Representing Spatial Norms

Transcription

1 Representing Spatial Norms BA Thesis (Afstudeerscriptie) written by Zora Jurriëns (born August 30th, 1984 in Amsterdam, Netherlands) under the supervision of Dr. Radboud Winkels, Drs. Rinke Hoekstra and Drs. Erik Hupkes, submitted in partial fulfillment of the requirements for the degree of BA Kunstmatige Intelligentie at the Universiteit van Amsterdam.

2 Keywords Abstract Since the different sources for spatial planning are not centralized, an application is being developed that can inform people about where they are allowed to perform a plan or a certain activity. An obvious choice for such an application, is to make use of Semantic web technology. In support of this application the underlying knowledge representation of spatial norms (OWL) is investigated in this paper. The goal is to detect conflicts between norms and detect conflicts between a concrete case and norms. Three different structures to represent spatial norms are discussed. Furthermore, the more complicated norms involving calculations are examined. Semantic web, OWL, Spatial planning, Sparql, Use Case, Geoserver

3 Contents 1 Introduction 3 2 Spatial Plans, Norms and Use Cases Spatial Plans Norms Use cases How to support this domain Semantic web and OWL Difficulties using OWL Calculations in OWL Exchange in OWL Representation First representation Second representation Third representation Final representation Representation Complicated Norms First Category: complicated norms that do not need calculations Second Category: complicated norms that do need calculations Conclusions, Discussions and Further work Future work References 16 A Representation 1 17 B Representation 2 20 C Representation

4 1 Introduction Land use is bound by a large number of rules and is an important legal domain within legislation. Several regulations are in place to determine whether a plan or activity with regard to the use of land is permitted. These regulations include all levels of land use and determine the organization of urban plans, regional plans and zoning plans. The variety of plans makes it hard to design a project that complies to all regulations and thus having a profound impact on the design of plans from both citizens, local governments and cooperation alike, creating several reasons why they would need access to the regulations. Unlike most legal domains, where textual descriptions are the most dominant source of law, the main source to gain information about spatial regulations is in the form of maps. A spatial plan can be represented as a map with color coding s and symbols to represent the different norms applied to that area. The more extensive description of the norms can be found in an additional textual explanation. Currently a direct link between both sources is often not available making the process of accessing this kind of information long-wired and insufficient. For instance, to find regulations for a specific area one would need to collect all the different pieces related to that area, read trough the textual content and search the maps related to the content. Furthermore most sources are not centralised and it can be tedious an tiresome work to collect all the pieces that are needed for the design of a plan. Several applications are being developed to overcome these problems with the aid of the Semantic Web technologies. The Leibniz Center for Law has been working on making the link between the maps and the additional textual explanation explicit. This makes it possible for all parties to retrieve information about what the regulations regarding spatial norms are and where they apply (Winkels, Boer, & Hupkes, 2007). They made use of a distributed approach with Semantic web technologies like OWL and XML and a viewer application called Legal Atlas was created. This paper describes research to improve the underlying knowledge representation in an already existing application called FEED (Berg, Latour, & Overgoor, 2009) which is similar to Legal Atlas. FEED is a small test application where a user can view which area s have a certain restriction or can click on an area and see it s possible designations as can be seen in Figure 1. When a conflict situation is detected between spatial norms, e.g. an area that has a two conflicting designations, the conflict area in the application is coloured red. The focus of this research is to investigate how different representations of spatial norms in OWL-DL best suit this kind of application and support normative reasoning. The goal is to detect conflicts between norms and detect conflicts between a concrete case and norms. Furthermore, it is investigated how to be able to use the more complicated norms like having a norm with information about a radius. Since these kind of applications become more extensive, the need to have an overview of the different representations arises as do the need to represent more complicated norms in OWL. Representing calculations and numbers in OWL has always proven difficult and to be able to represent a norm with a radius is something not yet accomplished. The rest of this paper will be organised as follows, an overview is given of possible norms and use cases in section 2, the use of Semantic web and OWL is explained in section 3, the different representations of the spatial norms are discussed in section 4, the representation of the complicated norms are elaborated in section 5 and end with the conclusion and related work in section 6. 3

5 Figure 1: 2 Spatial Plans, Norms and Use Cases 2.1 Spatial Plans Spatial planning can be viewed as a process where the available space is carefully divided between different designations. Designations are the eventual goal of a certain area, e.g. land with designation nature is destined for all categories of nature. Other examples of designations are: residential, industrial, nature, sport, water etc. The main designations can be viewed as a super class for several designation related sub classes. For instance, designation recreation has two sub classes; daily recreation and holiday recreation, both of which also have sub classes that are more specified, e.g. daily recreation has a subclass petting zoo. The IMRO2006 is an information model for spatial planning from the Dutch government, it is a standardized model with definitions needed for describing spatial plans. This standardized model of IMRO provides basic designations that are used throughout this research. Spatial planning provides an efficient, structured use of land and different sorts of regulations in the form of plans. In the Netherlands the most familiar plans are zoning plans and regional plans. Since spatial planning is a domain of law where space plays important role, it is not surprising that the main source of a plan is in the form of maps. A zoning plan can be viewed as a map with color coding s and symbols to represent the different norms of a specific area. For instance, the designation factory can have the color blue and the map will show blue polygons where this designation is allowed. In order to get a more extensive description of such a norm, one can read it in the additional textual explanation. 2.2 Norms An important part of creating an application that is able to tell whether a plan is permitted at a certain place, are the norms that determine the outcome. Norms are rules that can be viewed as the general structure of a spatial plan. In order to create a generic representation of the norms, the variety of norms is investigated. Unfortunately there is no standardized set of norms, making it necessary to extract the information out of different spatial plans. Several zoning plans have been accessed for this research, including zoning plan De Steiger from the municipality Almere 1. In this plan numerous norms are addressed; to give an illustrative example: Op bedrijventerrein De Steiger wordt, conform het gemeentelijk beleid, de vestiging van 1 seksinrichting middels een wijzigingsbevoegdheid mogelijk gemaakt. De afstand tussen de vestigingslocatie tot een woning moet minimaal 50 meter bedragen. 2 1 Bestemmingsplan De Steiger, Gemeente Almere. documentnr september Translation: At business area De Steiger is, in accordance with the municipal policy, the establishment of one brothel by 4

6 The extraction of all the different norms from this zoning plan would result in an abundance of specific norms that are not needed for the application. Since it is not useful to have all the specific norms that apply for just one zoning plan, a generic solution is preferred. One would like to filter the different norms and find the most general structure behind the different type of norms, thus creating a generic solution for every type. The process of extracting norms from different spatial plans was repeated and eventually the result was filtered such that the most generic norms would remain. The outcome of this process can be examined in Table 1 and 2, examples are included to create a clear idea of what a type of norm entails. Type of Norm Within designation A (not) a subclass of A is allowed Within designation A function B is (not) allowed, where B can be a subclass of designation C Example In designation Public Area several sub designations are allowed, such as Water and Green. In designation Green, Cycle Paths are allowed. Table 1: Simple type of norms and examples for clarification Type of Norm Within designation A compensation of A is (not) allowed in a designated compensation area B Designation A is (not) allowed to be higher than.. meters Within designation A function B is (not) allowed provided that in a radius of.. meters (not) function or designation C Within designation A is designation B (not) allowed as.. percent of designation A Example In the designation Forest is construction, as a compensation of felled forest allowed in a designated area. The hotel that s being built can not be higher than 30 meters. In the Netherlands a brothel is only allowed if in a 50 meter radius no Residential designation exists. Within the designation residence a working designation is allowed provided that it takes up at most 25 percent of the residence designation. Table 2: More complex type of norms and examples for clarification The norms from the first table can be considered to be easier to model, while the norms from the second table become harder to model. This is apparent if one for instance looks at the different examples, modeling a norm with percentages in them is more difficult than one without. The different norm types can appear a little abstract, especially because of the use of designations. However every designation can be replaced by it s super or subclass, creating more understandable rules. One can even go all the way down and reach the external node of the designation hierarchy, reaching the most specific designation. For instance, this can result in more specific norms, where designations are similar to description of a function. Examples can be viewed in Table 3. In the original FEED project the emphasis was not on the underlying knowledge representation, but on the application itself. Only the more simple norms were included in the application. Part of this research is to investigate how to model the more difficult norms. To get a clear idea on what norms have to be taken into account, some scenario s are considered in the form of use cases. means of an amending power possible. The distance between the location of the establishment and a residence should be at least 50 meters. 5

7 Type of Norm Around object A B (not) is allowed from.. meters Function A can be at most.. meter Example Around a LPG filling point it is not allowed to have sensitive designations within a radius of 60 meter. The addition can be at most 3 meters. Table 3: More specific type of norms and examples for clarification 2.3 Use cases Use cases in general describe a system from a user s perspective. A use case describes the behaviour of a system as it reacts to a signal from outside the system. This signal can be from a different system or it can also be an action from a person. The intended result is to create a model where it becomes clear what can be done with a system and who is able to use it. The who can vary from other systems to people who interact with the system. For instance, an ATM use case can easily represented 3, the ATM is the system and the outside source is a person. Use cases are highly popular in the field of engineering, but a variety of instructions exist. The most common entities to describe in a use case are; the actor, the system and the goal. The considerations to make when starting to create a use case, is what scenarios would one want that the system is capable of handling. In the article (Hoekstra, Winkels, & Hupkes, 2009) several use cases and scenarios are described on the basis of simple and more complicated norms. In the article the same Legal Atlas as mentioned in the introduction is extended with supporting the users in answering questions like where will I be able to build this? The Legal atlas is broadened with instead of just concept-based information retrieval, also map-based normative reasoning. Users are now able to select an area, a designation for that area and the system will then asses if this designation is allowed at that specific area. This enables users to experiment with their plans even more and for instance find out where to best build a new pool. From the information retrieved out of this article follows a similar approach to create use cases based upon the different types of norms the system will need to handle and the actions (goals) the actor requires of the system. In this research the types of norms this system will need to be able to handle are stated in Table 1and 2 in section 2.2. The actions that the actor would like to be able to perform can vary from wanting to check where a certain action is allowed, e.g. where can I build a factory?, to wanting to check if a certain action is allowed at a certain place, e.g. can I built my factory here? An action can even represent an entire zoning plan that the actor would like to compare with an already existing regional plan in the system. The variety of actions and type of norms require more than one use case. Three different use cases are designed for this research. The first use case (Figure 2) is the most general form of the system. As can be seen, the actor can load a plan after which the plan is temporarily saved. The system tests if the plan complies to the existing norms and gives the result. The form of the result and the plan is not specified in this use case making it a general approach of the system. An already more specified use case of the requirements of the system can be viewed in Figure 3. In this particular use case the attention is directed on visualising the answer of the question Where can I do this?. Only a designation can be loaded and the results set would be everywhere this designation is allowed. Furthermore the outcome is visualised if this is possible. Another more specified use case answers the question Can this go here? (Figure 4). This use case represents the more difficult actions, this system would need to handle. Here a plan can be loaded, but also the location where one would like to realise the plan. This sort of action makes it far more specific needing only to find the existing norms at that location. Furthermore, this use case specifies the result. When the result entails a conflict, a conflict is returned and provided that it can be visualised it will be visualised. As stated before, a use case can give a good overview of what the system needs to be capable of handling. 3 See: 6

8 Figure 2: Most general use case Figure 3: Specified Use case This makes it easier to start modeling the representation of spatial norms needed for the system. 2.4 How to support this domain To satisfy the previously mentioned use cases a web based application with a map based interface is being developed. In support of this application the representation of spatial norms in OWL is explored in this paper. The focus is to investigate how different representations of spatial norms in OWL-DL best suit this kind of application and support normative reasoning. The goal is to detect conflicts between norms and detect conflicts between a concrete case and norms. Furthermore, it is investigated how to represent more complicated norms. This research is based upon earlier work and predecessors of this application that already exist, for instance Legal Atlas (mentioned in the previous sections). Legal Atlas is a prototype viewer application that makes the link between different sources, maps and textual explanation, explicit (Winkels et al., 2007). Another application is the test application (Berg et al., 2009), which is a step further than Legal 7

9 Figure 4: Specified use case Atlas. With this application it is possible to see where something is allowed and where something is not allowed and instead of using a knowledge representation that supports reasoning, the information is queried with SPARQL (explained in section 5.2). This research will take a next step and reasoning about spatial norms will be applied. In the next section a brief explanation for the use of Semantic web technologies in this research, to continue the representation for spatial norms in OWL. 3 Semantic web and OWL Since the sources concerning spatial plans are not centralised, not of the same format and the spatial plans can be changed, the use of Semantic Web technologies become an apparent choice. The main idea of the Semantic web is to create resources that are machine readable, this because a machine is not capable to retrieve useable information from the web if no machine readable descriptions are added. To create machine readable descriptions, metadata and underlying ontologies have to be attached to the data. Metadata provides the data with semantics, ontologies describe the structure of the knowledge making it possible for the machine to infer information with reasoners, thus retrieving more meaningful results. The Semantic Web is based mostly upon XML and RDF, both developed by the World Wide Web Consortium 4. RDF can be seen as a way to represent data in statements of the form object, predicate and subject. The OWL Web Ontology is an addition to the basic semantics of RDF. OWL consists of classes, individuals (instances of classes), object-and data properties. It is based upon a logical model 4 See: 8

10 which makes it possible to describe and define concepts. This logical model allows a reasoner to check whether a created ontology is consistent and infer structures where a concept is compliant with the given definitions 5. Furthermore OWL has a richer set of operators, including union and negation and is designed for applications that need to process the content of information and perform reasoning tasks on the information. There are three versions of OWL available, in this paper OWL-DL is used, this version is the most expressive and is still computationally complete. The expressiveness of OWL doesn t make it ideally to use for the representation of the relations between spatial norms (see 3.1), however one would like to use Semantic Web language and a reasoner (Hoekstra et al., 2009). Pellet provides a reasoning for OWL-DL that is sound and complete, meaning that all the information that can be inferred will be inferred if the model is consistent (Sirin, Parsia, Grau, Kalyanpur, & Katz, 2007). Pellet is the reasoner used for reasoning tasks in this paper. 3.1 Difficulties using OWL OWL is known to have some difficulties, for instance the inability to use calculations in OWL. This can pose a problem for representing the more difficult norms of this research. In the following subsection some related work on the difficulties with using OWL is described to create an idea where the problems are. In both articles a solution is proposed to bypass the problem Calculations in OWL In biological domain it is often necessary that numeric data is transformed before being used, i.e. a typical example is to calculate the Body Mass Index from height and weight. In (Iannone & Rector, 2008) a solution is proposed to work around the inability of OWL to work with calculations. The formula s necessary are attached to the data properties as annotations. If then an individual has such properties (in OWL) a filler is computed after a preprocessing phase (ontology is then sent to a reasoner for classification). Another option is to attach the formula s to classes, this however can be troublesome because non primitive classes can not be annotated, this would mean that every class should be defined as a primitive class which can be redundant and unnecessary. This approach is meant as a temporary solution and early exploration of including calculations into OWL Exchange in OWL Representing exchange in Descriptive Logic languages like OWL DL poses a problem (Hoekstra & Breuker, 2008). The diamond shaped structure that represents exchange is a structure that can not be represented in the tree like structure of these languages. An exchange is a process or an action that binds two actions by reciprocity, for instance a seller receives the same money as the buyer gives to the seller. The authors propose an approximation of the diamond shaped structure in five straightforward steps and thus being able to represent actions or processes of exchange. 4 Representation The focus of this research is to investigate how different representations of spatial norms in OWL-DL best suit the system described by the use cases in section 2.3. The goal is to detect conflicts between norms and detect conflicts between a concrete case and a norm. The representation can be divided into three different parts: the designations, the regions or polygons and the spatial norms. The standardised model IMRO is used for the type of designations, as mentioned in section 2.1. The regions represent an area on a map and the spatial norms represent what is allowed and disallowed (Table 1). To explore which representational structure best satisfies the need of this application it is not necessary to model the complicated norms, because these norms will not change the 5 9

11 basis of the structure. Therefore only the easier spatial norms are modelled, the modeling of the more complicated norms is addressed in section 5. The starting point for all the representations is to model a small structure, where a polygon or a region with a certain designation has to be correctly classified. In the following subsections three different representation based upon the following structure, are explained: Nature and Recreation are sub classes of Designation. It is not allowed to have overlap between Recreation and Nature. To keep it simple for the example the relation overlap is not symmetrical, Nature can overlap with Recreation. However, there is one exception. It is allowed to have overlap between a Petting Zoo and Nature. A Petting Zoo is a subclass of Daily Recreation, which is a subclass of Recreation. 4.1 First representation The first representation consists of three main classes (Figure 5): Designation, Region and Situation. The class Designation has an individual for every class or subclass of Designation, so eventually for every designation there is also an individual for that designation. The class Region has individuals for every region one would like to have in the structure and each individual falls under a specific region, e.g. individual region nature falls under the class RegionNature. The region classes can be divided into more specific sub classes, but for the explanation of this structure it is redundant. An individual of Region can be viewed as a polygon on a map. The structure relation between Designation and Region is that each individual from Region has a property:hasdesignation with an individual from Designation. So if one would like to add a new polygon to the model, one would need to add a region individual and give it a property:hasdesignation with a designation individual. In the class Situation the norms are stated, in this case the norm NoOverlapRecreationNature and the exception to that rule ExceptionPettingZoo. This means that these norms are represented as classes and if an individual is classified under either one of them, it means that the individual contradicts the norm. Both classes are represented in way to find individuals that contradict a norm, because then a conflict is detected. To illustrate how these norms are described: NoOverlapRecreationNature RegionRecreation and overlap some RegionNature ExceptionP ettingzoo RegionRecreation and designation some P ettingzoo and overlap some RegionNature Both state a situation that can be seen as an anomaly. An individual can be classified under either these classes if it has a property:hasoverlap relation with a region nature individual. If an individual is classified under NoOverlapRecreationNature it is disallowed by the norm. Consider the example, there is polygon recreation and it overlaps with polygon nature. This individual would create a conflict. There is also another type of conflict possible, if an individual is classified under ExceptionPettingZoo. This is a normative conflict, because the individual is disallowed by the norm NoOverlapRecreationNature and allowed by the norm ExceptionPettingZoo as can be seen in Figure 6. To easily find normative conflicts, the norms are also modelled as individuals and the properties allowed and disallowed are added to the model. This creates the opportunity to add the ExceptionPettingZoo allowed by norm exception-petting-zoo as description of a super class of ExceptionPettingZoo. The same oppurtunity arises for NoOverlapRecreationNature, where the description of a super class becomes NoOverlapRecreationNature disallowed by norm no overlap recreation nature. These two super classes ensure that after classification, the individuals that cause a contradiction or a normative conflict get a disallowed by norm or allowed by norm as a object property assertion. An individual petting zoo would get both allowed and disallowed as property assertion and can therefore easily be found. When found, a problem arises, which norm should prevail? The solution can be resolved by lex speciales discussed in (van de Ven, Hoekstra, Breuker, Wortel, & El-Ali, 2008), which entails that the most specific norm that applies to the situation prevails. However, in OWL it is easy to see which norm is the most specific (leaf node) as is clearly visible in Figure: 6, but within a system it is hard to tell and special queries need to be made to retrieve this information. 10

12 The good thing about this structure is that it is very clear and intuitive, because everything is represented in classes. The bad thing is that the Designation and Region both have individuals for every class and subclass, which is a lot of double work. 6 Figure 5: Before reasoning 3 Figure 6: After reasoning 3 6 Primitive classes are coloured yellow system classes are shown in a paler yellow (Thing). Defined classes (classes that have at least one equivalent class) are coloured in orange. from 11

13 4.2 Second representation The second representation is similar to the first representation, except it doesn t have a class Designation. Instead it has a hierarchy of designations individuals that is created with the properties: broader and narrower. The property broader can be seen as the property subclassof, e.g. a petting zoo is narrower than daily recreation or daily recreation is broader than petting zoo. A region now has a property:hasdesignation with an designation individual as is illustrated in Figure: 7. The norms are described in a similar way as in the first representation, e.g. Region A can not overlap with Region B. Classification of anomaly (conflict, contradiction) situations is done the same as in the first representation, with the use of lex specialis and the use of the properties disallowed and allowed. The hierarchy of designations individuals fulfills the same role as the Designation class from the first representation, with the advantage that less extra classes have to be created, making it less double work. Another advantage is the use of the properties broader and narrower that come from SKOS (Simple knowledge organization) 7. This is a standard way to represent knowledge organization systems using the RDF and makes it easier to interchange between different files. Figure 7: the diamond shape represents a individual and the round shape a class 4.3 Third representation The third class has only one main class left, Situations. In Situations the different anomalies are described. Every conflict or contradiction is defined in this class and it s sub classes. The designations are modelled the same way as in the second representation, as an designation individual hierarchy (Figure 7, only the diamond hierarchy) with the SKOS properties: broader and narrower. An advantage is that a new polygon can directly have a property to a designation individual and no new classes have to be added. A disadvantage can be seen in the fact that, because of only a designation individual hierarchy, one need to specify the class description very thoroughly. For instance, one must describe a designation and all the designations broader or narrower to reach all the way up or down in the designa

14 tion individual hierarchy as can be seen in the following description: N ooverlaprecreationn ature (designation some (broader value desig recreation)) or (designation value desig recreation) and (overlap some (designation some (broader value desig nature))) or (overlap some (designation value desig nature)) Classification of anomaly (conflict, contradiction) situations is done the same as in the first representation, with the use of lex specialis and the use of the properties disallowed and allowed. 4.4 Final representation Every representation mentioned in the previous subsections has it s advantages. The first two representations have a clear structure and the inherit relations are clearly visible, but both have the disadvantage that there are things double represented. For example, a class of designations and a designation individual for every class or subclass of designations, is creating double work. The third representation doesn t have that disadvantage, but the description of classes has to be much more precise. Since one would like to minimise the double work and make use of the SKOS standards the best representation for this system is the third representation. 5 Representation Complicated Norms In the previous section a representation is chosen for this system. This structure is capable of representing the less complicated norms, however the more complicated norms are not described. In this section the approach to represent the complicated norms is explained. The complicated norms from Table 2 can be divided into two categories, the type of norms with calculations and the norms without. Both categories will be addressed, beginning with the first category: the norms that do not need calculations. 5.1 First Category: complicated norms that do not need calculations Within designation A compensation of A is (not) allowed in a designated compensation area B The compensation norm is written in OWL. This is explained on the basis of the following example: Mister X wants to built a marina on a polygon with designation Recreation, only his plan is bigger than the polygon and it overlaps slightly with a polygon with designation Nature. In this particular area it is allowed to compensate and mister X chooses to compensate the lost Nature with a compensation area. This situation has to be represented as an exception to the class NoOverlapRecreationNature for that rule still applies, recreation is still not allowed to overlap with nature. The exception is that overlap is allowed if there is an compensation area. A compensation area can be represented as a compensation individual or can be for instance a nature individual, like an individual with designation nature or an individual with a narrower designation than nature. For this example the compensation individual is a nature individual. Since it is not allowed to compensate everywhere it useful to only use designated compensation area s, this can be done by adding each compensation individual to the class Compensation. Now only compensation individuals can be addressed. Next the class ExceptionCompensateNature can be modelled, which is described as follows: (compensates some (Compensation and designation some (broader value desig nature)) or (compensates some (Compensation and designation value desig nature)) and (overlap some (designation some (broader value desig nature))) or (overlap some (designation value desig nature)) The class entails that if there is individual that has an overlap between Recreation and Nature, but also has a property:hascompensate with an individual that is nature (or narrower than nature) 13

15 and this individual is an individual of class Compensation it is allowed. The description of this class is of a similar structure as the description of the class ExceptionPettingZoo, it represents a normative conflict(explained in section 4.1). The situation is allowed by the norm Exception- CompensateNature and disallowed by the norm NoOverlapRecreationNature. The most specific norm applies, so it is allowed for Mister X to have an overlap with Nature only if he compensates with an individual of the class Compensation. A drawback of the way this norm is represented is that it is hard to use this kind of norm in the final system. For instance, if an actor would need to indicate that he is willing to compensate a slight overlap, the system would than need to represent all the compensation area s to make it easier for the actor to choose. Next the actor would need to choose a compensation area, only it is not yet possible to indicate a percentage so a whole polygon is needed for compensation instead of the small area that needs to be compensated. Designation A is (not) allowed to be higher than.. meters To represent a norm with height, it must be possible to compare heights. Luckily it is possible in OWL to make comparisons between data properties with integers (>, =, <). A height can than be represented as an asserted data property, e.g property:hasheight 5(integer). The next step is to create a norm that some height is not allowed in combination with some designation. For instance, one can create a norm that says that all individuals with designation recreation (or subclasses) and heights above 5 are not allowed. P olygon and (designation some (broader value designation recreation)) or (designation value designation recreation) and height some integer[> 5 integer] This creates a class where all individuals of designation type recreation and height above 5 are classified as a conflict. A disadvantage of the representation of this norm is that if no height is specified it is always allowed. Furthermore, this norm concerns only specific area s and one would need to create a new specific norm for every area that makes use of height. A possibility is to make standard classes, e.g. Under5m contains all individuals under 5 meters, and combine these classes with the regular norms for such an area. 5.2 Second Category: complicated norms that do need calculations The second category has some difficulties to it, the calculations. As is described in section 3.1 OWL is known to have a problem with calculations, but a solution was presented in (Iannone & Rector, 2008). The authors propose a solution to attach formula s to data properties. Unfortunately this solution does not suffice for the representation of norms from this category, because it only attach s formula s to data properties and this can not be used between individuals. Another option to deal with calculations is to not do them in OWL and use an external technologie instead. There are several technologies that can be used in combination with OWL, a GeoServer can for instance do calculations over geospatial data. Since the already existing application (Berg et al., 2009) works with a GeoServer 8 it is useful to do the same. Furthermore they also use SPARQL 9 for querying OWL, this is used as well. A short description of the technologies: A GeoServer is a server that allows users to share and edit geospatial data. It is possible to retrieve data from the GeoServer with Web Feature Service (WFS) 10 queries. WFS represents an interface for specifying request for geographical features to the GeoServer. Furthermore, it is possible to let the GeoServer do calculations with the use of a filter over a WFS query. SPARQL is an RDF query language and is useful in this system for querying the OWL repository. In the following explanations of the norms the GeoServer shall be a Black box and the WFS and SPARQL queries shall only be mentioned in theory SPARQL: Simple Protocol and RDF Query Language, see

16 Within designation A function B is (not) allowed provided that in a radius of.. meters designation C Since it is not possible to calculate a radius in OWL, this has to be done with external technologies. To explain this norm properly the following example is used: In the Netherlands a brothel is only allowed if in a 50 meter radius no Residential designation exists. It is possible to query all individuals that are brothels out of OWL repository with SPARQL, after that a WFS query is created. This query would need to create a radius around the brothels and ask for the individuals within the radius. Next this query is send to the GeoServer and the GeoServer sends individuals back within the radius. The following step is that these individuals have to be compared with SPARQL to the individuals from the OWL repository and if some are not already in there they have to be added. When a Residential individual is detected this individual has to get a data property:haswithinradius < 50 and in OWL there should be a class NoResidentialIn50mBrothel. If the norm is stated correctly these individuals are classified under this class and indicate a conflict. If such a norm is admitted into the general structure of the system, one needs to take account that this solution has a drawback. This drawback is that if all brothels are queried, it can amount to a lot of individuals that lie within the radius. This can result in a lot of data the systems has to compare, making it not an ideal solution. This is especially true for the use case Where is this allowed? (Figure 3), because this would result in that all residential designations have to be checked on whether there is a brothel in a radius of 50 meters. The use case Can this go here? (Figure 4) doesn t have that problem, because just one area has to be checked. Within designation A is designation B (not) allowed as.. percent of designation A This norm poses a problem, not only because it has calculations with percentages in them, but also because it represent double designations. It has not yet been possible to model such a norm and will be mentioned in section future work. 6 Conclusions, Discussions and Further work In this research an approach has been presented on how to represent spatial norms in OWL. Three different representational structures were discussed and based upon the requirements for this system, the disadvantages from the first two representations weighed more than the disadvantages of the last. The third representation is more efficient and better to maintain. Furthermore the expressiveness of this representation is of the same quality as the other representations. However, it does have a slight disadvantage in describing the classes and some loss of clearness of the structure. Also some solutions were proposed for the representation of complicated norms. The complicated norms without calculations are successfully modelled and included into the representation. On the other hand the complicated norms with calculations posed a problem. The inability of OWL to do calculations caused that these norms were not successfully modelled in OWL. Only in combination with external technologies the norm with a radius could be successfully represented, the other norm is not yet possible in this representation. The overall conclusion is that it is possible to represent the spatial norms in OWL, but that OWL is not suitable to represent all spatial norms in such a way to do calculations with it. The reason why one still would like to use OWL is that it is a Semantic Web language that is suitable for the representation of logic based knowledge structures, furthermore the reasoner provides sound and complete reasoning, meaning that all the information that can be inferred will be inferred if the model is consistent. 6.1 Future work The representation created in this research needs further development. An important part would be that this representation needs to be tested as a component of a system. This is especially necessary for the more complicated norms, because it is important textitif and textithow the complicated norms can be an asset to the system. A recommendation is to thoroughly experiment with the representations and find out how they can be best admitted into the system. Furthermore, there is need for more research on how to represent norms with percentages in them. This is not only useful for the norms with percentages in them, but also useful for the compensation norm, since one would like to compensate only a small 15

17 portion instead of a whole polygon. Additional research can be done to represent norms not yet mentioned in this research. For instance the norm: function A has to be at most.. minutes away from designation B. An illustration for this norm is: the fire department has to able to reach the Residential designation within 8 minutes. This norm might be represented the same way as a norm with a radius, since 8 minutes distance can be mapped as a radius (e.g. radius is the longest route). It it probably useful to combine this with a site for travel planning. Another area of research could be to represent spatial norms with Region Connection Calculus (RCC8)in OWL. RCC8 represents the 8 basic relations that are possible between regions, like Overlaps and Externally Connected (next to). Some research has already been done (Grütter & Bauer-Mesmer, 2007), they represent norms with spatial aspects. It might be a possibility to represent the more complicated norms with spatial aspects, so the GeoServer doesn t have to be part of representing a norm. References Berg, T. van de, Latour, S., & Overgoor, J. (2009). Feed. (Bachelor A.I. Honours program, supervisors: Winkels, R., Boer, A., and Hupkes, E.) Grütter, R., & Bauer-Mesmer, B. (2007). Combining owl with rcc for spatioterminological reasoning on environmental data. Hoekstra, R., & Breuker, J. (2008, October). Polishing diamonds in OWL2. In A. Gangemi & J. Euzenat (Eds.), Proceedings of the 16th international conference on knowledge engineering and knowledge management (ekaw 2008). Springer Verlag. Hoekstra, R., Winkels, R., & Hupkes, E. (2009, June). Reasoning with spatial plans on the semantic web. In C. Hafner (Ed.), Proceedings of the twelfth international conference on artificial intelligence and law (icail 2009). ACM Press. Iannone, L., & Rector, A. (2008). Calculations in owl. (School of Computer science, University of Manchester) Sirin, E., Parsia, B., Grau, B. C., Kalyanpur, A., & Katz, Y. (2007). Pellet: A practical owl-dl reasoner. Websemantics. van de Ven, S., Hoekstra, R., Breuker, J., Wortel, L., & El-Ali, A. (2008, October). Judging Amy: Automated legal assessment using OWL 2. In Proceedings of OWL: Experiences and directions (owled 2008 eu). Winkels, R., Boer, A., & Hupkes, E. (2007). Legal atlas: Access to legal sources trough maps. ICAIL. 16

18 A Representation 1 <?xml version="1.0"?> <!DOCTYPE rdf:rdf [ <!ENTITY owl " > <!ENTITY xsd " > <!ENTITY owl2xml " > <!ENTITY rdfs " > <!ENTITY rdf " > <!ENTITY Ontology " > ]> <rdf:rdf xmlns=" xml:base=" xmlns:owl2xml=" xmlns:ontology =" xmlns:xsd=" xmlns:rdfs=" xmlns:rdf=" xmlns:owl=" <owl:ontology rdf:about=""/> <!-- Object Properties --> <! > <owl:objectproperty rdf:about="#allowed_by"/> <! > <owl:objectproperty rdf:about="#designation"/> <! > <owl:objectproperty rdf:about="#disallowed_by"/> <! > <owl:objectproperty rdf:about="#overlap"/> <!-- Classes --> <! > <owl:class rdf:about="#dailyrecreation"> <rdfs:subclassof rdf:resource="#recreation"/> 17

19 <! > <owl:class rdf:about="#designation"> <rdfs:subclassof rdf:resource="&owl;thing"/> <! > <owl:class rdf:about="#exceptionpettingzoo"> <owl:intersectionof rdf:parsetype="collection"> <rdf:description rdf:about="#regionrecreation"/> <owl:onproperty rdf:resource="#designation"/> <owl:somevaluesfrom rdf:resource="#pettingzoo"/> <owl:onproperty rdf:resource="#overlap"/> <owl:somevaluesfrom rdf:resource="#regionnature"/> </owl:intersectionof> <rdfs:subclassof rdf:resource="#situations"/> <rdfs:subclassof> <owl:onproperty rdf:resource="#allowed_by"/> <owl:hasvalue rdf:resource="#norm_exception_pettingzoo"/> </rdfs:subclassof> <! > <owl:class rdf:about="#holidayrecreation"> <rdfs:subclassof rdf:resource="#recreation"/> <! > <owl:class rdf:about="#marina"> <rdfs:subclassof rdf:resource="#holidayrecreation"/> <! > <owl:class rdf:about="#nature"> <rdfs:subclassof rdf:resource="#designation"/> <! > <owl:class rdf:about="#nooverlaprecreationnature"> <owl:intersectionof rdf:parsetype="collection"> <rdf:description rdf:about="#regionrecreation"/> <owl:onproperty rdf:resource="#overlap"/> <owl:somevaluesfrom rdf:resource="#regionnature"/> </owl:intersectionof> <rdfs:subclassof rdf:resource="#situations"/> <rdfs:subclassof> <owl:onproperty rdf:resource="#disallowed_by"/> <owl:hasvalue rdf:resource="#norm_no_overlap_recreationnature"/> </rdfs:subclassof> 18

20 <! > <owl:class rdf:about="#pettingzoo"> <rdfs:subclassof rdf:resource="#dailyrecreation"/> <! > <owl:class rdf:about="#recreation"> <rdfs:subclassof rdf:resource="#designation"/> <! > <owl:class rdf:about="#region"> <rdfs:subclassof rdf:resource="&owl;thing"/> <! > <owl:class rdf:about="#regionnature"> <owl:onproperty rdf:resource="#designation"/> <owl:somevaluesfrom rdf:resource="#nature"/> <rdfs:subclassof rdf:resource="#region"/> <! > <owl:class rdf:about="#regionrecreation"> <owl:onproperty rdf:resource="#designation"/> <owl:somevaluesfrom rdf:resource="#recreation"/> <rdfs:subclassof rdf:resource="#region"/> <! > <owl:class rdf:about="#situations"> <rdfs:subclassof rdf:resource="&owl;thing"/> <! > <owl:class rdf:about="#themepark"> <rdfs:subclassof rdf:resource="#dailyrecreation"/> <! > <owl:class rdf:about="&owl;thing"/> <!-- Individuals 19