Dealing with Geospatial Information in the Semantic Web Damian O Dea, Sean Geoghegan and Chris Ekins Command and Control Division Defence Science and Technology Organisation Edinburgh, SA 5111 Australia Damian.Odea, Sean.Geoghegan, Chris.Ekins@dsto.defence.gov.au Abstract Geospatial information plays a critical role to the military user. Geospatial Web Services promise to make such information more readily available. Searching for the right Geospatial information, however, is a difficult task. The Semantic Web promises to facilitate this process by improving the capability to search for information by better expressing the context and meaning of the search query. Meshing the two approaches to create a Geospatial Semantic Web is an idea that is gaining prominence in both areas of Geospatial Information Science and Semantic Web Services. The Defence Science and Technology Organisation is also working to develop a system to explore and develop the Geospatial Semantic Web as a concept. We report our initial thoughts about the feasibility of such a program. 1 Introduction Geospatial information is critical to the military user. Accessing the correct information is a complex task that often requires that the user understand more about the geospatial domain than their training provides. Enabling a query process that allows effective retrieval of the required information is a positive step for a geospatial intelligence officer. Intelligence analysts often need to deal with geospatial information in the course of their duties, such as ascertaining where events may occur and what facilities or logistically important environmental elements are present in an area of concern. The concept of Network Centric Warfare requires the availability of information from sensor-toshooter, and so knowing where to look and where to target is of critical importance to the military user of the future. While the Semantic Web (SW) activities and products address many of the requirements for capturing semantics and expressing ontologies, there is a fundamental lack in regard to capturing even the basics of geospatial information. There is a recognised need for an improvement in the capture of geospatial concepts to better facilitate querying of geospatial databases (Egenhofer, 2002). Furthermore, the nature of geospatial processing is highly mathematical, and thus unsuited for the logical formalisms behind the Semantic Web languages. Such processing needs to be performed externally, but the result of such processing needs to be semantically marked up and made available to classifiers for immediate reasoning. Copyright 2005, Commonwealth of Australia. This paper appeared at the Australasian Ontology Workshop (AOW 2005), Sydney, Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 58. T. Meyer, M. Orgun, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included. 2 Current efforts in the Geospatial Semantic Web Numerous efforts are currently active within both the Geospatial Information Science and Semantic Web world. Some of these will be reviewed in brief in this section. The University Consortium for Geospatial Information Science identified the Geospatial Semantic Web as a shortterm research priority in 2002. Additionally the development of Spatial Ontologies has been identified as a long-term Research Challenge (Mark et al., 2000) by the consortium. The key areas of interest to the participants involve retrieval of, and interoperability of, spatial information through semantic mechanisms, the development of semantic ontologies for both the geospatial and ecological cases a distinction drawn by Fonseca, et al. (Fonseca et al., 2002a) in 2002 - and development of Ontology-Driven Geospatial Information Systems (ODGIS) (Fonseca et al., 2002b). The Wisconsin Land Information System (WLIS) described by (Wiegand et al., 2003) is a means of providing full-fledged DBMS style querying across multiple distinct web-accessible geospatial information systems (GIS). This is achieved through a technique of ontology mapping from a global ontology (encoded as an XML document) to the diverse schema of various heterogeneous web-enabled GISs. The technique, as described in (Cruz et al., 2002), is really a solution to database schema mapping and overcomes the issues of syntactic heterogeneity in GIS data models, while relying upon the expert driver of an Agreement Manager to identify the semantic homogeneity of the concepts encoded in the databases. The query language used in the WLIS is XQuery, and a query processor forms sub-queries for each distinct data source, based upon the agreement (schema mapping) developed through the Agreement Manager above. The global ontology used for the basis of the mappings is a taxonomic classification of land use concepts, and does not contain any relationships between these concepts. At the W3C Workshop on Rule Language for Interoperability (April 2005), several use cases were presented to drive the effort of developing a standard webbased language for expressing rules. In use-case 6.1 Chen, et al. presented the need for the proposed language to express and process geospatial inference rules (Chen et al., 2005). Rules are needed for geospatial processing to deal with situations where reasoning moves from the geometry level (spatial representation of real world features) to the topology level (rules defining how spatial features relate to one another). For example, at the geometry level one may define a relationship between two spatial features to capture the relationship that the first object contains the second, or that the first crosses the second. A topology instead defines a rule that governs how a group of spatial objects behaves, e.g. no spatial feature in the group may cross another (ensuring boundaries remain contiguous instead of overlapping).
Additional requirements include enabling data interoperability by using rules to convert from one geometric representation to another or converting units of measure between different schemes. Another W3C Workshop, Frameworks for Semantics of Web Services (June 2005) saw a presentation from Lieberman, et al. (Lieberman et al., 2005) discussing the evolution of semantics for Geospatial Web Services. This presentation focused primarily on the Geospatial Semantic Web Interoperability Experiment described below. The emphasis of the paper was on the requirements for developing the geospatial semantic web further, as although the current technology goes part of the way to meeting the needs of this project there are still remaining aspects that are not available. These include a means to enable the processing of geospatial relationships and mechanisms to be able to describe the service content of the web services supporting the GSW. The Open Geospatial Consortium standards provide mechanisms for geospatial web services to describe the capabilities of the service itself, but require greater detail to support self-description of the dynamic content of many such services. The Geospatial Semantic Web Interoperability Experiment (GSW-IE) is a project being conducted by the Open Geospatial Consortium (OGC) in 2005. The intention is to develop a means of expressing spatial queries in a semantic manner (i.e. with an ontology) and provide webbased services to fulfil these queries. The program involves participants from Northrop-Grumman, BBN Technologies, and the National Geospatial-intelligence Agency (NGA) as well as several universities, and the Defence Science and Technology Organisation (DSTO) has Observer status on the project. The experiment requires the development of several ontologies, including a geospatial feature ontology for expressing the query, a service ontology to describe the web service function of a Web Feature Service Filter Encoding (WFS/FE) (Vretanos, 2005), and domain ontologies to define the specific domain of interest for the experiment. The base geospatial ontology will closely match the key conceptualisations captured in the Geospatial Markup Language (GML) specification (Cox et al., 2004) as well as geospatial metadata standards such as FGDC (Federal Geographic Data Committee, 1998). GML is an XML grammar encoded in XML Schema supporting the transport of geospatial information across the Web. As such it is a purely syntactic language and lacks explicit semantic information about the objects, relationships and functions described by documents that conform to the schema. The key concepts of GML are laid down by the OGC Abstract Specification1, which acts as a roadmap for the development of the implementation specifications produced by the OGC. The service ontology expresses the contract fulfilled by the WFS interface to support the response to geospatial feature requests. It is described in OWL-S, a language intended to describe Semantic Web Services. 3 Towards a Geospatial Knowledge Infrastructure Within DSTO there is an ongoing work program to develop a demonstration of a Geospatial Knowledge Infrastructure (GKI). The GKI is intended to provide a geospatial or intelligence analyst with numerous capabilities not available today. These include the ability to access data from diverse sources using a single web-based application, supported by semantic querying and browsing. As such the GKI plays an information integration role. Furthermore an analyst is empowered to make assertions into the knowledge base to explore the entailments of such assertions in a sandboxed model, and to commit useful knowledge back to the communal knowledge base. The intention of the prototype is to explore the applicability of semantic web technologies and the automatic generation of geospatial knowledge in enabling shared geospatial intelligence at a strategic level. Exploration of semantic web technologies include the suitability of ontology languages for expressing geospatial concepts and relationships, and the use of ontologies for integrating diverse data sources that share a common information domain. The exploration of automatic generation of geospatial knowledge looks at the capability of description logic (DL) classifiers and reasoning technology to deal with geospatial relationships and processing to derive additional facts that are not explicitly stated in the knowledge base of such a system. Initial development efforts were guided by a desire to understand the nature of the work being performed by geospatial intelligence officers. A set of use-cases was generated to inform the development of the demonstration prototype. The general domain of interest that we are using to explore the development of the prototype deals with returning evacuees from different geographic regions within an area of operations in a fictitious environment, while being able to determine infiltrators and possible terrorists within that group of persons. A candidate set of seven core geospatial processing services is identified as requirements for the GKI. These are (1) Address Finder; (2) Place Finder; (3) Proximity; (4) Query; (5) Route Finder; (6) Distance/Time; and (7) Line of Sight/Field of View. Each of these services is required to support numerous types of geospatial processing queries potentially through an aggregation of the outputs of the service. Combining these services in novel ways by channeling the output of one service to another provides an analyst user of the GKI with considerably more power. It is feasible that additional services that have not been identified here may be required and these would need to be pluggable functionalities. The core services need to be described in a semantic language such as OWL-S so as to be made available to SW applications. The results of these (possibly aggregate) services need to be presented in a semantic language as well, allowing them to be stored in the knowledge base of the GKI. Provision of both of these functionalities empowers the analyst with geospatial processing capability outside the scope of current SW technology. Several commercial technologies were evaluated to determine their applicability to the roles of key reasoning component, information integration enabler and knowledge repository. The evaluation considered features such as availability of automatic inferences and consistency checking of ontologies, the ability to differentiate explicit and inferred statements, query support at the data level (retrieval of information from support databases, or external web services), and query language expressiveness and functionality for querying the ontology or instances. Several ontology editing tools were investigated for their ease of use and support for the development of ontologies in the OWL-DL dialect. An early decision was made that all ontologies in the system would be constrained to OWL-DL to seek to maximize the reasoning capability of the system, as this was considered a primary attraction of SW technology. Not all of the ontology editors are commercial
products, and in fact some non-commercial efforts compare favourably to commercial efforts, in regard to dealing with the correctness of the OWL documents produced. 3.1 Semantic Information Demonstration Environment (SIDE) DSTO has developed the Semantic Information Demonstration Environment (SIDE) as an exercise to explore the possibilities of Semantic Web technologies for Geospatial and Intelligence Analysis. SIDE is a web application built on J2EE and Apache Struts technology. It uses the Jena API as its internal model. Jena provides a Java API that deals with ontology concepts such as classes, properties and instances. The backend storage uses the Kowari triple store. Kowari's support for the Lucene text search engine and its itql query language is used to provide an initial query functionality that 'jumps' the user into the graph. Jena is then used the browse through the graph in response to users clicking hyperlinks. Currently SIDE allows users to upload both RDF datasets and OWL ontologies which are then stored in Kowari. Eventually it will be developed to allow users to provide mappings between relational databases and ontologies. SIDE will then query the databases on the fly. However, our initial goal of exploring the possibilities of Semantic Web technology is well met by uploading OWL documents. Providing RDBMS support is more of an enterprise enabling activity which we expect to have its own set of issues such as being able to reason over external instances in a scalable way. This is something we will have to assess as the technology matures. SIDE provides a user with the ability to make assertions about and between instances in their own personal space. This allows them to test the implications of assertions without affecting other users views of the data. Some of the questions we aim to answer with this aspect of SIDE is whether DL reasoning provides enough entailments for this type of analysis and how do we ensure that users can not make assertions that produce inconsistencies. There are also other issues to consider such as presenting and filtering large lists of possible assertions and how to differentiate user assertions and inferences from base data. SIDE aims to be entirely data/ontology driven. The aim is that no ontology-specific code is required and the display, editing and creation of information are driven by the ontology. This has been partially successful; however, there are some displays that require using ontology specific classes or properties. E.g. the act of displaying an instance on a map depends on knowing the name and meaning of coordinate properties. What distinguishes SIDE from a more conventional approach, such as RDBMS with spatial operators, is the use of ontologies to map queries to database schema. This approach allows data source vendors to provide an (access) ontology describing the concepts and relations in their data source. The core ontologies of SIDE can be mapped by hand to these access ontologies, as shown in Figure 1 below, and queries can then span diverse, heterogeneous data sources. Such data sources could include geospatial data bases, geospatial web-services, and (local) RDF documents that conform to a published OWL ontology. SIDE users can assert mappings to the access ontologies and perform queries across multiple data sources through a single core ontology-based query. Figure 1 Querying multiple heterogeneous data sources 3.1.1 What would the system enable users to do? The types of questions that could be posed to the system come from numerous concerns: logistics, counter-terrorism, intelligence, etc. As an example of a logistics question, consider Can I land a P-3C Orion at any airport in Papua New Guinea? Here, land takes a non-geospatial aspect, referring instead to the process of bringing a plane safely to rest upon the ground. Our information sources about airports may not contain any information about capabilities in regard to military aircraft, and so an equivalence between P-3C Orion aircraft and a class of commercial aircraft may need to be asserted (or made available in an instance ontology) to the proposed system. Airport is a recognized type of geospatial feature, and Papua New Guinea as an instance of a geospatial feature such as a country. In the question, in indicates a geospatial containment relationship that also needs to be captured in a geospatial ontology being used to address the question. A counter-terrorism type of question could be How many associates of proscribed person X are in Australia right now? perhaps followed by the more relevant question of What was their last known location? Associates describes a relationship between persons that indicate that the person X knows of the persons in question, and the fact that we are looking for instances of the class of Person is inherent in the question. The geospatial constraint in Australia has a similar meaning to in Papua New Guinea in the example above, limiting the total number of results for the query. As phrased the query seeks a numeric answer, but the following question seeks to take the actual person instances and seed another query with this result set. 3.1.2 How the system will be used The process flow of an analyst user of the system is likely to be initiated by a search (perhaps coupled with browsing the result space). This can isolate a result set of interest that may need to be queried for further refinement, or presented as a constraint in subsequent related queries. Analysts can then create assertions, such as additional relationships between key instances of interest, or between concept classes. The implications of these assertions may need to be explored
( what if? analysis) and should valuable insights be obtained, publication of the assertions or derived knowledge back into the community domain for others to exploit. It is a basic assumption that an operational system would have a common knowledge repository from which all users would be able to draw their ground truths. Users need to be able to make assertions of discovered facts back into the knowledge base, in addition to speculative considerations about the world at large (as in Figure 2 below). When making speculative assertions, the user needs to be able to do so within a private enclave so as to not affect other analysts work at the same time. This ensures that such assertions can represent more belief-oriented hypothetical considerations rather than knowledge/fact-oriented statements. Exploring the implications of such assertions, users are able to engage in what-if? analysis to determine the outcomes of such speculation. Should there be useful conclusions drawn from this analysis the results should be publicised back into the common knowledge repository to contribute to further analysis processes. In addition to keeping the individual user s speculative enquiries private, allowing users to operate in enclaves also supports a manageable degree of reasoning. Not all the domain ontologies available to the SIDE system should be presented to each and every user. Logistic analyses may not be facilitated by making ontologies specific to counterterrorist analyses part of the reasoning process. The high time cost of performing reasoning on a multitude of ontologies means that such enclaves also serve as an improvement to the efficiency of SIDE. Figure 2 Analyst Workflow 3.2 Querying Performing a semantic geospatial query is a four step process governed by the knowledge repository that acts as the hub of the system, as shown in Figure 3 below. Within the SIDE web application, an analyst creates a query using the query interface. This query is then evaluated by the Knowledge Repository, and the results reported to the client, while being made available as a basis for any additional queries if they arise. The first step to evaluating the query is checking against base facts (knowledge derived from the external data sources) and asserted hypotheses created by users of the system. Secondly, a DL-reasoner is applied to generate any logical entailments of the instances and relationships within the knowledge repository. Rule-based reasoning is applied to the statements of the repository to generate additional geospatial knowledge that is not available to DL-reasoning processes. Rules need to implement both topological reasoning such as Region Connection Calculus (Randell et al., 1992) or Allen s Interval Calculus (Allen, 1983) and geospatial processing as unit of measure conversion. This additional knowledge needs to be made available to the DLreasoner to generate additional entailments from this new knowledge. Both the DL and rule-based reasoning are considered to be qualitative reasoning, mimicking a cognitive level of reasoning. If the query cannot be fulfilled by the knowledge available to the repository at the time (no statements are available to answer the query) then additional, computational or quantitative reasoning may be required to facilitate the response. In this instance the geoprocessing services would be called upon to perform the mathematical calculations and report the results back to the knowledge repository, where they can be stored as additional knowledge. Figure 3 SIDE Architecture 3.3 Query interface issues Developing the query interface is another area that is in need of exploration. The intended user of tools such as SIDE or ultimately an operational GKI will not be deeply familiar with the technology underpinning the semantic web. As such certain assumptions that they may make regarding how the system conceptualizes the world may lead to inadvertently introducing inconsistencies into the knowledge base. A query interface needs to present a simple enough view of the knowledge space as to be approachable by the novice user, without reducing the availability of the more powerful aspects of semantic searching. The model by which a user is able to retrieve information needs to be considered as well. Should the system present a query interface akin to the highly familiar Google or a browsing approach where a user may follow chains of hyperlinks representing the properties of objects within the system? Alternatively, a hybrid approach such as presented by EBay is a consideration. 4 Limitations of the current technology In developing SIDE we have also experimented with a number of inference methods. Since Kowari does not yet support reasoning at the knowledge base level we have been limited to using Jena's reasoning interface. We experimented with on-the-fly, query-time reasoning using both Pellet and Jena reasoners but found this to be too slow for a web application and these reasoners load the entire database into memory. The current method is to classify each dataset as it is loaded and then provide limited query-time reasoning for a smaller set of entailments. Identifying a useful set of entailments that support the types of assertions a user may wish to make is also an issue that needs to be addressed.
We have discovered that there is a limited capability with inferences from OWL to deal with geospatial relationships and processing. In drawing a distinction between the qualitative (DL or rule-based) reasoning versus the quantitative (mathematical) geospatial calculations we are starting to gain a good separation of the distinct needs of the GKI. Currently there is little in the way of reasoning support in DL-based formalisms or rule languages to handle the more complex spatial or temporal calculi. Both DLreasoning and rule-based reasoning are requirements for our intended client base to be able to gain valuable insights from the GKI. Example 1: knowing that Melbourne is within Victoria and Victoria is within Australia and that within is a transitive relationship; we can automatically gain (from inference) that Melbourne is within Australia. Example 2: knowing that Melbourne is within Victoria and that within is an inverse relationship to contains, we can also infer that Victoria contains Melbourne. Example3: knowing that Damian insamelocationas Sean, and Damian islocatedat Adelaide does not get us enough additional knowledge with inference alone. Without a rule to indicate that X insamelocationas Y, and X islocatedat Z then Y islocatedat Z, we cannot infer Sean islocatedat Adelaide. Examples 1 and 2 show how simple DL-reasoning entailments can support limited types of semantic geospatial querying. On the other hand, Example 3 is a case for requiring additional reasoning capability that can only be provided by a rule-based reasoner. Furthermore, handling a query that requires identifying instances of certain classes of features that are located within 500 km of a specified point (which may not be stored within the knowledge base, but rather encoded as part of the query) requires far more expressiveness than OWL ontologies can provide. 5 Conclusion The development of geospatial ontologies currently being undertaken in several distinct consortia, and although numerous efforts show promise, the semantic web is not ready to provide the expressiveness in terms of rules and language for geospatial applications. Rule languages are needed to be able to provide the expression of geospatial relationships, and rule-based reasoning needs to be integrated with the usual DL-based reasoning to allow knowledge arising from either reasoning process to inform the other. 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