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Semantic urban modelling: Knowledge representation of urban space
Abstract
The paper presents a methodology for describing in generative terms the structure of urban fabrics: the objective is to transfer conceptually the knowledge about the domain of urban space into a hierarchical and interrelated semantic structure with relevant concepts, elements and their mutual relationships, providing explicit and unambiguous definitions. The conceptual and operational instrument adopted for this purpose is the ontology, a method of knowledge representation and management coming from the Artificial Intelligence. This approach aims to create a customisable digital design tool, to support the designer in the early stages of urban design process, such as street pattern and massing definition, by generating in real time a number of design scenarios, starting from a large number of constraints and requests. This paper focuses on the knowledge formalisation aspects of the research that is the basis for the generative modelling of urban space.
... We here embrace this approach, although other semantic representations of the urban environment have been proposed (e.g. Berta et al., 2016;Dibble et al., 2019). ...
... Quantitative estimates of concepts such as visual salience and landmark importance are of growing interest (Filomena et al., 2019), but consistent and agreed definitions of how these characteristics are captured and defined are yet to be determined. In guiding these efforts, opportunities are presented in extending existing classifications of urban morphology, that may allow us to circumvent issues of inconsistent definition (Berta et al., 2016). ...
Spatial cognition is fundamental to the behaviour and activity of humans in urban space. Humans perceive their environments with systematic biases and errors, and act upon these perceptions, which in turn form urban patterns of activity. These perceptions are influenced by a multitude of factors, many of them relating to the static urban form. Yet much of geographic analysis ignores the influence of urban form, instead referring most commonly to the Euclidean arrangement of space. In this paper, we propose a novel spatial modelling framework for estimating cognitive distance in urban space. This framework is constructed from a wealth of research describing the effect of environmental factors on distance estimation, and produces a quantitative estimate of the effect based on standard GIS data. Unlike other cost measures, the cognitive distance estimate integrates systematically observed distortions and biases in spatial cognition. As a proof-of-concept, the framework is implemented for 26 cities worldwide using open data, producing a novel comparative measure of ‘cognitive accessibility’. The paper concludes with a discussion of the potential of this approach in analysing and modelling urban systems, and outlines areas for further research.
... Their ontology is build using the Protégé application and is grounded on existing upper ontological framework like SKOS, and FOAF. In (Berta et al, 2016) Berta et al. describe the Urban Morphology Ontology (UMO) for describing, in generative terms, the structure of urban fabrics. The goal is to support urban designers in the early stages of design process, like street pattern and massing definition, by generating in real time a number of design scenarios, starting from a large number of constraints and requests. ...
Over the past decades the understanding of environment has undergone profound changes. This has been accelerated by the advent of information science and big data, and the ever-increasing quantity of data produced by smart devices in urban areas and remote-sensing. This development has been accompanied by advanced information and communication technologies designed to take advantage of this data deluge. Computational ontologies have been proposed to turn this data into knowledge that can be exploited for a variety of tasks ranging from information retrieval to decision support. In this chapter, we review how recent approaches for information modelling in urban environments implement computational ontologies. This survey highlights the problems these approaches intend to solve, as well as their limitations. Based on this analysis, a roadmap of future research is drawn that needs to meet the environmental and ecological challenges raised by urbanization.
... Literature for the Synthetical MP.Métral et al.[118], Psyllidis[128] Karalis et al.[80], Yao and Kolbe[169] Komninos et al.[87], Psyllidis et al.[129], Yao et al.[170] Semantic enrichment Semantic alignment, enrichment, integration and verification of data Iwaniak et al.[76], Tardy et al.[149] Semantic feedback and communication Feedback and communication for knowledge management for planning support systems Murgante and Garramone[119], Ruikar et al.[133] Bereta et al.[17], Karalis et al.[80] Representation of urban space using semantic urban modelling Berta et al.[18], Billen[21] Semantic support applications Semantic support for Smart City and IoT applicationsBaracho et al. [9], Guo et al. [64], Katsumi and Fox [82], Komninos et al. [87], Métral et al. [117], Zhao and Wang [172] Semantic urban models Knowledge management and ontology development for semantic urban models Berdier and Roussey [16], Biljecki et al. [19], Caneparo et al. [29], Guyot et al. [65], Teller et al. [151] Semantic urban planning Semantic urban planning, land use and land cover management, planning ontologies, allocation-related master-planning actions Ahlqvist et al. [4], Gomes et al. [61], Kaczmarek et al. [78], Montenegro and Duarte [114], Ronzhin et al. [131], Soon [143] Development of Domain Ontologies Domain ontology development techniques Semantic data integration for urban analytics, coupling ontologies and urban design guidelines, capture spatio-temporal phenomena Cao and Hall [31], Corry et al. [40], Huang and Harrie[75], Psyllidis[128] Ontology-based GIS and big data analysis Chaidron et al.[33], Fischer et al.[53], Gao et al.[57] Searches and Information Queries Data city indicators and frameworks Uniting ontology-based geodata and geovisual analytics Ding et al.[45], Santos et al.[136] Knowledge access and discovery systems, spatial knowledge retrieval Lacasta et al.[92], Langenhan[94], Mai et al.[105] Daum et al.[43], Scheider et al.[137] Processes, Systems, and Agents Agent composition frameworks Agent composition frameworks for automated knowledge retrieval Zhou et al.[173] Knowledge management and planning support tools for urban typologies, genres, patterns, and urban design heuristics Gao et al.[57], Montenegro and Duarte[114], Murgante and Scorza[120], Teller et al.[151] Smart City representation in ontologies Smart City representation based in ontologies Aloufi and Alawfi[7], Komninos et al.[87], Psyllidis[128], Psyllidis et al.[129] System architecture design of ontologies Ontological design of system architecture Falquet et al.[51], Kaza and Hopkins[84] ...
Pre-print as published on https://como.ceb.cam.ac.uk/preprints/270/.
... In addition to the above, there are specific implementations of urban ontologies that are often used to describe knowledge about the city. Urban Morphology Ontology (SUMO) [12] is designed to support decision-making by urban experts. Ontology development is based on a formal representation of the main types of knowledge: taxonomy, meronomy (hyponymy), spatial relations with a formalized topology, and the shape and size of elements. ...
The article considers an approach to the design of a universal upper level conceptual model of urban data and its application for the formation of a relational database for development and landscaping projects. The conceptual model of urban data is designed to solve the problem of fragmentation and fragmentation of urban data in the context of the need for urban specialists in data prepared for specific applications. The problem of data fragmentation is caused by the presence of different data sources that produce data according to their internal standards, but there are no requirements for structuring and ordering data at the top level. The conceptual model of urban data should solve this problem. The urban data model is based on the representation of the city as a system formed by the urban planning environment, people with the characteristics of their behavior in the city, as well as providing urban infrastructures. The article shows that the proposed approach can be used as a basis for designing a database for the applied problem of modeling the loyalty of citizens.
This review focuses on recent research literature on the use of Semantic Web Technologies (SWT) in city planning. The review foregrounds representational, evaluative, projective, and synthetical meta-practices as constituent practices of city planning. We structure our review around these four meta-practices that we consider fundamental to those processes. We find that significant research exists in all four metapractices. Linking across domains by combining various methods of semantic knowledge generation, processing, and management is necessary to bridge gaps between these meta-practices and will enable future Semantic City Planning Systems.
Generative computation has the potential to enhance the accuracy, effectiveness, and creativity of spatial layout in design and planning. The paper proposes a methodology to separate the knowledge about objects, spatial relationships, and constraints from the generative process. The separation between the knowledge in a domain and its possible practical uses is an important achievement of semantic technologies, because it grants access to a large body of knowledge, spanning various aspects and processes across buildings and cities, which is being codified into formal ontologies. The present study has reused existing knowledge from two established ontologies. An illustrative case-project demonstrates the suitability of the methodology for a complex layout planning problem, involving a large number of decision-makers, with multiple competing objectives and criteria. The system implements multidimensional visual interactive tools to assist designers, planners, and decision-makers in exploring the layouts and the criteria, to develop their confidence in what qualifies as a good and effective solution.
While the urban fabric has both three and two dimensional aspects, we rarely if ever experience urban form as a fully three-dimensional object nor as a two-dimensional ground plan. Rather, the urban fabric is experienced more in terms of places with a fractal dimension lying between two and three. Hence we can (re)interpret urban form elements from colonnades to streets as ‘fractal’ types. This in turn suggests the possibility for a ‘fractal’ typo-morphology, addressing elements that occupy a typological space ‘in between’ 3D architectural modelling and 2D urban plan analysis. While Moudon could note that aspects of type were ‘vague and flawed with ambiguity’ (1994), it seems that there is still room for clarity; Dovey’s recent review of type (2016) invites further development and integration of the concept of type what may still be a disparate and ambiguous territory. Meanwhile, fractal approaches to morphology have been traditionally more concerned with larger scale urban patterns (e.g. Frankhauser, 2004) or measurement of architectural elements (e.g. Ostwald et al, 2015) with a fractal dimension less than two. This paper explores the possibilities for establishing a ‘fractal typo-morphology’ that recognises the ‘2.x’ dimensional aspect of the urban fabric and its component types. The paper generates a solution-space of types, illustrated with empirical examples, and organizes these into a typology for onward use, so that ideas of type, form and fractal dimension can contribute more fully as ‘conceptual tools’ both for understanding the urban fabric and for use as building blocks for urban design.
While the urban fabric has both three and two dimensional aspects, we rarely if ever experience urban form as a fully three-dimensional object nor as a two-dimensional ground plan. Rather, the urban fabric is experienced more in terms of places with a fractal dimension lying between two and three. Hence we can (re)interpret urban form elements from colonnades to streets as ‘fractal’ types. This in turn suggests the possibility for a ‘fractal’ typo-morphology, addressing elements that occupy a typological space ‘in between’ 3D architectural modelling and 2D urban plan analysis. While Moudon could note that aspects of type were ‘vague and flawed with ambiguity’ (1994), it seems that there is still room for clarity; Dovey’s recent review of type (2016) invites further development and integration of the concept of type what may still be a disparate and ambiguous territory. Meanwhile, fractal approaches to morphology have been traditionally more concerned with larger scale urban patterns (e.g. Frankhauser, 2004) or measurement of architectural elements (e.g. Ostwald et al, 2015) with a fractal dimension less than two. This paper explores the possibilities for establishing a ‘fractal typo-morphology’ that recognises the ‘2.x’ dimensional aspect of the urban fabric and its component types. The paper generates a solution-space of types, illustrated with empirical examples, and organizes these into a typology for onward use, so that ideas of type, form and fractal dimension can contribute more fully as ‘conceptual tools’ both for understanding the urban fabric and for use as building blocks for urban design. References (100 words) Dovey, K. (2016) Urban Design Thinking. London: Bloomsbury Academic. Moudon, A. V. (1994) Getting to know the built landscape: typomorphology, in Franck, K. A and H. Schneekloth (eds) Ordering Space: Types in Architecture and Design. New York: VNR. Frankhauser, P. (2004) Comparing the morphology of urban patterns in Europe – a fractal approach, in Borsdorf, A. and Zembri, P. (eds) European Cities – Insights on outskirts. Brussels: COST. Ostwald, M., Vaughan, J. and Tucker, C. (2015) Characteristic visual complexity: Fractal dimensions in the architecture of Frank Lloyd wright and Le Corbusier, in Williams, K. and Ostwald, M. (eds) Architecture and Mathematics from Antiquity to the Future. Switzerland: Springer.
This paper develops a model for an urban design tool based on discursive grammars. The proposed design synthesis process is consistent with a reflective design process. The formalism, called urban grammars, is formally defined and a design methodology for urban design based on urban grammars is proposed and discussed. An urban design is obtained from a full formulation-generation-evaluation cycle of design patterns called urban induction patterns, each one of them being also a limited cycle of formulation- generation-evaluation encoded as a discursive grammar replicating a generic design move. The defined structure is compatible with the principles of design machines but keeping the reflective structure of the design process.
This paper is concerned with the question of identifying and describing the general and historical character of human settlements. Beginning with a brief overview of the subject, the paper examines some basic issues concerning the perception of character and its historical dimension, highlighting a number of fundamental principles. The central argument of the paper is that the concept of urban tissue offers an effective framework for identifying and describing the physical characteristics that contribute to the general and historical character of towns. Specific, detailed procedures for identifying tissues are outlined, and an example is given. Applications in planning and urban design are also discussed: in particular, application to character assessment and conservation area definition. Further, the necessity of seeing character as only the outward aspect of a process involving humans and their environment is noted. This underlines the need to put any account of physical characteristics within the context of other aspects such as activities and intentions in order to move towards a better account of character.
Although information modeling techniques and ontologies developments
have been intensively used and studied, we believe that clear and
logical links between these different concepts are missing. It is even
more crucial in 3D city modeling context where standardization processes
are less developed. In this paper, we first give an unambiguous view of
the interrelations and dependencies between models and ontologies based
on a top-level characterization of data, knowledge and information. We
then propose an ontology of space aiming at strengthening basis of 3D
City models, with a specific application to some objects of the CityGML
3D geo-information standard.
In the last decades, the use of ontologies in information systems has be-
come more and more popular in various fields, such as web technologies,
database integration, multi agent systems, Natural Language Processing,
etc. At the beginning, Artificial Intelligent researchers borrow the word
“ontology” from Philosophy, but now this word spread in many scientific
domain and ontologies are now used in several developments. The main
goal of this chapter is to answer generic questions about ontologies, such
as: Which are the different kinds of ontologies? What is the purpose of the
use of ontologies in an application? Which methods can I use to build an
ontology?
The final publication is available at Springer via http://dx.doi.org/10.1007/978-0-85729-724-2
The urban morphological processes ontology (URMOPRO) has been developed to find an intermediate level of abstraction between
the quantitative measures and the conceptual frameworks needed to understand the observable changes in the cityscape (i.e.
morphological processes). The domain of application of this ontology is urban morphology research.
In order to create the contents of an address gazetteer service that forms part of a city council Spatial Data Infrastructure, all the exis- tent repositories containing address information in the different council offices must be analyzed and harmonized. The problem is that usually these repositories are constrained by the use of different taxonomies for the identification of urban network feature types. The objective of this work will be to describe how to establish a formal ontology enabling the interoperability among the different taxonomies, and facilitating the construction of the gazetteer contents.
Urban planning projects are complex and involve multiple actors ranging from urban planners to inhabitants. These actors differ greatly in their b ackground or their centers of interest. The main ob jective of our research is contributing to a better communication of urban planning projects between the various acto rs involved. With this intention, we defined an ontology-based s ystem whose main characteristics are, on one hand, the semantic integration in a knowledge base of the urb an knowledge from various sources such as GIS databases, master plans, local plans or any other document and , on the other hand, the modelisation of the center of interest of an urban actor.
In this paper we present Text2Onto, a framework for ontology learning from textual resources. Three main features distinguish
Text2Onto from our earlier framework TextToOnto as well as other state-of-the-art ontology learning frameworks. First, by
representing the learned knowledge at a meta-level in the form of instantiated modeling primitives within a so called Probabilistic
Ontology Model (POM), we remain independent of a concrete target language while being able to translate the instantiated primitives
into any (reasonably expressive) knowledge representation formalism. Second, user interaction is a core aspect of Text2Onto
and the fact that the system calculates a confidence for each learned object allows to design sophisticated visualizations
of the POM. Third, by incorporating strategies for data-driven change discovery, we avoid processing the whole corpus from
scratch each time it changes, only selectively updating the POM according to the corpus changes instead. Besides increasing
efficiency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying
corpus.
The paper presents an outline of a methodology for developing a socio-cultural ontology starting from Engeström's Activity Theory and the on the usage of ontologies in knowledge-based processing. A skeleton of an ontology containing the basic concepts related to the socio-cultural aspects in urban development is introduced. Implementation alternatives are discussed. © Springer-Verlag Berlin Heidelberg 2007.
A classification of built forms is presented. It is based on a study of buildings surveyed at 3350 addresses in four English towns and has been designed for use in the national Non-Domestic Building Stock (NDBS) database developed for the Department of Environment, Transport and the Regions. As the prime use of the database is in energy analysis, the classification focuses on the external envelopes of buildings. (Materials of construction, servicing systems, and activities are classified separately.) Built forms are distinguished according to two main criteria: the broad 'texture' of their internal subdivision; and whether they are daylit or artificially lit.
Presents a particular way of building ontologies that proceeds in
a bottom-up fashion. Concepts are defined in a way that mirrors the way
their instances are composed out of smaller objects. The smaller objects
themselves may also be modeled as being composed. Bottom-up ontologies
are flexible through the use of implicit and, hence, parsimonious
part-whole and subconcept-superconcept relations. The bottom-up method
complements current practice, where, as a rule, ontologies are built
top-down. The design method is illustrated by an example involving
ontologies of pure substances at several levels of detail. It is not
claimed that bottom-up construction is a generally valid recipe; indeed,
such recipes are deemed uninformative or impossible. Rather, the
approach is intended to enrich the ontology developer's toolkit
The general objective of this work is to provide domain- and resource-independent methods and tools for speeding up the ontology development process. This is achieved by reusing and re-engineering available non-ontological resources (NORs) as much as possible. To fulfil this overall goal, the author has decomposed this publication into the following methodological and technological objectives: The definition of methodological aspects related to the reuse of non-ontological resources for building ontologies The definition of methodological aspects related to the re-engineering of non-ontological resources for building ontologies The creation of a library of patterns for re-engineering non-ontological resources into ontologies The development of a software library, NOR2O, that implements the suggestions given by the re-engineering patterns. © 2012, Akademische Verlagsgesellschaft AKA GmbH, Heidelberg. All rights reserved.
Many types of part-whole relations have been proposed in the literature to aid the conceptual modeller to choose the most appropriate type, but many of those relations lack a formal specification to give clear and unambiguous semantics to them. To remedy this, a formal taxonomy of types of mereological and meronymic part-whole relations is presented that distinguishes between transitive and intransitive relations and the kind of entity types that are related. The demand to use it effectively brings afore new requirements for automated reasoning over a hierarchy of relations. To ensure logically and ontologically correct inferencing over both the class and role hierarchy, the new reasoning service RBox compatibility for Description Logics reasoners is introduced. The proposed combination of formal semantics and the new reasoning service will improve the representation of the application domain when using part-whole relations in conceptual models and ontologies.
This chapter presents methodological guidelines that allow engineers to reuse generic ontologies. This kind of ontologies represents notions generic across many fields, (is part of, temporal interval, etc.). The guidelines helps the developer (a) to identify the type of generic ontology to be reused, (b) to find out the axioms and definitions that should be reused and (c) to adapt and integrate the generic ontology selected in the domain ontology to be developed. For each task of the methodology, a set of heuristics with examples are presented. We hope that after reading this chapter, you would have acquired some basic ideas on how to take advantage of the great deal of well-founded explicit knowledge that formalizes generic notions such as time concepts and the part of relation.
The increasing pervasiveness of urban systems and networks utilizing digital technologies for their operation generates enormous amounts of digital traces capable of reflecting in real-time how people make use of space and infrastructures in the city. This is not only transforming how we study, design, and manage cities but opens up new possibilities for tools that give people access to up-to-date information about urban dynamics, allowing them to take decisions that are more in sync with their environment. This paper documents the ongoing LIVE Singapore! project which explores the development of an open platform for the collection, elaboration and distribution of a large and growing number of different kinds of real-time data that originate in a city. Inspired by recent data.gov initiatives, the platform is structured to become itself a tool for developer communities, allowing them to analyze data and write applications that create links between a city's different real-time data streams, offering new insights and services to citizens. Being a compact island based city-state metropolis, Singapore offers a unique context for this study. This paper addresses the value of stream data for city planning and management as well as modalities to give citizens meaningful access to large amounts of data capable of informing their decisions. We describe the technology context within which this project is framed, illustrate the requirements and the architecture of the open real-time data platform to serve as a base for programming the city, and finally we present and discuss the first platform prototype (using real-world data from operators of cellphone networks, taxi fleet, public transport, sea port, airport, and others). Based on this prototype a public showcasing of the project was staged in April 2011 at the Singapore Art Museum and the visual data analytics generated are illustrated in the paper. Finally, we draw some conclusions of technical as well as organizational nature regarding the challenges we faced when working in new ways with real-world, real-time data streams in an urban context that will help inform further development of our as well as of related projects in progressing in disclosing the potential of the wealth of digital data generated by urban systems, networks, and infrastructures.
To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations.We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.
Since the development of ontologies from scratch requires much time and many resources, the activity of knowledge acquisition
constitutes one of the most important steps at the beginning of the ontology development process. This activity is essential
in all the different methodologies for ontology design as a previous step to the conceptualization and formalization phases.
The Region Connection Calculus based on 8 relations (RCC8) is one of several extensively researched methods to use for qualitative spatial representation and reasoning. We discuss several issues arising when representing RCC8 in OWL DL, a decidable fragment of OWL. There is no direct encoding of such a calculus in OWL DL, as the language lacks required features such as role reflexivity, role Boolean operators, and role inclusion axioms. Some of these features are to be included in the new version of the OWL standard, OWL 2, but this language still lacks the expressive power to support role negations, conjunctions, disjunctions, and complex role inclusion axioms. Recently, advances in description logics languages as SROIQBs have made possible expressing some of the above constructs, while maintaining the decidability of the language. In this paper, we exploit these new opportunities by providing qualitative spatial knowledge representation on the Semantic Web.
Environmental databases store a wide variety of data from heterogeneous sources which are described with domain-specific terminologies and refer to distinct locations. In order to make them accessible also to non-expert users, terminological concepts and spatial relations must be represented in a way that they can be exploited for searches. In this paper we propose a hybrid knowledge representation system architecture which integrates terminological and spatial aspects of the application domain and provides support for reasoning with RCC, a well-known calculus for spatial reasoning, and the Semantic Web's ontology language OWL. Our approach is motivated by the observation that RCC cannot be expressed in OWL without a major revision of the latter. Issues of upholding consistency of the knowledge base in view of an evolving ontology and of computational complexity are discussed.
I present a method for reasoning about spatial relationships on the basis of entailments in propositional logic. Formalisms for representing topological and other spatial information (e.g. [2] [10] [11]) have generally employed the 1st-order predicate calculus. Whilst this language is much more expressive than 0-order (propositional) calculi it is correspondingly harder to reason with. Hence, by encoding spatial relationships in a propositional representation automated reasoning becomes more effective. I specify representations in both classical and intuitionistic propositional logic, which — together with well-defined meta-level reasoning algorithms — provide for efficient reasoning about a large class of spatial relations.
Reasoning about your spatial environment can be a challenging task, especially when you are a robot trying to rely solely
on quantitative measures. With nearness being such a vague concept, a qualitative representation is an obvious choice offering
a wider range of possible values.
This paper introduces a qualitative representation for spatial proximity that accounts for absolute binary nearness relations.
The formalism is based on the notion of perceived points, called sites, in a point based universe. Proximity concepts are
determined by the parameters of distance between two sites and weight of each of those sites. These parameters were drawn
from the concept of Generalised Voronoi Diagrams.
Cognitively useful models and interpretations of our formalism are shown in both a navigation and a natural language context.
Philosophers have spent 25 centuries debating ontological categories. Their insights are directly applicable to the analysis, design, and specification of the ontologies used in knowledge-based systems. This paper surveys some of the ontological questions that arise in artificial intelligence, some answers that have been proposed by various philosophers, and an application of the philosophical analysis to the clarification of some current issues in AI. Two philosophers who have developed the most complete systems of categories are Charles Sanders Peirce and Alfred North Whitehead. Their analyses suggest a basic structure of categories that can provide some guidelines for the design of AI systems.
This thesis is focused on the reuse and possible subsequent re-engineering of knowledge resources, as opposed to custom-building new ontologies from scratch. The deep analysis of the state of the art has revealed that there are some methods and tools in the literature for transforming non-ontological resources into ontologies, but with some limitations: _ Most of the methods presented are based on ad-hoc transformations for the resource type, and the resource implementation. _ Only a few take advantage of the resource data model, an important artifact for the re-engineering process [GGPSFVT08]. _ There is no any integrated framework, method or corresponding tool, that considers the resources types, data models and implementations identified in an unified way. _ With regard to the transformation approach, the majority of the methods perform a TBox transformation, many others perform an ABox transformation and some perform a population. However, no method includes the possibility to perform the three transformation approaches. _ Regarding to the degree of automation, almost all the methods perform a semi-automatic transformation of the resource. _ According to the explicitation of the hidden semantics in the relations of the resource components, we can state that the methods that perform a TBox transformation make explicit the semantics in the relations of the resource components. Most of those methods identify subClassOf relations, others identify ad-hoc relations, and some identify partOf relations. However, only a few methods make explicit the three types of relations. _ With respect to how the methods make explicit the hidden semantics in the relations of the resource terms, we can say that three methods rely on the domain expert for making explicit the semantics, and two rely on an external resource, e.g., DOLCE ontology. Moreover, there are two methods that rely on external resources but not for making explicit the hidden semantics, but for finding out a proper ontology for populating it. _ According to the provision of the methodological guidelines, almost all the methods provide methodological guidelines for the transformation. However these guidelines are not finely detailed; for instance, they do not provide information about who is in charge of performing a particular activity/task, nor when that activity/task has to be carried out. _ With regard to the techniques employed, most of the methods do not mention them at all. Only a few methods specify techniques as transformation rules, lexico-syntactic patterns, mapping rules and natural language techniques. In this thesis we have provided a method and its technological support that rely on re-engineering patterns in order to speed up the ontology development process by reusing and re-engineering as much as possible available non-ontological resources. To achieve this overall goal, we have decomposed it in the following objectives: (1) the definition of methodological aspects related with the reuse of non-ontolo-gical resource for building ontologies; (2) the definition of methodological aspects related with the re-engineering of non-ontological resources for building ontologies; (3) the creation of a library of patterns for re-engineering nonontological resources into ontologies; and (4) the development of a software library that implements the suggestions given by the re-engineering patterns. Having in mind these goals, in this chapter we present how the open research problems identified in Chapter 2 are solved by the main thesis contributions. Then, we discuss the verification of our hypotheses, and finally we provide an outlook for the future work in those topics.
The depiction of urban morphological processes needs the construction of a specific ontology based on a double temporal approach in which synchronic and diachronic relationships must cohabitate. The aim of this paper is to discuss continuity matters of this kind of ontologies and, especially, the effective capability of concepts to evolve as our perception of the complexity of processes grows.
The objective of this paper is to study the relations between connectivity and adjacency measures, developed in previous papers, as functions of the urban spatial structure, by means of multivariate statistical techniques at two levels of resolution and spatial aggregation for the kilometre square cells used to record the town of Reading, England. It is shown that of the measures studied one should be discarded as it has no functional relation with the urban spatial structure and the others are structurally interdependent with it. It is concluded that, after having validated these measures with the urban spatial structure, they can be used as indices of built-form development.
This paper, the first of a sequence, defines built forms as quasi-mathematical models and uses graph-theoretic representation in order to express how buildings are connected and packed over an area of land. Buildings are represented by points called built forms; external walls and partitions (party walls between buildings) by lines. The connected subgraphs made up of built forms and partitions are called arrays of built forms. This constitutes a simplified view of the built-form subsystem which, together with the channel network, gives rise to an urban graph. Twelve measures representing either the connectivity amongst the elements of the built-form subsystem or of the adjacency between built forms and the external environment are defined in order to provide a numerical scale for the properties under study. The majority of these are ratio measures which will be evaluated in a subsequent paper by use of actual data.
During the last six years the authors have been working in the Centre for Configurational Studies on a series of research projects whose overall aim is to increase knowledge of the composition and morphology of the English building stock. Earlier work was devoted to domestic buildings -- houses and apartments -- whereas more recent research is concerned with nondomestic building types, focusing particularly on offices and shops. Two large surveys are described of domestic and nondomestic buildings made in Cambridge and Swindon, respectively. This empirical work has been accompanied by the development of a theoretical morphology or science of architectural form which attempts to explain why certain plans and built forms occur in practice and not others. It is only on the basis of such a theory, the authors argue, that any scientific generalisations can be built about the relationships of form to performance. A central feature of the approach to morphological classification taken here is the separation of shape or configurational properties from dimensional or metric properties.
This paper is intended to serve as a comprehensive introduction to the emerging field concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to effective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing definitions. We then consider the benefits of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the specification, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging field, considering various case studies, software tools for ontology development, key reearch issues and future prospects.
Spatial reasoning is essential for many AI applications. In most existing systems the representation is primarily numerical, so the information that can be handled is limited to precise quantitative data. However, for many purposes the ability to manipulate high-level qualitative spatial information in a flexible way would be extremely useful. Such capabilities can be proveded by logical calculi; and indeed 1st-order theories of certain spatial relations have been given [20]. But computing inferences in 1st-order logic is generally intractable unless special (domain dependent) methods are known.
0-order modal logics provide an alternative representation which is more expressive than classical 0-order logic and yet often more amenable to automated deduction than 1st-order formalisms. These calculi are usually interpreted as propositional logics: non-logical constants are taken as denoting propositions. However, they can also be given a nominal interpretation in which the constants stand for some kind of object. I show how 0-order logics can be given a spatial interpretation: constants denote regions and logical operators correspond to operations on regions which are important for characterising spatial situations.
Representing certain spatial concepts requires the introduction of modal operators, interpreted as functions generating regions related in specific ways to those denoted by their arguments. A significant example is the convex-hull operator whose value is the smallest convex region containing its argument. I investigate how this this operator can be captured in a multi-medal logic.