Automatic grounding of vague geographic ontology in data

In constructing an ontological theory of a domain such as geography, it is important not only to take account of the vagueness and ambiguity which is inherent in many of the relevant concepts, but also to be able to relate the high-level definitions of the theory to actual sets of data of varying kinds. Any attempt to ignore or remove vagueness and ambiguity risks errors and conflict in the ontological theory with the knowledge of different domain experts, while an inability to ground the theory in real data limits its practical use. We present here a means of structuring such a theory to handle these issues in a principled manner, which lends itself to concrete implementation. We illustrate with reference to several examples from the domain of hydrography

An Ontology for Grounding Vague Geographic Terms

Many geographic terms, such as “river” and “lake”, are vague, with no clear boundaries of application. In particular, the spatial extent of such features is often vaguely carved out of a continuously varying observable domain. We present a means of defining vague terms using standpoint semantics, a refinement of the philosophical idea of supervaluation semantics. Such definitions can be grounded in actual data by geometric analysis and segmentation of the data set. The issues raised by this process with regard to the nature of boundaries and domains of logical quantification are discussed. We describe a prototype implementation of a system capable of segmenting attributed polygon data into geographically significant regions and evaluating queries involving vague geographic feature terms

A unified theory of granularity, vagueness and approximation

Abstract: We propose a view of vagueness as a semantic property of names and predicates. All entities are crisp, on this semantic view, but there are, for each vague name, multiple portions of reality that are equally good candidates for being its referent, and, for each vague predicate, multiple classes of objects that are equally good candidates for being its extension. We provide a new formulation of these ideas in terms of a theory of granular partitions. We show that this theory provides a general framework within which we can understand the relation between vague terms and concepts and the corresponding crisp portions of reality. We also sketch how it might be possible to formulate within this framework a theory of vagueness which dispenses with the notion of truth-value gaps and other artifacts of more familiar approaches. Central to our approach is the idea that judgments about reality involve in every case (1) a separation of reality into foreground and background of attention and (2) the feature of granularity. On this basis we attempt to show that even vague judgments made in naturally occurring contexts are not marked by truth-value indeterminacy. We distinguish, in addition to crisp granular partitions, also vague partitions, and reference partitions, and we explain the role of the latter in the context of judgments that involve vagueness. We conclude by showing how reference partitions provide an effective means by which judging subjects are able to temper the vagueness of their judgments by means of approximations

Assimilating knowledge from neuroimages in schizophrenia diagnostics

The aim of this article is to propose an integrated framework for classifying and describing patterns of disorders from medical images using a combination of image registration, linear discriminant analysis and region-based ontologies. In a first stage of this endeavour we are going to study and evaluate multivariate statistical methodologies to identify the most discriminating hyperplane separating two populations contained in the input data. This step has, as its major goal, the analysis of all the data simultaneously rather than feature by feature. The second stage of this work includes the development of an ontology whose aim is the assimilation and exploration of the knowledge contained in the results of the previous statistical methods. Automated knowledge discovery from images is the key motivation for the methods to be investigated in this research. We argue that such investigation provides a suitable framework for characterising the high complexity of MR images in schizophrenia

Semantic categories underlying the meaning of ‘place’

This paper analyses the semantics of natural language expressions that are associated with the intuitive notion of ‘place’. We note that the nature of such terms is highly contested, and suggest that this arises from two main considerations: 1) there are a number of logically distinct categories of place expression, which are not always clearly distinguished in discourse about ‘place’; 2) the many non-substantive place count nouns (such as ‘place’, ‘region’, ‘area’, etc.) employed in natural language are highly ambiguous. With respect to consideration 1), we propose that place-related expressions should be classified into the following distinct logical types: a) ‘place-like’ count nouns (further subdivided into abstract, spatial and substantive varieties), b) proper names of ‘place-like’ objects, c) locative property phrases, and d) definite descriptions of ‘place-like’ objects. We outline possible formal representations for each of these. To address consideration 2), we examine meanings, connotations and ambiguities of the English vocabulary of abstract and generic place count nouns, and identify underlying elements of meaning, which explain both similarities and differences in the sense and usage of the various terms

Standpoint Logic: A Logic for Handling Semantic Variability, with Applications to Forestry Information

It is widely accepted that most natural language expressions do not have precise universally agreed definitions that fix their meanings. Except in the case of certain technical terminology, humans use terms in a variety of ways that are adapted to different contexts and perspectives. Hence, even when conversation participants share the same vocabulary and agree on fundamental taxonomic relationships (such as subsumption and mutual exclusivity), their view on the specific meaning of terms may differ significantly. Moreover, even individuals themselves may not hold permanent points of view, but rather adopt different semantics depending on the particular features of the situation and what they wish to communicate. In this thesis, we analyse logical and representational aspects of the semantic variability of natural language terms. In particular, we aim to provide a formal language adequate for reasoning in settings where different agents may adopt particular standpoints or perspectives, thereby narrowing the semantic variability of the vague language predicates in different ways. For that purpose, we present standpoint logic, a framework for interpreting languages in the presence of semantic variability. We build on supervaluationist accounts of vagueness, which explain linguistic indeterminacy in terms of a collection of possible interpretations of the terms of the language (precisifications). This is extended by adding the notion of standpoint, which intuitively corresponds to a particular point of view on how to interpret vague terminology, and may be taken by a person or institution in a relevant context. A standpoint is modelled by sets of precisifications compatible with that point of view and does not need to be fully precise. In this way, standpoint logic allows one to articulate finely grained and structured stipulations of the varieties of interpretation that can be given to a vague concept or a set of related concepts and also provides means to express relationships between different systems of interpretation. After the specification of precisifications and standpoints and the consideration of the relevant notions of truth and validity, a multi-modal logic language for describing standpoints is presented. The language includes a modal operator for each standpoint, such that \standb{s}\phi means that a proposition $\phi$ is unequivocally true according to the standpoint $s$ --- i.e.\ $\phi$ is true at all precisifications compatible with $s$. We provide the logic with a Kripke semantics and examine the characteristics of its intended models. Furthermore, we prove the soundness, completeness and decidability of standpoint logic with an underlying propositional language, and show that the satisfiability problem is NP-complete. We subsequently illustrate how this language can be used to represent logical properties and connections between alternative partial models of a domain and different accounts of the semantics of terms. As proof of concept, we explore the application of our formal framework to the domain of forestry, and in particular, we focus on the semantic variability of `forest'. In this scenario, the problematic arising of the assignation of different meanings has been repeatedly reported in the literature, and it is especially relevant in the context of the unprecedented scale of publicly available geographic data, where information and databases, even when ostensibly linked to ontologies, may present substantial semantic variation, which obstructs interoperability and confounds knowledge exchange

Approaching the notion of place by contrast

Place is an elusive notion in geographic information science. This paper presents an approach to capture the notion of place by contrast. This approach is developed from cognitive concepts and the language that is used to describe places. It is complementary to those of coordinate-based systems that dominate contemporary geographic information systems. Accordingly the approach is aimed at explaining structures in verbal place descriptions and at localizing objects without committing to geometrically specified positions in space. We will demonstrate how locations can be identified by place names that are not crisply defined in terms of geometric regions. Capturing the human cognitive notion of place is considered crucial for smooth communication between human users and computer-based geographic assistance systems

Approaching the notion of place by contrast

Abstract: Place is an elusive notion in geographic information science. This paper presents an approach to capture the notion of place by contrast. This approach is developed from cognitive concepts and the language that is used to describe places. It is complementary to those of coordinate-based systems that dominate contemporary geographic information systems. Accordingly, the approach is aimed at explaining structures in verbal place descriptions and at localizing objects without committing to geometrically specified positions in space. We will demonstrate how locations can be identified by place names that are not crisply defined in terms of geometric regions. Capturing the human cognitive notion of place is considered crucial for smooth communication between human users and computer-based geographic assistance systems

An ontological analysis of vague motion verbs, with an application to event recognition

This research presents a methodology for the ontological formalisation of vague spatial concepts from natural language, with an application to the automatic recognition of event occurrences on video data. The main issue faced when defining concepts sourced from language is vagueness, related to the presence of ambiguities and borderline cases even in simple concepts such as ‘near’, ‘fast’, ‘big’, etc. Other issues specific to this semantic domain are saliency, granularity and uncertainty. In this work, the issue of vagueness in formal semantics is discussed and a methodology based on supervaluation semantics is proposed. This constitutes the basis for the formalisation of an ontology of vague spatial concepts based on classical logic, Event Calculus and supervaluation semantics. This ontology is structured in layers where high-level concepts, corresponding to complex actions and events, are inferred through mid-level concepts, corresponding to simple processes and properties of objects, and low-level primitive concepts, representing the most essential spatio-temporal characteristics of the real world. The development of ProVision, an event recognition system based on a logic-programming implementation of the ontology, demonstrates a practical application of the methodology. ProVision grounds the ontology on data representing the content of simple video scenes, leading to the inference of event occurrences and other high-level concepts. The contribution of this research is a methodology for the semantic characterisation of vague and qualitative concepts. This methodology addresses the issue of vagueness in ontologies and demonstrates the applicability of a supervaluationist approach to the formalisation of vague concepts. It is also proven to be effective towards solving a practical reasoning task, such as the event recognition on which this work focuses

Ontologies and representation spaces for sketch map interpretation

In this paper, we present a systematic approach to sketch map interpretation. The method decomposes the elements of a sketch map into a hierarchy of categories, from the material sketch map level to the non-material representational sketch map level, and then interprets the sketch map using the five formal representation spaces that we develop. These spaces (set, graph, metric and Euclidean) provide a tiered formal representation based on standard mathematical structures. We take the view that a sketch map bears information about the physical world and systematises this using extensions of existing formal ontologies. The motivation for this work is the partially automatic extraction and integration of information from sketch maps. We propose a set of ontologies and methods as a first step in the direction of a formalisation of partially automatic extraction and integration of sketch map content. We also see this work as a contribution to spatial cognition, where researchers externalise spatial knowledge using sketch mapping. The paper concludes by working through an example that demonstrates the sketch map interpretation at different levels using the underlying method