AI for Development & Innovation Research Publications

2019

van der Merwe A, Gerber A, Smuts H. Guidelines for Conducting Design Science Research in Information Systems. In: SACLA. Springer; 2019. doi:10.1007/978-3-030-35629-3_11.

Information Systems (IS) as a discipline is still young and is continuously involved in building its own research knowledge base. Design Science Research (DSR) in IS is a research strategy for design that has emerged in the last 16 years. IS researchers are often lost when they start with a project in DSR, especially young researchers. We identified a need for a set of guidelines with supporting reference literature that can assist such novice adopters of DSR. We identified major themes relevant to DSR and proposed a set of six guidelines for the novice researcher supported with references summaries of seminal works from the IS DSR literature. We believe that someone new to the field can use these guidelines to prepare him/herself to embark on a DSR study.

@{261,
  author = {Alta van der Merwe and Aurona Gerber and Hanlie Smuts},
  title = {Guidelines for Conducting Design Science Research in Information Systems},
  abstract = {Information Systems (IS) as a discipline is still young and is continuously involved in building its own research knowledge base. Design Science Research (DSR) in IS is a research strategy for design that has emerged in the last 16 years. IS researchers are often lost when they start with a project in DSR, especially young researchers. We identified a need for a set of guidelines with supporting reference literature that can assist such novice adopters of DSR. We identified major themes relevant to DSR and proposed a set of six guidelines for the novice researcher supported with references summaries of seminal works from the IS DSR literature. We believe that someone new to the field can use these guidelines to prepare him/herself to embark on a DSR study.},
  year = {2019},
  journal = {SACLA},
  month = {15/07 - 17/07},
  publisher = {Springer},
  isbn = {978-3-030-35628-6},
  doi = {10.1007/978-3-030-35629-3_11},
}
Gerber A, Matthee M. Design Thinking for Pre-empting Digital Disruption. In: Conference on e-Business, e-Services and e-Society. Springer; 2019. doi:https://doi.org/10.1007/978-3-030-29374-1_62.

Digital disruption is the phenomenon when established businesses succumb to new business models that exploit emerging technologies. Futurists often make dire predictions when discussing the impact of digital disruption, for instance that 40% of the Fortune 500 companies will disappear within the next decade. The digital disruption phenomenon was already studied two decades ago when Clayton Christensen developed a Theory of Disruptive Innovation, which is a popular theory for describing and explaining disruption due to technology developments that had occurred in the past. However it is still problematic to understand what is necessary to avoid disruption, especially within the context of a sustainable society in the 21st century. A key aspect we identified is the behavior of non-mainstream customers of an emerging technology, which is difficult to predict, especially when an organization is operating in an existing solution space. In this position paper we propose complementing the Theory of Disruptive Innovation with design thinking in order to identify the performance attributes that encourage the unpredictable and unforeseen customer behavior that is a cause for disruption. We employ case-based scenario analysis of higher education as evaluation mechanism for our extended disruptive innovation theory. Our position is that a better understanding of the implicit and unpredictable customer behavior that cause disruption due to additional performance attributes (using design thinking) could assist organizations to pre-empt digital disruption and adapt to support the additional functionality.

@{259,
  author = {Aurona Gerber and Machdel Matthee},
  title = {Design Thinking for Pre-empting Digital Disruption},
  abstract = {Digital disruption is the phenomenon when established businesses succumb to new business models that exploit emerging technologies. Futurists often make dire predictions when discussing the impact of digital disruption, for instance that 40% of the Fortune 500 companies will disappear within the next decade. The digital disruption phenomenon was already studied two decades ago when Clayton Christensen developed a Theory of Disruptive Innovation, which is a popular theory for describing and explaining disruption due to technology developments that had occurred in the past. However it is still problematic to understand what is necessary to avoid disruption, especially within the context of a sustainable society in the 21st century. A key aspect we identified is the behavior of non-mainstream customers of an emerging technology, which is difficult to predict, especially when an organization is operating in an existing solution space. In this position paper we propose complementing the Theory of Disruptive Innovation with design thinking in order to identify the performance attributes that encourage the unpredictable and unforeseen customer behavior that is a cause for disruption. We employ case-based scenario analysis of higher education as evaluation mechanism for our extended disruptive innovation theory. Our position is that a better understanding of the implicit and unpredictable customer behavior that cause disruption due to additional performance attributes (using design thinking) could assist organizations to pre-empt digital disruption and adapt to support the additional functionality.},
  year = {2019},
  journal = {Conference on e-Business, e-Services and e-Society},
  pages = {759 - 770},
  month = {18/09 - 20/09},
  publisher = {Springer},
  isbn = {978-3-030-29373-4},
  doi = {https://doi.org/10.1007/978-3-030-29374-1_62},
}
Eybers S, Gerber A, Bork D, Karagiannis D. Matching Technology with Enterprise Architecture and Enterprise Architecture Management Tasks Using Task Technology Fit. In: Lecture Notes in Business Information Processing. Springer; 2019. doi:10.1007/978-3-030-20618-5_17.

Advanced modeling is a challenging endeavor and good tool support is of paramount importance to ensure that the modeling objectives are met through the efficient execution of tasks. Tools for advanced modeling should not just support basic task modeling functionality such as easy-to-use interfaces for model creation, but also advanced task functionality such as consistency checks and analysis queries. Enterprise Architecture (EA) is concerned with the alignment of all aspects of an organization. Modeling plays a crucial role in EA and the matching of the correct tool to enable task execution is vital for enterprises engaged with EA. Enterprise Architecture Management (EAM) reflects recent trends that elevate EA toward a strategic management function within organizations. Tool support for EAM would necessarily include the execution of additional and often implicit advanced modeling tasks that support EAM capabilities. In this paper we report on a study that used the Task-Technology Fit (TTF) theory to investigate the extent to which basic and advanced task execution for EAM is supported by technology. We found that four of the six TTF factors fully supported and one partially supported EAM task execution. One factor was inconclusive. This study provided a insight into investigating tool support for EAM related task execution to achieve strategic EAM goals.

@inbook{258,
  author = {Sunet Eybers and Aurona Gerber and Dominik Bork and Dimitris Karagiannis},
  title = {Matching Technology with Enterprise Architecture and Enterprise Architecture Management Tasks Using Task Technology Fit},
  abstract = {Advanced modeling is a challenging endeavor and good tool support is of paramount importance to ensure that the modeling objectives are met through the efficient execution of tasks. Tools for advanced modeling should not just support basic task modeling functionality such as easy-to-use interfaces for model creation, but also advanced task functionality such as consistency checks and analysis queries. Enterprise Architecture (EA) is concerned with the alignment of all aspects of an organization. Modeling plays a crucial role in EA and the matching of the correct tool to enable task execution is vital for enterprises engaged with EA. Enterprise Architecture Management (EAM) reflects recent trends that elevate EA toward a strategic management function within organizations. Tool support for EAM would necessarily include the execution of additional and often implicit advanced modeling tasks that support EAM capabilities. In this paper we report on a study that used the Task-Technology Fit (TTF) theory to investigate the extent to which basic and advanced task execution for EAM is supported by technology. We found that four of the six TTF factors fully supported and one partially supported EAM task execution. One factor was inconclusive. This study provided a insight into investigating tool support for EAM related task execution to achieve strategic EAM goals.},
  year = {2019},
  journal = {Lecture Notes in Business Information Processing},
  pages = {245 - 260},
  publisher = {Springer},
  isbn = {978-3-030-20617-8},
  doi = {10.1007/978-3-030-20618-5_17},
}
Thomas A, Gerber A, van der Merwe A. A Conceptual Framework of Research on Visual Language Specification Languages. In: International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). Winterton, South Africa: IEEE; 2019. doi:10.1109/ICABCD.2019.8851003.

Visual languages make use of spatial arrangements of graphical and textual elements to represent information. Domain specific diagrams, including flowcharts and music sheets, are examples of visual languages. An established area of research is the study of languages which can be used to create declarative specifications of visual languages. In this paper, the result of a review of research on visual language specification languages is presented. Specifically, a structured literature review is conducted to establish research themes by analysing what has been studied in the context of specification languages. The result of the literature review is used to develop a conceptual framework that consists of six research themes with related topics. Additionally, discussions on how the conceptual framework can be used as a basis to guide research in the field of specification languages, to perform feature based characterisations and to create lists of criteria to evaluate and compare specification languages are included in this paper.

@{255,
  author = {Anitta Thomas and Aurona Gerber and Alta van der Merwe},
  title = {A Conceptual Framework of Research on Visual Language Specification Languages},
  abstract = {Visual languages make use of spatial arrangements of graphical and textual elements to represent information. Domain specific diagrams, including flowcharts and music sheets, are examples of visual languages. An established area of research is the study of languages which can be used to create declarative specifications of visual languages. In this paper, the result of a review of research on visual language specification languages is presented. Specifically, a structured literature review is conducted to establish research themes by analysing what has been studied in the context of specification languages. The result of the literature review is used to develop a conceptual framework that consists of six research themes with related topics. Additionally, discussions on how the conceptual framework can be used as a basis to guide research in the field of specification languages, to perform feature based characterisations and to create lists of criteria to evaluate and compare specification languages are included in this paper.},
  year = {2019},
  journal = {International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD)},
  month = {05/09 - 06/09},
  publisher = {IEEE},
  address = {Winterton, South Africa},
  isbn = {978-1-5386-9236-3},
  url = {https://ieeexplore.ieee.org/document/8851003},
  doi = {10.1109/ICABCD.2019.8851003},
}

2018

van der Merwe A, Gerber A. Guidelines for using Bloom’s Taxonomy Table as Alignment Tool between Goals and Assessment. In: SACLA. Springer; 2018.

In academia lecturers are often appointed based on their research profile and not their teaching and learning (T&L) experience. Although universities do emphasize T&L, it might often not even be mentioned during interviews. In the field of education lecturers are more aware of using tools such as Bloom’s Taxonomy during their T&L activities. However, in the field of information systems limited academic papers are available on how lecturers can align their goals with the assessment in their courses. In this paper Bloom’s Taxonomy Table was used to evaluate the alignment of goals of the case and the assessment done on a fourth-year level subject offered in the information systems field. The purpose of the paper was firstly to reflect on the practice of using Bloom’s Taxonomy Table as an evaluation tool and then secondly to provide a set of guidelines for lecturers who want to use Bloom’s Taxonomy Table in alignment studies.

@{260,
  author = {Alta van der Merwe and Aurona Gerber},
  title = {Guidelines for using Bloom’s Taxonomy Table as Alignment Tool between Goals and Assessment},
  abstract = {In academia lecturers are often appointed based on their research profile and not their teaching and learning (T&L) experience. Although universities do emphasize T&L, it might often not even be mentioned during interviews. In the field of education lecturers are more aware of using tools such as Bloom’s Taxonomy during their T&L activities. However, in the field of information systems limited academic papers are available on how lecturers can align their goals with the assessment in their courses. In this paper Bloom’s Taxonomy Table was used to evaluate the alignment of goals of the case and the assessment done on a fourth-year level subject offered in the information systems field. The purpose of the paper was firstly to reflect on the practice of using Bloom’s Taxonomy Table as an evaluation tool and then secondly to provide a set of guidelines for lecturers who want to use Bloom’s Taxonomy Table in alignment studies.},
  year = {2018},
  journal = {SACLA},
  pages = {278 - 290},
  month = {18/06 - 20/06},
  publisher = {Springer},
  isbn = {978-0-720-80192-8},
}
Thomas A, Gerber A, van der Merwe A. Ontology-Based Spatial Pattern Recognition in Diagrams. In: Artificial Intelligence Applications and Innovations. Springer; 2018. https://www.springer.com/us/book/9783319920061.

Diagrams are widely used in our day to day communication. A knowledge of the spatial patterns used in diagrams is essential to read and understand them. In the context of diagrams, spatial patterns mean accepted spatial arrangements of graphical and textual elements used to represent diagram-specific concepts. In order to assist with the automated understanding of diagrams by computer applications, this paper presents an ontology-based approach to recognise diagram-specific concepts from the spatial patterns in diagrams. Specifically, relevant spatial patterns of diagrams are encoded in an ontology, and the automated instance classification feature of the ontology reasoners is utilised to map spatial patterns to diagram-specific concepts depicted in a diagram. A prototype of this approach to support automated recognition of UML and domain concepts from class diagrams and its performance are also discussed in this paper. This paper concludes with a reflection of the strengths and limitations of the proposed approach.

@{257,
  author = {Anitta Thomas and Aurona Gerber and Alta van der Merwe},
  title = {Ontology-Based Spatial Pattern Recognition in Diagrams},
  abstract = {Diagrams are widely used in our day to day communication. A knowledge of the spatial patterns used in diagrams is essential to read and understand them. In the context of diagrams, spatial patterns mean accepted spatial arrangements of graphical and textual elements used to represent diagram-specific concepts. In order to assist with the automated understanding of diagrams by computer applications, this paper presents an ontology-based approach to recognise diagram-specific concepts from the spatial patterns in diagrams. Specifically, relevant spatial patterns of diagrams are encoded in an ontology, and the automated instance classification feature of the ontology reasoners is utilised to map spatial patterns to diagram-specific concepts depicted in a diagram. A prototype of this approach to support automated recognition of UML and domain concepts from class diagrams and its performance are also discussed in this paper. This paper concludes with a reflection of the strengths and limitations of the proposed approach.},
  year = {2018},
  journal = {Artificial Intelligence Applications and Innovations},
  pages = {61 -72},
  month = {25/05 - 27/05},
  publisher = {Springer},
  isbn = {978-3-319-92007-8},
  url = {https://www.springer.com/us/book/9783319920061},
}
Gerber A. Computational Ontologies as Classification Artifacts in IS Research. In: AMCIS. ; 2018. https://aisel.aisnet.org/amcis2018/Semantics/Presentations/5/.

Based on previous work on classification in IS research, this paper reports on an experimental investigation into the use of computational ontologies as classification artifacts, given the classification approaches identified in information systems (IS) research. The set-theoretical basis of computational ontologies ensures particular suitability for classification functions, and classification was identified as an accepted approach to develop contributions to IS research. The main contribution of the paper is a set of guidelines that IS researchers could use when adopting a classification approach and constructing an ontology as the resulting classification artifact. The guidelines were extracted by mapping an ontology construction approach to the classification approaches of IS research. Ontology construction approaches have been developed in response to the significant adoption of computational ontologies in the broad field of computing and IS since the acceptance of the W3C standards for ontology languages. These W3C standards also resulted in the development of tools such as ontology editors and reasoners. The advantages of using computational ontologies as classification artifacts thus include standardized representation, as well as the availability of associated technologies such as reasoners that could, for instance, ensure that implicit assumptions are made explicit and that the ontology is consistent and satisfiable. The research results from this experimental investigation extend the current work on classification in IS research.

@{256,
  author = {Aurona Gerber},
  title = {Computational Ontologies as Classification Artifacts in IS Research},
  abstract = {Based on previous work on classification in IS research, this paper reports on an experimental investigation into the use of computational ontologies as classification artifacts, given the classification approaches identified in information systems (IS) research. The set-theoretical basis of computational ontologies ensures particular suitability for classification functions, and classification was identified as an accepted approach to develop contributions to IS research. The main contribution of the paper is a set of guidelines that IS researchers could use when adopting a classification approach and constructing an ontology as the resulting classification artifact. The guidelines were extracted by mapping an ontology construction approach to the classification approaches of IS research. Ontology construction approaches have been developed in response to the significant adoption of computational ontologies in the broad field of computing and IS since the acceptance of the W3C standards for ontology languages. These W3C standards also resulted in the development of tools such as ontology editors and reasoners. The advantages of using computational ontologies as classification artifacts thus include standardized representation, as well as the availability of associated technologies such as reasoners that could, for instance, ensure that implicit assumptions are made explicit and that the ontology is consistent and satisfiable. The research results from this experimental investigation extend the current work on classification in IS research.},
  year = {2018},
  journal = {AMCIS},
  month = {16/09 - 18/09},
  isbn = {978-0-9966831-6-6},
  url = {https://aisel.aisnet.org/amcis2018/Semantics/Presentations/5/},
}
Harmse H, Britz K, Gerber A. Informative Armstrong RDF datasets for n-ary relations. In: Formal Ontology in Information Systems: 10th International Conference, Cape Town, South Africa. IOS Press; 2018.

The W3C standardized Semantic Web languages enable users to capture data without a schema in a manner which is intuitive to them. The challenge is that for the data to be useful, it should be possible to query the data and to query it efficiently, which necessitates a schema. Understanding the structure of data is thus important to both users and storage implementers: the structure of the data gives insight to users in how to query the data while storage implementers can use the structure to optimize queries. In this paper we propose that data mining routines can be used to infer candidate n-ary relations with related uniqueness- and null-free constraints, which can be used to construct an informative Armstrong RDF dataset. The benefit of an informative Armstrong RDF dataset is that it provides example data based on the original data which is a fraction of the size of the original data, while capturing the constraints of the original data faithfully. A case study on a DBPedia person dataset showed that the associated informative Armstrong RDF dataset contained 0.00003% of the statements of the original DBPedia dataset.

@{188,
  author = {Henriette Harmse and Katarina Britz and Aurona Gerber},
  title = {Informative Armstrong RDF datasets for n-ary relations},
  abstract = {The W3C standardized Semantic Web languages enable users to capture data without a schema in a manner which is intuitive to them. The challenge is that for the data to be useful, it should be possible to query the data and to query it efficiently, which necessitates a schema. Understanding the structure of data is thus important to both users and storage implementers: the structure of the data gives insight to users in how to query the data while storage implementers can use the structure to optimize queries. In this paper we propose that data mining routines can be used to infer candidate n-ary relations with related uniqueness- and null-free constraints, which can be used to construct an informative Armstrong RDF dataset. The benefit of an informative Armstrong RDF dataset is that it provides example data based on the original data which is a fraction of the size of the original data, while capturing the constraints of the original data faithfully. A case study on a DBPedia person dataset showed that the associated informative Armstrong RDF dataset contained 0.00003% of the statements of the original DBPedia dataset.},
  year = {2018},
  journal = {Formal Ontology in   Information Systems: 10th International Conference, Cape Town, South Africa},
  pages = {187-198},
  month = {17/09-21/09},
  publisher = {IOS Press},
}

2017

Gerber A, Morar N, Meyer T, Eardley C. Ontology-based support for taxonomic functions. Ecological Informatics. 2017;41. https://ac.els-cdn.com/S1574954116301959/1-s2.0-S1574954116301959-main.pdf?_tid=487687ca-01b3-11e8-89aa-00000aacb35e&acdnat=1516873196_6a2c94e428089403763ccec46613cf0f.

This paper reports on an investigation into the use of ontology technologies to support taxonomic functions. Support for taxonomy is imperative given several recent discussions and publications that voiced concern over the taxonomic impediment within the broader context of the life sciences. Taxonomy is defined as the scientific classification, description and grouping of biological organisms into hierarchies based on sets of shared characteristics, and documenting the principles that enforce such classification. Under taxonomic functions we identified two broad categories: the classification functions concerned with identification and naming of organisms, and secondly classification functions concerned with categorization and revision (i.e. grouping and describing, or revisiting existing groups and descriptions). Ontology technologies within the broad field of artificial intelligence include computational ontologies that are knowledge representation mechanisms using standardized representations that are based on description logics (DLs). This logic base of computational ontologies provides for the computerized capturing and manipulation of knowledge. Furthermore, the set-theoretical basis of computational ontologies ensures particular suitability towards classification, which is considered as a core function of systematics or taxonomy. Using the specific case of Afrotropical bees, this experimental research study represents the taxonomic knowledge base as an ontology, explore the use of available reasoning algorithms to draw the necessary inferences that support taxonomic functions (identification and revision) over the ontology and implement a Web-based application (the WOC). The contributions include the ontology, a reusable and standardized computable knowledge base of the taxonomy of Afrotropical bees, as well as the WOC and the evaluation thereof by experts.

@article{163,
  author = {Aurona Gerber and Nishal Morar and Thomas Meyer and C. Eardley},
  title = {Ontology-based support for taxonomic functions},
  abstract = {This paper reports on an investigation into the use of ontology technologies to support taxonomic functions. Support for taxonomy is imperative given several recent discussions and publications that voiced concern over the taxonomic impediment within the broader context of the life sciences. Taxonomy is defined as the scientific classification, description and grouping of biological organisms into hierarchies based on sets of shared characteristics, and documenting the principles that enforce such classification. Under taxonomic functions we identified two broad categories: the classification functions concerned with identification and naming of organisms, and secondly classification functions concerned with categorization and revision (i.e. grouping and describing, or revisiting existing groups and descriptions).
Ontology technologies within the broad field of artificial intelligence include computational ontologies that are knowledge representation mechanisms using standardized representations that are based on description logics (DLs). This logic base of computational ontologies provides for the computerized capturing and manipulation of knowledge. Furthermore, the set-theoretical basis of computational ontologies ensures particular suitability towards classification, which is considered as a core function of systematics or taxonomy.
Using the specific case of Afrotropical bees, this experimental research study represents the taxonomic knowledge base as an ontology, explore the use of available reasoning algorithms to draw the necessary inferences that support taxonomic functions (identification and revision) over the ontology and implement a Web-based application (the WOC). The contributions include the ontology, a reusable and standardized computable knowledge base of the taxonomy of Afrotropical bees, as well as the WOC and the evaluation thereof by experts.},
  year = {2017},
  journal = {Ecological Informatics},
  volume = {41},
  pages = {11-23},
  publisher = {Elsevier},
  isbn = {1574-9541},
  url = {https://ac.els-cdn.com/S1574954116301959/1-s2.0-S1574954116301959-main.pdf?_tid=487687ca-01b3-11e8-89aa-00000aacb35e&acdnat=1516873196_6a2c94e428089403763ccec46613cf0f},
}
Coetzer W, Moodley D. A knowledge-based system for generating interaction networks from ecological data. Data & Knowledge Engineering. 2017;112. doi:http://dx.doi.org/10.1016/j.datak.2017.09.005.

Semantic heterogeneity hampers efforts to find, integrate, analyse and interpret ecological data. An application case-study is described, in which the objective was to automate the integration and interpretation of heterogeneous, flower-visiting ecological data. A prototype knowledgebased system is described and evaluated. The system's semantic architecture uses a combination of ontologies and a Bayesian network to represent and reason with qualitative, uncertain ecological data and knowledge. This allows the high-level context and causal knowledge of behavioural interactions between individual plants and insects, and consequent ecological interactions between plant and insect populations, to be discovered. The system automatically assembles ecological interactions into a semantically consistent interaction network (a new design of a useful, traditional domain model). We discuss the contribution of probabilistic reasoning to knowledge discovery, the limitations of knowledge discovery in the application case-study, the impact of the work and the potential to apply the system design to the study of ecological interaction networks in general.

@article{154,
  author = {Willem Coetzer and Deshen Moodley},
  title = {A knowledge-based system for generating interaction networks from ecological data},
  abstract = {Semantic heterogeneity hampers efforts to find, integrate, analyse and interpret ecological data. An application case-study is described, in which the objective was to automate the integration and interpretation of heterogeneous, flower-visiting ecological data. A prototype knowledgebased system is described and evaluated. The system's semantic architecture uses a combination of ontologies and a Bayesian network to represent and reason with qualitative, uncertain ecological data and knowledge. This allows the high-level context and causal knowledge of behavioural interactions between individual plants and insects, and consequent ecological interactions between plant and insect populations, to be discovered. The system automatically assembles ecological interactions into a semantically consistent interaction network (a new design of a useful, traditional domain model). We discuss the contribution of probabilistic reasoning to knowledge discovery, the limitations of knowledge discovery in the application case-study, the impact of the work and the potential to apply the system design to the study of ecological interaction networks in general.},
  year = {2017},
  journal = {Data & Knowledge Engineering},
  volume = {112},
  pages = {55-78},
  publisher = {Elsevier},
  isbn = {0169-023X},
  url = {http://pubs.cs.uct.ac.za/archive/00001220/01/coetzer-et-al-DKE-2017.pdf},
  doi = {http://dx.doi.org/10.1016/j.datak.2017.09.005},
}

2016

Coetzer W, Moodley D, Gerber A. Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data. PLoS ONE . 2016;11(11). http://pubs.cs.uct.ac.za/archive/00001127/01/journal.pone.0166559.pdf.

Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioral ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics.

@article{159,
  author = {Willem Coetzer and Deshen Moodley and Aurona Gerber},
  title = {Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data},
  abstract = {Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioral ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics.},
  year = {2016},
  journal = {PLoS ONE},
  volume = {11},
  pages = {1-15},
  issue = {11},
  url = {http://pubs.cs.uct.ac.za/archive/00001127/01/journal.pone.0166559.pdf},
}
Lapalme J, Gerber A, van der Merwe A, Zachman J, de Vries M, Hinkelmann K. Exploring the future of enterprise architecture: A Zachman perspective. 2016. http://www.sciencedirect.com/science/journal/01663615/79.

No Abstract

@misc{150,
  author = {James Lapalme and Aurona Gerber and Alta van der Merwe and John Zachman and Marne de Vries and Knut Hinkelmann},
  title = {Exploring the future of enterprise architecture: A Zachman perspective.},
  abstract = {No Abstract},
  year = {2016},
  url = {http://www.sciencedirect.com/science/journal/01663615/79},
}
Harmse H, Britz K, Gerber A. Armstrong Relations for Ontology Design and Evaluation. 2016. http://ceur-ws.org/Vol-1577/.

No Abstract

@misc{149,
  author = {Henriette Harmse and Katarina Britz and Aurona Gerber},
  title = {Armstrong Relations for Ontology Design and Evaluation},
  abstract = {No Abstract},
  year = {2016},
  url = {http://ceur-ws.org/Vol-1577/},
}
Hinkelmann K, Gerber A, Karagiannis D, Thoenssen B, van der Merwe A, Woitsch R. A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology. Computers in Industry. 2016;79. http://www.sciencedirect.com/science/article/pii/S0166361515300270.

No Abstract

@article{148,
  author = {Knut Hinkelmann and Aurona Gerber and Dimitris Karagiannis and Barbara Thoenssen and Alta van der Merwe and Robert Woitsch},
  title = {A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology},
  abstract = {No Abstract},
  year = {2016},
  journal = {Computers in Industry},
  volume = {79},
  publisher = {Sciencedirect},
  url = {http://www.sciencedirect.com/science/article/pii/S0166361515300270},
}

2015

de Vries M, Gerber A, van der Merwe A. The enterprise engineering domain. In: Advances in Enterprise Engineering IX. Springer; 2015. http://link.springer.com/chapter/10.1007%2F978-3-319-19297-0_4.

No Abstract

@inbook{124,
  author = {Marne de Vries and Aurona Gerber and Alta van der Merwe},
  title = {The enterprise engineering domain},
  abstract = {No Abstract},
  year = {2015},
  journal = {Advances in Enterprise Engineering IX},
  publisher = {Springer},
  isbn = {978-3-319-19296-3},
  url = {http://link.springer.com/chapter/10.1007%2F978-3-319-19297-0_4},
}
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