Discrete Mereotopology

PublishedWhereas mereology, in the strict sense, is concerned solely with the part–whole relation, mereotopology extends mereology by including also the notion of connection, enabling one to distinguish, for example, between internal and peripheral parts, and between contact and separation. Mereotopology has been developed particularly within the Qualitative Spatial Reasoning research community, where it has been applied to, amongst other areas, geographical information science and image analysis. Most research in mereotopology has assumed that the entities being studied may be subdivided without limit, but a number of researchers have investigated mereotopological structures based on discrete spaces in which entities are built up from atomic elements that are not themselves subdivisible. This chapter presents an introductory treatment of mereotopology and its discrete variant, and provides references for readers interested in pursuing this subject in further detail

Improving Model Finding for Integrated Quantitative-qualitative Spatial Reasoning With First-order Logic Ontologies

Many spatial standards are developed to harmonize the semantics and specifications of GIS data and for sophisticated reasoning. All these standards include some types of simple and complex geometric features, and some of them incorporate simple mereotopological relations. But the relations as used in these standards, only allow the extraction of qualitative information from geometric data and lack formal semantics that link geometric representations with mereotopological or other qualitative relations. This impedes integrated reasoning over qualitative data obtained from geometric sources and “native” topological information – for example as provided from textual sources where precise locations or spatial extents are unknown or unknowable. To address this issue, the first contribution in this dissertation is a first-order logical ontology that treats geometric features (e.g. polylines, polygons) and relations between them as specializations of more general types of features (e.g. any kind of 2D or 1D features) and mereotopological relations between them. Key to this endeavor is the use of a multidimensional theory of space wherein, unlike traditional logical theories of mereotopology (like RCC), spatial entities of different dimensions can co-exist and be related. However terminating or tractable reasoning with such an expressive ontology and potentially large amounts of data is a challenging AI problem. Model finding tools used to verify FOL ontologies with data usually employ a SAT solver to determine the satisfiability of the propositional instantiations (SAT problems) of the ontology. These solvers often experience scalability issues with increasing number of objects and size and complexity of the ontology, limiting its use to ontologies with small signatures and building small models with less than 20 objects. To investigate how an ontology influences the size of its SAT translation and consequently the model finder’s performance, we develop a formalization of FOL ontologies with data. We theoretically identify parameters of an ontology that significantly contribute to the dramatic growth in size of the SAT problem. The search space of the SAT problem is exponential in the signature of the ontology (the number of predicates in the axiomatization and any additional predicates from skolemization) and the number of distinct objects in the model. Axiomatizations that contain many definitions lead to large number of SAT propositional clauses. This is from the conversion of biconditionals to clausal form. We therefore postulate that optional definitions are ideal sentences that can be eliminated from an ontology to boost model finder’s performance. We then formalize optional definition elimination (ODE) as an FOL ontology preprocessing step and test the simplification on a set of spatial benchmark problems to generate smaller SAT problems (with fewer clauses and variables) without changing the satisfiability and semantic meaning of the problem. We experimentally demonstrate that the reduction in SAT problem size also leads to improved model finding with state-of-the-art model finders, with speedups of 10-99%. Altogether, this dissertation improves spatial reasoning capabilities using FOL ontologies – in terms of a formal framework for integrated qualitative-geometric reasoning, and specific ontology preprocessing steps that can be built into automated reasoners to achieve better speedups in model finding times, and scalability with moderately-sized datasets

INTERMEDIATE LOGICS AND POLYHEDRA

Polyhedra enjoy a peculiar property: every geometric shape with a certain \u201cregularity\u201d \u2013 in specific terms, certain classes of (closed) topological manifolds \u2013 can be captured by a polyhedron via triangulation, that is, by subdividing the geometric shapes into appropriate \u201ctriangles\u201d, called simplices (which, in the 1- and 0-dimensional case, are simply edges and vertices, respectively). Therefore, one might well wonder: what is the intermediate logic of the class of triangulable topological manifolds of a given dimension d? The main result of the present work is to give the answer to this question in the case of 1-dimensional manifolds, that is, the circle and the closed interval