Glen Hart

Rabbit: developing a control natural language for authoring ontologies

The mathematical nature of description logics has meant that domain experts find them hard to understand. This forms a significant impediment to the creation and adoption of ontologies. This paper describes Rabbit, a Controlled Natural Language that can be translated into OWL with the aim of achieving both comprehension by domain experts and computational preciseness. We see Rabbit as complementary to OWL, extending its reach to those who need to author and understand domain ontologies but for whom descriptions logics are difficult to comprehend even when expressed in more user-friendly forms such as the Manchester Syntax. The paper outlines the main grammatical aspects of Rabbit, which can be broadly classified into declarations, concept descriptions and definitions, and elements to support interoperability between ontologies. The paper also describes the human subject testing that has been performed to date and indicates the changes currently being made to the language following this testing. Further modifications have been based on practical experience of the application of Rabbit for the development of operational ontologies in the domain of topography.

Geospatial semantics and linked spatiotemporal data --Past, present, and future

The Geosciences and Geography are not just yet another application area for semantic technologies. The vast heterogeneity of the involved disciplines ranging from the natural sciences to the social sciences introduces new challenges in terms of interoperability. Moreover, the inherent spatial and temporal information components also require distinct semantic approaches. For these reasons, geospatial semantics, geo-ontologies, and semantic interoperability have been active research areas over the last 20 years. The geospatial semantics community has been among the early adopters of the Semantic Web, contributing methods, ontologies, use cases, and datasets. Today, geographic information is a crucial part of many central hubs on the Linked Data Web. In this editorial, we outline the research field of geospatial semantics, highlight major research directions and trends, and glance at future challenges. We hope that this text will be valuable for geoscientists interested in semantics research as well as knowledge engineers interested in spatiotemporal data.

Linked Data : A Geographic Perspective

A Gentle Beginning What This Book Is About and Who It Is For Geography and the Semantic Web GI in the Semantic Web Examples Conventions Used in the Book Structure of the Book A Last Thought about How to Read This Book Linked Data and the Semantic Web Introduction From a Web of Documents to a Web of Knowledge Early History and the Development of the Semantic Web Semantic Web Benefits How It Works Recent Trends in the Field Summing Up and Signposts to the Next Chapter Notes Geographic Information Introduction What Is Geographic Information? The Many Forms of GI Representations and Uses of GI A Brief History of Geographic Information Summary Notes Geographic Information in an Open World Introduction Principles Applying the Semantic Web to GI Important Observations Summary Notes The Resource Description Framework Introduction RDF: The Purpose A Word about Identity The RDF Data Model RDF Serialization RDFS Popular RDFS Vocabularies RDF for the Thinking Geographer Summary Notes Organizing GI as Linked Data Introduction Identity: Designing and Applying Universal Resource Identifiers Identity: Names Geometry Classification Topology and Mereology Summary Notes Publishing Linked Data Introduction Linked Data Principles Making URIs Dereferenceable or Slash versus Hash Linked Data Design Linked Data Generation Describing the Linked Dataset Provenance Authentication and Trust Licensing Linked Data Software Tools Testing and Debugging Linked Data Summary Notes Using Linked Data Introduction Business Models for Linked Data SPARQL Linking to External Datasets: Types of Link Link Design Process Link Discovery and Creation Encoding Context: An Atheists View of Web Identity Link Maintenance Evaluating Link Quality and Avoiding Semantic Spam Summary Notes OWL Introduction The Nature of OWL OWL Language Elements Properties Tools for Authoring Summary Notes Building Geographic Ontologies Introduction Types of Ontology Methodologies Building the Topographic Ontology of Merea Maps Ontology Reuse: Aiding Third-Party Data Integration Summary Notes Linking It All Together Introduction The Wide Scope of Geographic Information An Open World The Simplicity and Complexity of the Semantic Web The Technologies Benefits and Business Models Future Directions Concluding Thoughts Note References Appendix A: OWL Species Appendix B: OWL Constructs: Manchester Syntax and Rabbit Index

Supporting domain experts to construct conceptual ontologies: A holistic approach

Abstract A recent trend in ontology engineering research aims at encouraging the active participation of domain experts in the ontology creation process. Ontology construction methodologies together with appropriate tools and technologies, such as controlled natural languages, semantic wikis, intelligent user interfaces and social computing, are being proposed to enable the direct input from domain experts and to minimize the dependency on knowledge engineers at every step of ontology development. The time is ripe for consolidating methodological and technological advancements to create intuitive ontology engineering tools which can make Semantic Web technologies usable by a wide variety of people without formal knowledge engineering skills. A novel, holistic approach to facilitate the involvements of domain experts in the ontology authoring process is presented here. It integrates (i) an ontology construction methodology, (ii) the use of a controlled natural language, and (iii) appropriate tool support. The integrated approach is illustrated with the design, implementation and evaluation of ROO – a unique ontology authoring tool which combines intelligent techniques to assist domain experts in constructing ontologies. The benefits and limitations of the proposed approach are analyzed based on user studies with ROO. A broader discussion is provided pointing at issues to be taken into account when assisting the involvement of domain experts in ontology construction.

Rabbit to OWL: ontology authoring with a CNL-based tool

Recent work on ontology engineering has seen the adoption of controlled natural languages to ease the process of ontology authoring. However, CNL-based tools still require good knowledge engineering skills to be used efficiently. In this paper presents ROO, an ontology authoring tool that has been designed to cater for the needs of domain experts with little or no ontology engineering experience. ROO combines a CNL-based interface with appropriate tool support based on an ontology construction methodology. We focus on how this tool support is provided in ROO by using and implementing novel aspects of the Rabbit controlled natural language and we refer to an evaluation study that provides empirical evidence in support of using CNL-based techniques to assist ontology authors.

Talking rabbit: a user evaluation of sentence production

Rabbit is a controlled natural language (CNL) designed to aid experts in understanding and authoring domain ontologies. There are three broad types of Rabbit sentence: declarations, axioms and import statements. This paper evaluates the ease with which domain experts without any prior ontology development experience can author declarations and axiom sentences in Rabbit. Participants were asked to author Rabbit sentences about an artificial domain (Planet Zog). The most common error observed was the omission of the every keyword at the beginning of sentences. Another common error was to confuse instance and subclass declarations. Suggested improvements include changes to the Rabbit syntax as well modifications to a CNL authoring environment.

MapSnapper: Engineering an Efficient Algorithm for Matching Images of Maps from Mobile Phones

The MapSnapper project aimed to develop a system for robust matching of low-quality images of a paper map taken from a mobile phone against a high quality digital raster representation of the same map. The paper presents a novel methodology for performing content-based image retrieval and object recognition from query images that have been degraded by noise and subjected to transformations through the imaging system. In addition the paper also provides an insight into the evaluation-driven development process that was used to incrementally improve the matching performance until the design specifications were met.

Ontology ontogeny: understanding how an ontology is created and developed

This paper describes the development of a systematic method for creating domain ontologies. We have chosen to explicitly recognise the differing needs of the human domain expert and the machine in our representation of ontologies in two forms: a conceptual and a logical ontology. The conceptual ontology is intended for human understanding and the logical ontology, expressed in description logics, is derived from the conceptual ontology and intended for machine processing. The main contribution of our work is the division of these two stages of ontology development, with emphasis placed on domain experts themselves creating the conceptual ontology, rather than relying on a software engineer to elicit knowledge about the domain. In particular, this paper concentrates on the creation of conceptual ontologies and analyses the success of our methodology when tested by domain experts.

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 Scale of VGI in Map Production: A Perspective on European National Mapping Agencies

The perspective of European National Mapping Agencies (NMA) on the role of citizen sensing in map production was explored. The NMAs varied greatly in their engagement with the community generating volunteered geographic information (VGI) and in their future plans. From an assessment of NMA standard practices, it was evident that much VGI was acquired with a positional accuracy that, while less than that typically acquired by NMAs, actually exceeded the requirements of the nominal data capture scale used by most NMAs. Opportunities for VGI use in map revision and updating were evident, especially for agencies that use a continuous rather than cyclical updating policy. Some NMAs had also developed systems to engage with citizen sensors and examples are discussed. Only rarely was VGI used to collect data on features beyond the standard set used by the NMAs. The potential role of citizen sensing and so its current scale of use by NMAs is limited by a series of concerns, notably relating to issues of data quality, the nature and motivation of the contributors, legal issues, the sustainability of data source, and employment fears of NMA staff. Possible priorities for future research and development are identified to help ensure that the potential of VGI in mapping is realized.