An exploration into cognitive bias in ontologies

Ontologies and similar artefacts are used in a myriad of ontology-driven information systems and increasingly also linked to data analytics. Algorithmic bias in data analytics is a well-known notion, but what does bias mean in the context of ontologies that provide a structuring mechanism for, e.g., an algorithm’s or query’s input? What are the sources of bias there, and cognitive bias in particular, and howdo they manifest in ontologies? We examined and enumerated eight broad sources that can cause bias that may affect an ontology’s content. They are illustrated with examples from extant ontologies and samples from the literature. We then assessed three concurrently developed COVID-19 ontologies on modelling bias and detected different subsets of types of bias in each one, to a greater or lesser extent.This first characterisation aims contribute to a sensitisation of bias in ontologies primarily regarding representation of the knowledge

Natural Language Generation Requirements for Social Robots in Sub-Saharan Africa

Robots are deployed in Africa mainly in manufacturing, yet they may assist in society as future oriented technologies as well. They may ameliorate, e.g., service delivery issues and skills shortages. In this discussion paper, several uses and use cases relevant to Sub-Saharan Africa are described and requirements identified. We zoom in on human-robot interaction in Niger-Congo B (‘bantu’) languages. Use cases for healthcare and education elucidate specific requirements for the natural language generation component of robots in society. In contrast to typical generation systems, it demands i) combining data-to-text and knowledge-to-text in one system, ii) generating different types of sentences so as to switch between written and spoken language, and iii) processing non-trivial numbers

Towards a Framework for Meaning Negotiation and Conflict Resolution in Ontology Authoring

Ontology authoring involves making choices about what subject domain knowledge to include. This may concern sorting out ontological differences as well as making choices of conflicting axioms due to limitations in the logic. Examples are different foundational ontologies in ontology matching and OWL 2 DL’s transitive object property versus qualified cardinality constraints. Such conflicts have to be resolved. However, there is currently only isolated and fragmented guidance for doing so, which therefore results in ad hoc decision-making. This work aims to ad-dress this by working towards a framework dealing with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak

ToCT: A task ontology to manage complex templates

Natural language interfaces are a well-known approach to grant non-experts access to semantic web technologies. A number of such systems use simple templates to achieve that for English and more elab-orate solutions for other languages. They keep being designed from scratch in an ad hoc manner, since there is no shared conceptualisation of simple templates and there is no model that is formalised using a Semantic Web language to apply the techniques to itself. We aim to address this by proposing a general-purpose solution in the form of a novel model for templates, formalised as a task ontology in OWL,calledToCT. We used it to develop an ontology-driven text generator for isiZulu, a morphologically-rich language, to test its capabilities. The generator verbalises the TBox of an ontology as validationq uestions. This evaluation showed that the task ontology is sufficiently expressive for the template design, which was subsequently verified with user evaluations who judged the texts positivel

Assessing and Enhancing Bottom-up CNL Design for Competency Questions for Ontologies

Competency questions (CQs) are used in ontology development to demarcate the scope, provide insights into their content,and verification. Their use has been impeded by problems with authoring good CQs. This may be assisted by a con-trolled natural language (CNL), but its development is time-consuming when carried out manually. A recent study on data-driven CNL design to learn templates from a set of CQs, resulting in CLaRO,had somewhat better coverage and some noise due to grammar errors in the source CQs. In this paper, we aim to investigate such a bottom-up approach to CNL development for CQs regarding the effects of 1) improving the quality of the source data 2) whether more CQs from other do-mains induce more templates and 3) if the structure of knowledge in subject do-mains has a role to play in the matching of patterns to templates; therewith might indicate that possibly a structure of knowledge in a subject domain may continue to affect bottom-up CNL creation.The CQ cleaning increased the number of templates from 93 to 120 main templates and an additional 12 variants. The new CQ dataset of 92 CQs generated 27new templates and 7 more variants. Thus,increasing the domain coverage had the most effect on the CNL. The CLaRO v2with all generated templates has 147 templates and 59 variants thereof and showed94.1% coverage

Generating Answerable Questions from Ontologies for Educational Exercises

Proposals for automating the creation of teaching materials across the sciences and humanities include question generation from ontologies. Those efforts have focused on multiple-choice questions, whereas learners also need to be exposed to other types of questions, such as yes/no and short answer questions. Initial results showed it is possible to create ontology-based questions. It is unknown how that can be done automatically and whether it would work beyond that use case in biology. We investigated this for ten types of educationally useful questions with additional sentence formulation variants. Each type of questions has a set of template specifications, axiom prerequisites on the ontology, and an algorithm to generate the questions from the ontology. Three approaches were designed: template variables using foundational ontology categories, using main classes from the domain ontology, and sentences mostly driven by natural language generation techniques. The user evaluation showed that the second approach resulted in slightly better quality questions than the first, and the linguistic-driven templates far outperformed both on syntactic and semantic adequacy of the questions

An evaluation of template and ML-based generation of user-readable text from a knowledge graph

Typical user-friendly renderings of knowledge graphs are visualisations and natural language text. Within the latter HCI solution approach, data-driven natural language generation systems receive increased attention, but they are often outperformed by template-based systems due to su ering from errors such as content dropping, hallucination, or repetition. It is unknown which of those errors are associated signi cantly with low quality judgements by humans who the text is aimed for, which hampers addressing errors based on their impact on improving human evaluations. We assessed their possible association with an experiment availing of expert and crowdsourced evaluations of human authored text, template generated text, and sequence-to-sequence model generated text. The results showed that there was no significant association between human authored texts with errors and the low human judgements of naturalness and quality. There was also no significant association between machine learning generated texts with dropped or hallucinated slots and the low human judgements of naturalness and quality. Thus, both approaches appear to be viable options for designing a natural language interface for knowledge graph

Dimensions Affecting Representation Styles in Ontologies

There are different ways to formalise roughly the same knowledge, which negatively affects ontology reuse and alignment and other tasks such as formalising competency questions automatically. We aim to shed light on, and make more precise, the intuitive notion of such `representation styles' through characterising their inherent features and the dimensions by which a style may differ. This has led to a total of 28 different traits that are partitioned over 10 dimensions. The operationalisability was assessed through an evaluation of 30 ontologies on those dimensions and applicable values. It showed that it is feasible to use the dimensions and values and resulting in three easily recognisable types of ontologies. Most ontologies had clearly one or the other trait, whereas some were inherently mixed due to inclusion of different and conflicting design decisions

A classification of grammar-infused templates for ontology and model verbalisation

Involving domain-experts in the development, maintenance, and use of knowledge organisation systems can be made easier through the introduction of easy-to-use interfaces that are based on natural language. Well resourced languages make use of natural language generation techniques to provide such interfaces. In particular, they often make use of templates combined with computational grammar rules to generate grammatically complex text. However, there is no model of pairing templates and computational grammar rules to ensure suitability for less-resourced languages. These languages require a modular design that ensures grammar detachability so as to allow grammar re-use across domains and applications. In this paper, we present a model and classification scheme for grammar-infused templates suited for less-resourced languages and classify existing systems that make use of them. We have found that of the 15 systems that pair templates and grammar rules, and their 11 distinct template types, 13 have support for detachable grammars

TDDonto2: A Test-Driven Development Plugin for arbitrary TBox and ABox axioms

Ontology authoring is a complex task where modellers rely heavily on the automated reasoner for verification of changes, using effectively a time-consuming test-last approach. Test-first with Test-Driven Development aims to speed up such processes, but tools to date covered only a subset of possible OWL 2 DL axioms and provide limited feedback. We have addressed these issues with a model for TDD testing to give more feedback to the modeller and seven new, generic, TDD algorithms that also cover OWL 2 DL class expressions on the left-hand side of inclusions and ABox assertions by availing of several reasoner methods. The model and algorithms have been implemented as a Prot\'eg\'e plugin, TDDonto2