Datasets:

WINGNUS
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ACL-OCL

	{
	"paper_id": "2020",
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	"date_generated": "2023-01-19T06:06:30.353188Z"
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	"title": "OWLSIZ: An isiZulu CNL for structured knowledge validation",
	"authors": [
	{
	"first": "Zola",
	"middle": [],
	"last": "Mahlaza",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "University of Cape Town South Africa",
	"location": {}
	},
	"email": "zmahlaza@cs.uct.ac.za"
	},
	{
	"first": "C",
	"middle": [
	"Maria"
	],
	"last": "Keet",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "University of Cape",
	"location": {}
	},
	"email": "mkeet@cs.uct.ac.za"
	}
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	"year": "",
	"venue": null,
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	"abstract": "In iterative knowledge elicitation, engineers are expected to be directly involved in validating the already captured knowledge and obtaining new knowledge increments, thus making the process time consuming. Languages such as English have controlled natural languages than can be repurposed to generate natural language questions from an ontology in order to allow a domain expert to independently validate the contents of an ontology without understanding a ontology authoring language such as OWL. IsiZulu, South Africa's main L1 language by number speakers, does not have such a resource, hence, it is not possible to build a verbaliser to generate such questions. Therefore, we propose an isiZulu controlled natural language, called OWL Simplified isiZulu (OWLSIZ), for producing grammatical and fluent questions from an ontology. Human evaluation of the generated questions showed that participants' judgements agree that most (83%) questions are positive for grammaticality or understandability.",
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	"abstract": [
	{
	"text": "In iterative knowledge elicitation, engineers are expected to be directly involved in validating the already captured knowledge and obtaining new knowledge increments, thus making the process time consuming. Languages such as English have controlled natural languages than can be repurposed to generate natural language questions from an ontology in order to allow a domain expert to independently validate the contents of an ontology without understanding a ontology authoring language such as OWL. IsiZulu, South Africa's main L1 language by number speakers, does not have such a resource, hence, it is not possible to build a verbaliser to generate such questions. Therefore, we propose an isiZulu controlled natural language, called OWL Simplified isiZulu (OWLSIZ), for producing grammatical and fluent questions from an ontology. Human evaluation of the generated questions showed that participants' judgements agree that most (83%) questions are positive for grammaticality or understandability.",
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	"section": "Abstract",
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	"text": "Ontology developers often rely on domain experts when building models or ontologies. This knowledge elicitation is often done in an iterative manner to ensure a high quality artefact. That is, the first interaction between knowledge engineers and domain experts generally focuses on only obtaining the first usable knowledge increment. Further interactions focus on obtaining further increments and also validating the previous iteration's codified knowledge. This requires the engineer to have a method of presenting the codified knowledge in an accessible manner. One method to resolve this, is to render the ontology with a controlled natural language (CNL) and provide supporting tooling for that; see (Safwat and Davis, 2017) for a recent overview. The manner in which these tools are to be used throughout the knowledge acquisition process is left to the discretion of the ontology developer, as they have not been incorporated into existing structured methods of knowledge validation. This may lead to inefficiencies, especially for inexperienced ontologists, as it requires one to be directly involved in obtaining the additional knowledge increment and also validating the already captured knowledge. For a language like English, one could repurpose a CNL such as OWL Simplified English (Power, 2012) to generate questions that domain experts could answer without involving an ontologist. This is more challenging for other languages, since fewer resources exist for them.",
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	"text": "(Safwat and Davis, 2017)",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "For our context in South Africa, isiZulu is relevant, since it is the main language by first language speakers and serves as one of the main communication languages alongside English, Afrikaans, and Setswana (out of 11 official languages and sign language). It is not possible to generate questions from ontologies in isiZulu, since such a CNL does not exist and only some statements can be generated with the CNL of (Keet and .",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "We propose a solution in the form of OWL Simplified isiZulu (OWLSIZ), an isiZulu CNL for authoring questions to be used in structural questionnaires and/or Likert items to reduce the novice ontology engineer's efforts in validating a model or ontology's contents. To support its use, we developed an OWL verbaliser (in Java) that uses the CNL to generate questions. Evaluation of the quality of the generated questions shows that participants agree that most (83%) of the texts are positive since they have at most one participant who considers them to be ungrammatical and unacceptable.",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "The rest of the paper is structured as follows. Section 2 outlines various methods used for knowledge elicitation, Section 3 discusses existing work on generating controlled natural languages from ontologies, Section 4 presents our CNL, Section 5 presents the design of the verbaliser, Section 6 presents the quality of our CNL, Section 7 the discussion of results, and Section 8 concludes.",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "Ontology engineering methodologies (OEMs) can be categorised into three groups: collaborative, non-collaborative, and custom (Kotis et al., 2020) . Of interest to this work are collaborative OEMs, which have well-defined phases and involve numerous stakeholders across the different phases. The methodologies do not restrict one to specific kinds of elicitation methods as evidenced by the nine collaborative OEMs considered by (Kotis et al., 2020) . Of course, many semi-automatic techniques have been proposed over the years, notably NLP-based ontology learning techniques, but the focus here is the stage where there is a domain expert in the loop, who is typically not well-versed in logic.",
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	"section": "Knowledge elicitation and validation",
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	"text": "Some 25 years ago already, strategies like observations, interviews, or task analysis based methods were already proposed (Cooke, 1994) . The chosen method impacts the overall time it takes to acquire the necessary domain knowledge, especially since most OEMs (7 out of 9) are fully iterative and the rest are either partially iterative or agile. Structured interview techniques such as the use of questionnaires, twenty-questions, and Likert scale items (Cooke, 1994) are less time consuming because they are guided. They can still be improved through the automation of question or Likert item creation. To the best of our knowledge, while there are verbalisers that can be used to create Likert items, there are currently no methods for creating questions to be used within structured techniques.",
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	"text": "For instance, if an axiom Human \u2203hasP art.Heart had been added in an ontology authoring iteration, then in its validation stage, a structured questionnaire could link that to a CNL containing the template \"Does each {C1} {OP1} some {C2}?\" to generate the yes/no validation question 'Does each human have as part some heart?'. The domain expert would be validating the knowledge added to the ontology by answering 'yes' and indicating a mistake by answering 'no'.",
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	"text": "There are numerous tools that take ontologies or similar models as input and produce text. For the present purpose, we will categorise them as either educational question generators and model/ontology verbalisers. Tools for the former take an ontology and generate questions to be presented to people to test their knowledge. Tools for the latter take an ontology's axioms or a model's contents and convert them into natural language text to make the input accessible to stakeholders who are not familiar with modelling languages.",
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	"section": "Text generation from ontologies",
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	"text": "The existing educational question generators are built for English only (Chaudhri et al., 2014; Alsubait et al., 2016; Papasalouros et al., 2008; E.V. and Kumar P., 2015; Zhang and VanLehn, 2016) . They either use bare templates or SimpleNLG (Gatt and Reiter, 2009) for realisation, where 'bare' templates are a sequence of fixed words and slot, such as, Model-T's template (Puzikov and Gurevych, 2018) . None of the two realisation components are usable for isiZulu, because of the language's grammatical complexity or its lack of reusable and comprehensive computational grammar rules. Contrarily, the verbalisers offer components that can be used to build an isiZulu CNL for question generation even though none of the existing verbalisers were designed for such a purpose. This is because there is a lot of variability concerning the realisation methods and languages supported by ontology verbalisers, as can be seen in Table 1 .",
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	"text": "While early verbalisers (e.g., (Wilcock, 2003) ) supported only English, a number of systems have since been built to support other languages; see Table 1 for an overview. About half of these other verbalisers (52%) rely on \"grammar-infused templates\" (Mahlaza and Keet, 2019) for surface realisation, i.e., plain templates with some grammar rules to improve some aspects of the generated sentence. Pertinent to our considered language, isiZulu, four verbalisers (Lim and Halpin, 2016; Demey and Heath, 2014; Byamugisha et al., 2016 ) rely on what they call \"patterns\", which might be argued to be a form of template created to capture some linguistic dependency between items.Such patterns, and unlike a regular template, come with linguistic rules that control the value of the concords that are based on the noun class of the inserted word. An example of a pattern, for verbalising 'has part' in isiZulu, is as follows (Keet and Khumalo, 2016) :",
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	"text": "QCall ncx,pl W ncx,pl SC ncx,pl -CONJ-P ncy RC ncy -QC ncy -dwa",
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	"text": "For instance, the \"QCall ncx,pl \" is for the universal quantification (\u2200, 'for all'), which is generated 2012OWL Grammatical framework \u00d7 English, Catalan, Dutch, Finnish, French, German, Italian, Spanish, Swedish, and Urdu Sadoun et al. 2016OWL Canned text \u00d7 Arabic, English, French, Hindi, Japanese, Mandarin Chinese, Russian, Ukrainian and Tibetan Jarrar et al. 2006ORM Template \u00d7 English, Dutch, German, Italian, Spanish, Catalan, French, Lithuanian, Russian, and Arabic Lim and Halpin 2016ORM \"Pattern\" P English, Malay, and Mandarin Dann\u00e9lls et al. 2013aOWL based on what the plural of the noun class is of the noun that denotes the entity that plays the whole in the has-part relation (the \"W ncx,pl \"), combining the appropriate quantitative concord (e.g., ba-for noun class 2) with -onke to generate the appropriate surface realisation (e.g., bonke, for nouns in noun class 2). An axiom with \u2200Isibhedlela ('hospital', in noun class 7) then verbalises as zonke izibhedlela ('all hospitals') after applying the appropriate algorithms associated with the pattern. Compare this with straightforward templates for English for the same scenario: where the text remains the same and the ontology vocabulary can simply be plugged into the variable slots, or, at most, generate a plural version of a variable in the singular.",
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	"text": "Demey and Heath (2014)'s patterns are syntax templates, which are not suitable for isiZulu due to lack of existing computational syntax rules. The other patterns are potentially suitable because they are created to introduce rules for noun-related dependencies, which are essential to all the languages in the Niger-Congo B family. For instance, Lim and Halpin 2016 These two kinds of noun-centered patterns were not used to generate questions from OWL in isiZulu, but statements, rather. Moreover, they are either limited capturing a single POS that is controlled by noun (Lim and Halpin, 2016) or tightly couple the linearization algorithm with the tem-plate (Keet and Byamugisha et al., 2016) . In this work, in contrast, we design a CNL that is able to generate questions, whose grammarinfused templates are not tightly coupled with the linearisation algorithm, and are able to capture dependencies between the noun and numerous other parts-of-speech.",
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	"text": "The approach used to design the CNL to generate validation questions in isiZulu from ontologies, is incremental and bottom-up. We begin by selecting the first set of OWL constructors to support by selecting ones already supported by an existing English CNL and use its templates as inspiration for our isiZulu templates. The chosen CNL for that is OWL Simplified English (Power, 2012) and the resulting OWLSIZ templates are provided in Table 2 , together with an approximate English translation for indicative purpose.",
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	"section": "OWL Simplified isiZulu",
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	"text": "The isiZulu templates use several concords 1 , copulas, and locative affixes. They are polymorphic affixes whose values change depending on the noun in which they are found in the case of the locative prefix and copula or due to the noun class of another noun in the sentence in the case of concords. In Table 2 , we use a box around a sequence of affixes to illustrate a decomposed word whose underlying morphemes are provided in a sequential manner. We also use an arrow from a concord to the noun that controls its value. For instance, in template 10, the third word is made up of two affixes: the subject concord (SC) and -odwa. The subject concord's value depends on the noun class of the value inserted into the class slot (i.e., {C}) and there are 17 such classes in isiZulu. When the subject concord's value is inserted, then phonological conditioning may be applied when combining the two affixes, since isiZulu does not permit consecutive vowels. For instance, when noun class 15's ku-is appended to -odwa, we obtain kodwa.",
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	"text": "To illustrate the use of the isiZulu templates, let us consider how to verbalise as a question an axiom of the type A B, e.g., ihebhu umuthi and in OWL functional syntax style, SubClassOf(ihebhu umuthi). Template 1 would be chosen, since the B in the position of C2 is a named class. One would then obtain the following text (i) Ingabe lonke ihebhu lingumuthi?",
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	"text": "1 consult Meeussen (1967) for a detailed categorisation of concords Is SC-every herb SC-COP-plant? 'Is every herb a plant?' In this example, the concords and copula are indicated in bold: the concords are the lo-(from li-+ o-) and li-components of the highlighted text, which are governed by the noun class of the first noun, ihebhu 'herb', and the copula is the -ng-component, which is determined by the first character of the second noun, u-.",
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	"text": "We designed the verbaliser for OWLSIZ in the way shown with the architecture diagram in Figure 1 . It was implemented in Java and it uses the OWL API 2 to parse the ontology. Unlike the predominant realisation method as listed in Table 1 , GF, we have chosen to rely on Java, because that enables us to have a verbaliser that can readily generate text from any ontology (provided it has isiZulu labels). GF-based verbalisers, on the other hand, require two additional time-consuming steps, being converting OWL ontologies into GF (e.g., (Angelov and Enache, 2010) ), and the high start-up costs in developing a resource grammar that faces the usual difficulty for under-resourced languages in that there is scant documentation of the grammar.",
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	"text": "We will discuss the planning and realisation components in the remainder of this section. There are minimal rules for selecting a template for each supported axiom type. Only one logical axiom (SubClassOf) and one class expression (Ob-jectSomeValuesFrom) have multiple template forms. Template 1 verbalises SubClassOf axioms where the range is an OWL class and Template 1.1 handles cases where the range is a class expressions. A single rule is used by the verbaliser to choose between the two forms of templates.",
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	"text": "There are four different templates for the Ob-jectSomeValuesFrom class expressions (number 6). 2: List of OWL Simplified IsiZulu templates. The arrows indicate dependencies, pointing from dependent to the determiner, and a box around elements means that those elements inside it will result in one word in the sentence generated. Abbreviations: I = individual, C = class, DP = data property, OP = Object property, CE = Class expression, OP noun = Object property that is noun, DP = Data property, L = Literal, N = Cardinality, SC = subject concord, OC = object concord, COP = copula prefix, RelC = relative concord, LocPre = locative prefix. Template 6.4 is applicable when the object property is a noun and the OWL class belongs to noun classes 1, 1a, 2, or 2a, whereas template 6.2 is used when that OWL class does not belong to classes 1a, 2, or 2a. Template 6.3 is applicable when the object property is the containment part-whole relation (Keet and Khumalo, 2016) . Template 6 and 6.1 are applicable when the range is an OWL class and class expression respectively. The verbaliser has a planning rule that chooses an appropriate template when given a class expression.",
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	"text": "The nncPairs of Figure 1 is a file nncPairs.txt that is used for determining whether an object property is or contains a noun and to retrieve the noun class of the noun, which is reused from Keet et al. 's isiZulu verbaliser (Keet et al., 2017) . We extended the file by adding 8 words to add more variation (umakhalekhukhwini, uZola, iNokia 3310, ifoni, umfundi, ukubhukuda, ihebhu, and umdlalo) and changed the noun class annotations of 6 nouns (ufulawa 3a, amanzi 6, irhaba 9, ivazi-9, ithiyetha yokuhlinzela 9, indoda 5) due to two typos, mass noun marker deletion, and differences of opinion on whether noun class 9a is indeed separate from noun class 9.",
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	"text": "The realisation module takes a template and slot fillers, inserts the fillers into their respective slots, resolves the values of concords, locatives, and copulas in polymorphic words, and then forms words by appending the affixes together while relying on isiZulu phonological conditioning rules taken from (Naidoo, 2002; Sibanda, 2007; Pretorius and Bosch, 2010; Van der Spuy, 2014; Posthumus, 2016) .",
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	"end": 401,
	"text": "Posthumus, 2016)",
	"ref_id": "BIBREF36"
	}
	],
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	"eq_spans": [],
	"section": "Verbaliser implementation",
	"sec_num": "5"
	},
	{
	"text": "In order to evaluate the quality of questions generated by the verbaliser, we make use of the test ontology presented in . We extended the ontology's 82 logical axioms with 12 axioms to ensure coverage for OWLSIZ: ClassAssertion (2), ObjectProperty (2), ObjectPropertyAssertion (1), EquivalentClasses (1), DataPropertyAssertion (1), DataProperty (1), ObjectAllValuesFrom (1), ObjectExactCardinality (1), ObjectMinCardinality (1), and ObjectMaxCardinality (1). This update resulted in an ontology with 91 axioms (it is not 94 because 3 axioms were used to constrain the existing axioms).",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Evaluation",
	"sec_num": "6"
	},
	{
	"text": "For internal evaluation, the ontology was verbalised and we categorized the resulting texts into verbalisable and unverbalisable. These two classes capture our verbaliser's (un)supported axioms. For each of the verbalisable axioms, we analysed their corresponding text to determine whether there are phonological conditioning errors, morphological agreement errors, and any other grammatical error.",
	"cite_spans": [],
	"ref_spans": [],
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	"section": "Evaluation",
	"sec_num": "6"
	},
	{
	"text": "For external evaluation, the ontology was verbalised and we packaged the resulting questions into a survey. Participants were recruited via snowball sampling using the first author's Twitter and WhatsApp accounts. The participants were asked to judge the quality of each question by choosing either \"grammatical and acceptable\", \"grammatical and ambiguous\", \"ungrammatical and understandable\", or \"ungrammatical and unacceptable\". In order to obtain high quality judgements, we ensured that each participants did not judge more than 40 sentences by randomly diving the 76 texts into two surveys each containing 38 texts (henceforth, survey A and B). Survey A includes texts generated by templates 3, 10, 12, and 13 while survey B does not and survey B includes texts generated by templates 4, 8, and 11 while survey A does not. Participants were randomly assigned to a survey.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Evaluation",
	"sec_num": "6"
	},
	{
	"text": "The verbaliser's input, chosen templates, and numbered output texts are given as supplementary material at https://github.com/AdeebNqo/ grammarinfusedtemplates.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Evaluation",
	"sec_num": "6"
	},
	{
	"text": "Out of the total 91 axioms, 76 were verbalisable axioms and of their corresponding texts, 74 texts are free of morphological agreement errors, phonological conditioning errors, or any other grammatical errors as assessed by the first author, an L2 isiZulu speaker with experience with various aspects of isiZulu grammar (e.g., (Mahlaza, 2018) ). Template 5 was selected when verbalising Disjoint-Classes(isidlanyama isidlazitshalo), and the system generated the following output:",
	"cite_spans": [
	{
	"start": 327,
	"end": 342,
	"text": "(Mahlaza, 2018)",
	"ref_id": "BIBREF31"
	}
	],
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	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "(i) asikho yini isidlanyama esiyisidlazitshalo?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "NEG-SC-exist carnivore [N C7] RelC-COP- herbivore [N C7] ?",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "'Is there no carnivore that is a herbivore?' In the output, isidlazitshalo is prefixed with esiand -y-where esi-is a relative concord that would have a different value if isidlanyama was not used (e.g, it would be eli-if a noun that belongs to class 5 was used instead). Similarly, -y-is the copulative prefix value; if the value inserted into {C2} had u, o, or a as preceding vowel, then -ng-would be used instead.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "One out of the two questions determined to have errors had an morphological agreement error (question 42 in the supplementary material). The second was generated by Template 8 and, when verbalising DataPropertyAssertion(neminyaka uZola 50), the system generated the following output: where the subject concord (SC) depends on the individual (uZola) and the relative concord (RelC) depends on the noun found in the object property (i.e., iminyaka). We cannot correct template 8 by introducing the subject and relative concords as done for this particular example because different categories of data properties may require different solutions.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "The 15 that were classified as unverbalisable had several causes. 10 had nouns whose classes were unresolved; e.g., one where the compound noun's noun class was unresolved (isampula igazi ought to have been isampula egazi 'blood sample' in noun class 5). The other five included unsupported axiom types, such as ObjectComplementOf.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "Six participants filled in one of the surveys, of which five L1 isiZulu speakers and one L2. Survey A had two participants (one L1 and one L2 isiZulu speakers) who made a total of 76 judgements. Survey B had four participants and they made a total of 152 judgements (38 per participant). The separation of the participants' judgements into their respective categories is listed in Table 3 . A majority of the judgements were that the texts are grammatical and acceptable; there were 41/76 in Survey A and 78/152 in Survey B. Overall, i.e., combining Survey A and B, most of the judgements were positive, since only 19% of the judgements were ungrammatical + unacceptable. The participants' judgements of each question are given in Figure 2 . Observe that there are only two questions (labelled 25 and 42 in the figure) for which participants agree that they are ungrammatical and unacceptable, which are: 25 : iNokia 3310 lifundisa uZola? 'The Nokia 3310 teaches Zola' 42 : Ingabe noma yiyiphi indlu eyinyama?",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 381,
	"end": 388,
	"text": "Table 3",
	"ref_id": "TABREF6"
	},
	{
	"start": 731,
	"end": 739,
	"text": "Figure 2",
	"ref_id": "FIGREF2"
	}
	],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "'Is every house (the same as) meat? These two questions were generated by template 3 and 4, respectively. This does not mean that they are of low quality, for the following reasons. Question 25 is ungrammatical and unacceptable due to the presence of the word eyinyama as opposed to iyinyama in that specific context. Analysis of the reason why an 'e' was used as opposed to 'i' shows that the serialised template used by Java verbaliser is slightly different from the one listed in Table 2 as we mistakenly used the relative concord in place of the subject concord. Question 42 is likely judged ungrammatical and unacceptable by participants due to unfamiliarity of the noun, as they may be unsure to which noun class 'Nokia 3310' belongs: by default, foreign objects are allocated to noun class 5 (hence iNokia), but 'mobile phone', that the Nokia is, is umakhalekhukhwini, which is in noun class 3. Figure 2 also shows that 83% of the texts are judged positively as they have at most one participant who considers them to be ungrammatical and unacceptable. Furthermore, since Survey A only had two participants, even if we adjust how we determine the number texts judged positively and define them as the texts where is no participant who considers the text to be ungrammatical and unacceptable, we still find that most (71%) of Survey A's questions were judged positively by the participants.",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 483,
	"end": 490,
	"text": "Table 2",
	"ref_id": null
	},
	{
	"start": 902,
	"end": 910,
	"text": "Figure 2",
	"ref_id": "FIGREF2"
	}
	],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "Participants agreed in their judgements (i.e., they chose the same options out of the ones listed in Section 6) for 25/76 texts (more specifically, 16/38 for Survey A and 9/38 for survey B). Furthermore, out of the 25, they agreed positively to 24 of the texts and agreed that remaining question is ungrammatical and unacceptable. While in (Keet and Khumalo, 2014) it seemed that there was more disagreement in the human evaluation the longer the sentences were, here, the participants' disagreement is not due to differences in text length. This since the texts for which the participants agree and disagree are of similar length (agree = average of 4 words and disagree = 5 average words), save for a single outlier with 7 words in the texts for which they disagree. The disagreement may be due to a misunderstanding that the participants have regarding how to evaluate the texts. For instance, when given text that reflects an unacceptable conceptualisation of the real world, then participants may be selecting \"ungrammatical and unacceptable\" to reflect the unacceptability of the conceptualisation, as opposed to evaluating the quality of the text. This is suspected in the evaluation of the question Ingabe lonke ibhotela lenza ifoni eliyi-1 ncamashi? ('Does every butter make exactly 1 phone?') where three participants selected \"Ungrammatical and unacceptable\" and one selected \"Grammatical and ambiguous\", even though the text is grammatically correct as judged by the first author. Furthermore, regional differences in the dialects spoken by the participants may result in one participant judging a particular text acceptable while others judge it ambiguous.",
	"cite_spans": [
	{
	"start": 350,
	"end": 364,
	"text": "Khumalo, 2014)",
	"ref_id": "BIBREF24"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "We also determined the number of texts that all participants judge as being grammatically correct irrespective of whether they are acceptable or ambiguous. This was calculated by counting the number of texts where participants judged them as any combination of 'grammatical and ambiguous' and 'grammatical and acceptable'. We found that only 51% were judged as being grammatically correct hence 37/76 are ungrammatical. Moreover, the participants all agree that a majority (20/37) of the ungrammatical texts are still acceptable.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "The evaluation shows that the templates produce texts, whose majority is found to be understandable and grammatically correct by isiZulu speakers. Moreover, even when generated texts are ungrammatical, most of them are still acceptable. Most of the texts generated by the templates are free of morphological agreement errors. In the only case where there was such an error (question 42), it was due to human error when creating the serialised template and not a problem with the templates listed in Table 2 . The above observations suggest that the verbaliser can be used successfully (i.e., with isiZulu domain experts able to understand the meaning of the questions) when validating an ontology that has the axioms listed in Table 2 , with the exception of axioms involving the Equivalent-Classess type.",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 499,
	"end": 506,
	"text": "Table 2",
	"ref_id": null
	},
	{
	"start": 727,
	"end": 734,
	"text": "Table 2",
	"ref_id": null
	}
	],
	"eq_spans": [],
	"section": "Results and discussion",
	"sec_num": "7"
	},
	{
	"text": "We have created the first isiZulu CNL and verbaliser capable to generating questions from an ontology for the purpose of knowledge elicitation and validation. Evaluation of the quality of text generated by the implemented verbaliser shows that most (81%) of the participants' judgements, overall, are positive. Moreover, when we analysed judgements aggregated for each question, we find that most of the texts (83%) are judged positively because they have at most one participant who considers them to be ungrammatical and unacceptable. Adjusting how we determine the number of texts judged positively for Survey A since there were only two participants, we found that most (71%) of Survey A's questions were also judged positively because they had no participant who considers them to be ungrammatical and unacceptable. Overall, while there is a sizeable number (37/76) of questions for which the participants agree that they are ungrammatical, a majority of them (20/37) are still judged as being acceptable.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Conclusions and future work",
	"sec_num": "8"
	},
	{
	"text": "Future work includes determining which templates are generating unacceptable texts, soliciting an isiZulu grammarian's feedback regarding its grammaticality, and correcting the identified problems.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Conclusions and future work",
	"sec_num": "8"
	},
	{
	"text": "https://github.com/owlcs/owlapi",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	}
	],
	"back_matter": [
	{
	"text": "This work was financially supported by Hasso Plattner Institute for Digital Engineering through the HPI Research School at UCT and the National Research Foundation (NRF) of South Africa (Grant Number 120852).",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Acknowledgments",
	"sec_num": null
	}
	],
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	"text": "use them to encode Malay and Mandarin noun classifiers and Keet and Khumalo (2017); Byamugisha et al. (2016) use theirs to capture agreement between nouns and a number of parts-of-speech in isiZulu and Runyankore.",
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	"text": "Verbaliser architecture",
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	"text": "Ratings of the sentences, aggregated by sentence. Survey A texts are numbered 1-38 and Survey B texts are numbered 39-76. Abbreviations: Gramm. + Ambig. = grammatical and ambiguous, Gramm. + Accept. = grammatical and acceptable, Ungramm. + Underst. = ungrammatical and understandable, Ungramm. + unaccept. = ungrammatical and unacceptable.",
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	"text": "List of verbalisers with relevant core features. Abbreviations: OWL = Web Ontology Language, ORM = Object-relational mapping, FBM = Fact Based Modeling and the template classification (classif.) abbreviations fromMahlaza and Keet (2019) are P = partial attachment, C = compulsory attachment, CE = compulsory attachment and embedding, and EP = embedding and partial attachment",
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	"content": "<table><tr><td>Reference Dann\u00e9lls et al. (2013b) Camilleri et al.</td><td>Input Realisation method OWL Grammatical framework</td><td>Template classif. \u00d7</td><td>Language(s) English, Bulgarian, Catalan, Danish, Dutch, Finnish, French, Hebrew, Italian, German, Norwegian, Romanian, Russian, Spanish, and Swedish</td></tr></table>",
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	"text": "Each [Whole] has as part at least one [Part]. All [Whole] pl have as part at least one [Part].",
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	"text": "Number of participants' judgements. Abbreviations: Gramm. + ambig. = grammatical and ambiguous, Gramm. + accept. = grammatical and acceptable, Ungramm. + understand. = ungrammatical and understandable, Ungramm. + unaccept. = ungrammatical and unacceptable, and Pct. = percent",
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	"content": "<table><tr><td>Survey</td><td>Gramm. + am-big.</td><td>Gramm. + ac-cept.</td><td>Ungramm. + under-stand.</td><td>Ungramm. + unac-cept.</td></tr><tr><td colspan=\"2\">A B A+B A+B Pct. 18% 17 23 40</td><td>41 78 119 52%</td><td>6 19 25 11%</td><td>12 32 44 19%</td></tr></table>",
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