; Yue, et al., 2007). By using the researches and technologies, composing and executing web services can be achieved in the prototype in the future. 3. Processing natural language: In this paper, we did not deal with natural language processing, thus we used mixed ways (e.g. combo box and search box) to limit submitted spatial problems. In the future, natural language processing can be adopted into the framework on detecting, analyzing, and transforming inputted spatial problems into more useful and precision information (Strokes, Li, Moffat, & Rong, 2008). 4. Editing ontologies interface: Domain knowledge from experts has to be encoded in OWL by using protégé3 in the framework. Therefore, ontological engineers are needed to help domain

20 experts to encode their knowledge into ontologies. However, once knowledge changes, ontological engineers have to access original ontologies to change it again. It is not very convenience when ontologies are edited by many experts and are changed frequently. Therefore, a Wiki way for domain experts directly edit ontologies by themselves is a needed task (Iorio, Presutti, & Vitali, 2006). 5. CONCLUSIONS AND FUTURE WORKS The paper presented a problem-oriented framework for web-based SDSS by using ontologies. We used four ontologies (e.g. geospatial geometry ontology, geospatial web services ontology, geospatial function ontology, and task ontology) to manage spatial and domain knowledge from domain experts which is organized in a task-oriented way. The task-oriented way is similar with a workflow, which define solutions (e.g. what GIS data and functions are needed and how to compose these needed GIS data and functions in a sequence). Thus, when decision makers submit a geospatial problem in the web portal, the corresponding solution in the task ontology can be inferred to answer the problem. Additionally, the semantic of keywords that decision makers inputted, and what GIS data and functions are needed in the task ontology can be inferred automatically by machines. When discovering GI services in SDCI, using inferred semantic search can improve traditional keyword search, since the result of keyword search is usually nonsense and large in quantity, with high recall and low precision (Antoniou & Harmelen, 2004). Discovered GI services can also be automatically composed as a workflow to generate an initial result for decision makers to evaluate. Therefore, the framework contains four parts (e.g. a web portal, an ontologies engine, SDCI, a service chain mediator) to provide a knowledge-based search and to entail GI services in a service chain for automatically generating results. We also conduct a scenario on an earthquake emergency in Taipei City, Taiwan, to test the feasibility of the framework. In the earthquake emergency, emergency geospatial tasks are needed for decision makers, e.g. how many residents in the damaged area? Where are the nearest hospitals or hydrants? In this research, we demonstrate how to achieve semantic interoperability, how to infer in the ontologies, how to discover suitable GI services, and how to compose a workflow based on discovered web services to generate results for evaluating by decision makers. Moreover, tasks can be extensible by GIS domain experts to meet different geospatial problems. In each task, experts only need to contribute their knowledge (i.e. conceptual workflow), including what GIS data and function are needed and how to compose these GIS data and function in a sequence. And domain experts do not need to specific what concrete GI services are needed for every part in the workflow, because the framework will be automatically discovered the needed concrete GI services in SDCI for every task. Some challenges are also discussed and can be improved the framework in the future. However, by this framework, geospatial semantic interoperability and a problem-oriented framework for Web SDSS to solve geospatial problems can still be achieved. We not only presented a knowledge-based 20

21 and GI services-based SDSS, but also provided a flexible and extensible architecture for storing knowledge contributed by domain experts and solving geospatial semantic impediments. 21

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