Heshan Du

Matching Formal and Informal Geospatial Ontologies

The rapid development of crowd-sourcing or volunteered geographic information both challenges and provides opportunities to authoritative geospatial information. Matching geospatial ontologies is an essential element to realizing the synergistic use of disparate geospatial information. We propose a new semi-automatic method to match formal and informal real life geospatial ontologies, at both terminology level and instance level, ensuring that overall information is logically coherent and consistent. Disparate geospatial ontologies are matched by finding a consistent and coherent set of mapping axioms with respect to them. Disjointness axioms are generated in order to facilitate detection of errors. In contrast to other existing methods, disjointness axioms are seen as assumptions, which can be retracted during the overall process. We produce candidates for retraction automatically, but the ultimate decision is taken by domain experts. Geometry matching, lexical matching and cardinality checking are combined when matching geospatial individuals (spatial features).

The Logic of NEAR and FAR

We propose a new qualitative spatial logic based on metric (distance) relations between spatial objects. We provide a sound and complete axiomatisation of the logic with respect to metric models. The logic is intended for use in checking consistency of matching geospatial individuals from different data sets, where some data sets may be imprecise (e.g. crowd-sourced data).

A Method for Matching Crowd-sourced and Authoritative Geospatial Data

A software tool MatchMaps was designed to facilitate matching between geospatial features held in different datasets, especially an authoritative dataset and a crowd-sourced dataset. MatchMaps is not fully automatic; it requires human interaction to resolve problematic matching cases. Previous studies have shown that this approach results in higher precision and recall than those achieved by fully automatic tools. In this study, we aim to evaluate MatchMaps with respect to the amount of human effort required for matching, and compare it with a fully manual matching process.

An Ontology of Soil Properties and Processes

Assessing the Underworld (ATU) is a large interdisciplinary UK research project, which addresses challenges in integrated inter-asset maintenance. As assets on the surface of the ground (e.g. roads or pavements) and those buried under it (e.g. pipes and cables) are supported by the ground, the properties and processes of soil affect the performance of these assets to a significant degree. In order to make integrated decisions, it is necessary to combine the knowledge and expertise in multiple areas, such as roads, soil, buried assets, sensing, etc. This requires an underpinning knowledge model, in the form of an ontology. Within this context, we present a new ontology for describing soil properties (e.g. soil strength) and processes (e.g. soil compaction), as well as how they affect each other. This ontology can be used to express how the ground affects and is affected by assets buried under the ground or on the ground surface. The ontology is written in OWL 2 and openly available from the University of Leeds data repository: http://doi.org/10.5518/54.

A tool for matching crowd-sourced and authoritative geospatial data

A software tool MatchMaps was designed to facilitate matching between geospatial features held in different datasets, especially an authoritative dataset and a crowd-sourced dataset. MatchMaps is not fully automatic; it requires human interaction to resolve problematic matching cases. Previous studies have shown that this approach results in higher precision and recall than those achieved by fully automatic tools. In this study, we aim to evaluate MatchMaps with respect to the amount of human effort required for matching, and compare it with a fully manual matching process.

A logic of part and whole for buffered geometries

We propose a new qualitative spatial logic for reasoning about part-whole relations between geometries (sets of points) represented in different geospatial datasets, in particular crowd-sourced datasets. Since geometries in crowd-sourced data can be less inaccurate or precise, we buffer geometries by a margin of error or level of tolerance σ, and define part-whole relation for buffered geometries. The relations between geometries considered in the logic are: buffered part of (BPT), Near and Far. We provide a sound and complete axiomatisation of the logic with respect to metric models, and show that its satisfiability problem is NP-complete.

Automated Reasoning for City Infrastructure Maintenance Decision Support

We present an interactive decision support system for assisting city infrastructure inter-asset management. It combines real-time site specific data retrieval, a knowledge base co-created with domain experts and an inference engine capable of predicting potential consequences and risks resulting from the available data and knowledge. The system can give explanations of each consequence, cope with incomplete and uncertain data by making assumptions about what might be the worst case scenario, and making suggestions for further investigation. This demo presents multiple real-world scenarios, and demonstrates how modifying assumptions (parameter values) can lead to different consequences.

ATU-DSS: Knowledge-Driven Data Integration and Reasoning for Sustainable Subsurface Inter-Asset Management

Urban infrastructure assets perform critical functions to the health and well-being of the society. In this paper, we present a prototype decision support system for sustainable subsurface inter-asset management. To the best of the authors’ knowledge, this work is the first on assessing the underground space by considering the inter-asset dependencies using semantic technologies. Based on a family of interlinked city infrastructure asset ontologies describing the ground, roads and buried utilities (e.g. water pipes), various datasets are integrated and logical rules are developed to describe the intra-asset and inter-asset relationships. An inference engine is employed to exploit the knowledge and data for assessing the potential impact of an event. This system can be beneficial to a wide range of stakeholders (e.g. utility incident managers) for quickly gathering of the localised contextual data and identifying potential consequences from what may appear as an insignificant trigger. A video demonstrating the prototype is available at: http://bit.ly/2mdyIY4.