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A Knowledge-Based System for Automated Discovery of Ecological Interactions in Flower-Visiting Data

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A Knowledge-Based System for Automated Discovery of Ecological Interactions in Flower-Visiting Data A Knowledge-Based System for Automated Discovery of Ecological Interactions in Flower-Visiting Data by Willem Coetzer Submitted in fulfilment of the academic requirements for the degree of Doctor of Philosophy in the School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban February 2017 As the candidate’s supervisor I have approved this thesis for submission. Signed: **’ * Name: Deshen Moodley Date: 16-02-2017 Signed: Name: Aurona Gerber Date: 16-02-2017 Abstract Studies on the community ecology of flower-visiting insects, which can be inferred to pollinate flowers, are important in agriculture and nature conservation. Many scientific observations of flower-visiting insects are associated with digitized records of insect specimens preserved in natural history collections. Specimen annotations include heterogeneous and incomplete, in situ field documentation of ecologically significant relationships between individual organisms (i.e. insects and plants), which are nevertheless potentially valuable. A wealth of unrepresented biodiversity and ecological knowledge can be unlocked from such detailed data by augmenting the data with expert knowledge encoded in knowledge models. An analysis of the knowledge representation requirements of flower-visiting community ecologists is presented, as well as an implementation and evaluation of a prototype knowledge-based system for automated semantic enrichment, semantic mediation and interpretation of flower-visiting data. A novel component of the system is a semantic architecture which incorporates knowledge models validated by experts. The system combines ontologies and a Bayesian network to enrich, integrate and interpret flower- visiting data, specifically to discover ecological interactions in the data. The system’s effectiveness, to acquire and represent expert knowledge and simulate the inferencing ability of expert flower-visiting ecologists, is evaluated and discussed. The knowledge-based system will allow a novice ecologist to use standardised semantics to construct interaction networks automatically and objectively. This could be useful, inter alia, when comparing interaction networks for different periods of time at the same place or different places at the same time. While the system architecture encompasses three levels of biological organization, data provenance can be traced back to occurrences of individual organisms preserved as evidence in natural history collections. The potential impact of the semantic architecture could be significant in the field of biodiversity and ecosystem informatics because ecological interactions are important in applied ecological studies, e.g. in freshwater biomonitoring or animal migration. II Preface The work described in this thesis was carried out in the School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, from February 2012 to January 2017, under the supervision of Associate Professors Deshendran Moodley and Aurona Gerber. These studies represent original work by the author and have not otherwise been submitted in any form for any degree or diploma to any tertiary institution. Where use has been made of the work of others it is duly acknowledged in the text. iii Declaration 1 - Plagiarism I, Willem Coetzer, declare that: 1. The research reported in this thesis, except where otherwise indicated, is my original research; 2. This thesis has not been submitted for any degree or examination at any other university; 3. This thesis does not contain other persons’ data, pictures, graphs or other information, unless specifically acknowledged as being sourced from other persons; 4. This thesis does not contain other persons' writing, unless specifically acknowledged as being sourced from other researchers. Where other written sources have been quoted, then: a. their words have been re-written but the general information attributed to them has been referenced; b. where their exact words have been used, then their writing has been placed in italics and inside quotation marks, and referenced; 5. This thesis does not contain text, graphics or tables copied and pasted from the Internet, unless specifically acknowledged, and the source being detailed in the thesis and in the References sections. Signed: IV Declaration 2 - Publications Article 1 (published) Coetzer, W., Moodley, D., Gerber, A. 2013. A case-study of ontology-driven semantic mediation of flower-visiting data from heterogeneous data-stores in three South African natural history collections. In The Semantic Web: ESWC 2013 Satellite Events. (P. Cimiano; M. Fernandez; V. Lopez; S. Schlobach; J. Voelker; Eds.) Lecture Notes in Computer Science. No. 7955: 87-100. • Selected as one of the best workshop papers submitted to ESWC 2013 W. Coetzer: Executed the work and wrote the article D. Moodley: Supervised the work and contributed to article writing A. Gerber: Supervised the work and contributed to article writing Article 2 (published) Coetzer, W., Moodley, D. & Gerber, A. 2014. A knowledge-based system for discovering ecological interactions in biodiversity data-stores of heterogeneous specimen-records: A case-study of flower-visiting ecology. Ecological Informatics 24: 47-59. W. Coetzer: Executed the work and wrote the article D. Moodley: Supervised the work and contributed to article writing A. Gerber: Supervised the work and contributed to article writing Article 3 (published) Coetzer, W., Moodley, D. & Gerber, A. 2016. Eliciting and representing high-level knowledge requirements to discover ecological knowledge in flower- visiting data. P LoS O ne 11: e0166559. W. Coetzer: Executed the work and wrote the article D. Moodley: Supervised the work and contributed to article writing A. Gerber: Supervised the work and contributed to article writing Article 4 (submitted on 26/01/2017 for publication in Data and Knowledge Engineering) A knowledge-based system for generating interaction networks from ecological data. W. Coetzer: Executed the work and wrote the article D. Moodley: Supervised the work and contributed to article writing A. Gerber: Supervised the work and contributed to article writing Signed: (a ). v Acknowledgements I am indebted to Prof. Paul Skelton, who first suggested that I should pursue a PhD degree, and supported my work and encouraged me. I am grateful to Dr Angus Paterson for his continued support. I would like to thank my supervisors, Prof. Deshendran Moodley and Prof. Aurona Gerber, for their guidance and encouragement. The assistance of Dr S.K. Gess, Dr C. Eardley, Dr C. Mayer, Dr G. Ballantyne, Dr G. Benadi and Dr J. Colville is gratefully acknowledged, as is that of my wife, Bridgit, for her proof-reading, which is second to none. VI Contents Chapter 1: 1.1 Introduction 1 1.2 Research Gap 3 1.3 Research Objectives 4 1.4 Research Method 4 1.5 Thesis Outline 5 Chapter 2: Article 1: A case-study of ontology-driven semantic 10 mediation of flower-visiting data from heterogeneous data-stores in three South African natural history collections Chapter 3: Article 2: A knowledge-based system for discovering 25 ecological interactions in biodiversity data-stores of heterogeneous specimen-records: A case-study of flower-visiting ecology Chapter 4: Article 3: Eliciting and representing high-level 38 knowledge requirements to discover ecological knowledge in flower-visiting data Chapter 5: Article 4: A knowledge-based system for generating 53 interaction networks from ecological data Chapter 6: 6.1 Contributions 98 6.2 Knowledge Models 100 6.3 Discussion 100 6.4 Conclusion 110 1 CHAPTER 1 1.1 Introduction Epistemic uncertainty pervades the design of scientific experiments and ecological field surveys, and linguistic uncertainty clouds the analysis and interpretation of data. Both kinds of uncertainty tend to compound the variability inherent in natural phenomena, the real target of investigations [1]. Uncertainty also appears as the inability easily to integrate one source of data with another due to the different ways in which scientists have defined and named their concepts and corresponding data fields, a phenomenon known as semantic heterogeneity. In biodiversity and ecosystem informatics the problem of semantic heterogeneity has given rise to metadata-naming conventions or standards (e.g. the Darwin Core Standard) that prescribe consistent concept definitions and terminology, which significantly ease the burden of vertical data integration and data discovery, and facilitate further data analysis and interpretation. Metadata-naming conventions, however, are only the first step in addressing semantic heterogeneity. Knowledge-based systems (KBS) have the potential automatically to integrate data at a higher level of abstraction than the use of metadata-naming conventions associated with vertical data integration. A KBS can also automate data interpretation. The traditions of natural history museums date back almost 300 years, and digitised records from different museums are broadly consistent in that they are always observations of specimens that have been accumulated over time, usually for the purpose of taxonomy (e.g. finding new species) and classification (arranging species into higher groups). Digitised records sometimes include notes describing insects’ associations with, or behaviour on, host-plants. Whereas this associated information is typically incomplete from the perspective of ecology, it is nevertheless valuable and much work has been done to standardise biodiversity information from museums
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