Knowledge-Based Systems for Data Modelling

Šuman, Sabrina; Jakupović, Alen; Kuljanac, Francesca Gržinić

doi:10.4018/ijeis.2016040101

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…We use the artefacts (corpus-based lexicon and CEN PSG-Phrase structure grammar) from previous works [1,46] to obtain valid BD sentences that represent the system input. The MULTEXT-East lexicon and its morpho-syntactic descriptions are used because it covers a broad range of syntax categories and supports the Croatian language (planned in further research).…”

Section: Previous Work Artefactsmentioning

confidence: 99%

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A Corpus-Based Sentence Classifier for Entity–Relationship Modelling

2022

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Automated creation of a conceptual data model based on user requirements expressed in the textual form of a natural language is a challenging research area. The complexity of natural language requires deep insight into the semantics buried in words, expressions, and string patterns. For the purpose of natural language processing, we created a corpus of business descriptions and an adherent lexicon containing all the words in the corpus. Thus, it was possible to define rules for the automatic translation of business descriptions into the entity–relationship (ER) data model. However, since the translation rules could not always lead to accurate translations, we created an additional classification process layer—a classifier which assigns to each input sentence some of the defined ER method classes. The classifier represents a formalized knowledge of the four data modelling experts. This rule-based classification process is based on the extraction of ER information from a given sentence. After the detailed description, the classification process itself was evaluated and tested using the standard multiclass performance measures: recall, precision and accuracy. The accuracy in the learning phase was 96.77% and in the testing phase 95.79%.

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Section: Previous Work Artefactsmentioning

confidence: 99%

“…All BD sentences were formulated to be syntactically correct according to the specific grammar rules (presented in Table 1 of Section 2.4). The phrase structure grammar for BDs and the data model language (EARC; entity attribute relationship cardinalities language) are described in papers [1,2].…”

Section: Introductionmentioning

confidence: 99%

A Corpus-Based Sentence Classifier for Entity–Relationship Modelling

2022

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“…There are many commercial graphical CASE tools which can be used to automatically convert a conceptual model into a logical or physical model (Thonggoom, 2011). However, there is no commercial or noncommercial tool which can automatically convert natural language text into a conceptual model (Song, Zhu, Ceong, & Thonggoom, 2015;Šuman, Jakupović, & Kuljanac, 2016;Thonggoom, 2011). Instead, various semi-automated approaches are used for this purpose, which include the following (Thonggoom, 2011).…”

Section: Natural Language Specification Problemsmentioning

confidence: 99%

“…Conversely, knowledge-based approaches such as the pattern-based, case-based and ontology-based approaches do need a set of linguistic rules extracted from a linguistic approach to guide the process of extraction of CMs from natural language text, since there is no domain-independent knowledge designed to support the creation of conceptual models. Furthermore, because of natural language ambiguities, and the fact that fully-automated extraction of conceptual models from natural language is not possible (Song et al, 2015;Šuman et al, 2016;Thonggoom, 2011), integrated approaches need to be supported by a minimum level of human intervention to help in solving ambiguities in natural language text. An integrated approach supported by a minimum level of human intervention would therefore help in producing a semi-automated tool to guide the process of extracting and producing conceptual models from natural language specifications.…”

Section: Table 21 Comparison Between Approaches Used For Extracting mentioning

confidence: 99%

Semi-automated development of conceptual models from natural language text

Omar

Baryannis

2020

Data & Knowledge Engineering

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The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to help designers translate natural language text into conceptual models, each approach has its limitations. One of the main limitations is the lack of a domain-independent ontology that can be used as a repository for entities and relationships, thus guiding the transition from natural language processing into a conceptual model. Such an ontology is not currently available because it would be very difficult and time consuming to produce. In this thesis, a semiautomated system for mapping natural language text into conceptual models is proposed. The model, which is called SACMES, combines a linguistic approach with an ontological approach and human intervention to achieve the task.

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“…Previous papers 1,2 have described the development phases for creating a phrase structure grammar (PSG) for BDs as well as for the EARC (entity attribute relationship cardinalities) data-modeling language. Their definitions were a prerequisite for creating translation rules for automated translation.…”

Section: Introductionmentioning

confidence: 99%

A dictionary for translation from natural to formal data model language

Šuman

Jakupović

Marinac³

2020

Computational Intelligence

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The paper describes our current research activities and results related to developing knowledge-based systems to support the creation of entity-relationship (ER) models. The authors based obtaining an ER model in textual form on translation from one language into another, that is, from an English controlled natural language into the formalized language of an ER data model. Our translation method consisted of creating translation rules of sentential form parts into ER model constructs based on the textual and character patterns detected in the business descriptions. To enable the computer analyses necessary for creating translation mechanisms, we created a linguistic corpus that contains lists of the business descriptions and the texts of other business materials. From the corpus, we then created a specific dictionary and linguistic rules to automate the business descriptions' translation into the ER data model language. Before that, however, the corpus was enriched by adding annotations to the words related to ER data model constructs. In this paper, we also present the main issues uncovered during the translation process and offer a possible solution with utility evaluation: applying information-extraction performance measures to a set of sentences from the corpus.

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Knowledge-Based Systems for Data Modelling

Cited by 5 publications

References 36 publications

A Corpus-Based Sentence Classifier for Entity–Relationship Modelling

A Corpus-Based Sentence Classifier for Entity–Relationship Modelling

Semi-automated development of conceptual models from natural language text

A dictionary for translation from natural to formal data model language

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