An Automated Multi-component Approach to Extracting Entity Relationships from Database Requirement Specification Documents

Du, Siqing; Metzler, Douglas P.

doi:10.1007/11765448_1

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…Unfortunately, text does not readily provide a holistic view of the involved entities and relationships. Aligned with other authors [20,27,28,48], we argue that extracting a conceptual model can significantly ease the communication between stakeholders.…”

Section: Discussionsupporting

confidence: 80%

“…Our baseline consists of the existing literature on deriving conceptual models from NL requirements [20,27,28,48]. However, we go beyond the limitations of these inspiring techniques, which either (1) require human supervision to appropriately tag the entities and relationships in the text [20,49], or (2) have low accuracy, often due to the ambitious attempt to support arbitrarily complex requirements statements [27,57].…”

Section: Introductionmentioning

confidence: 99%

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Extracting conceptual models from user stories with Visual Narrator

et al. 2017

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Extracting conceptual models from natural language requirements can help identify dependencies, redundancies, and conflicts between requirements via a holistic and easy-to-understand view that is generated from lengthy textual specifications. Unfortunately, existing approaches never gained traction in practice, because they either require substantial human involvement or they deliver too low accuracy. In this paper, we propose an automated approach called Visual Narrator based on natural language processing that extracts conceptual models from user story requirements. We choose this notation because of its popularity among (agile) practitioners and its focus on the essential components of a requirement: Who? What? Why? Coupled with a careful selection and tuning of heuristics, we show how Visual Narrator enables generating conceptual models from user stories with high accuracy. Visual Narrator is part of the holistic Grimm method for user story collaboration that ranges from elicitation to the interactive visualization and analysis of requirements.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Extracting conceptual models from user stories with Visual Narrator

et al. 2017

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“…data flow diagrams). [13], [14], [15], [16] use extracted keywords to construct database models (e.g. Entity Relationship Diagram, Extended Entity Relationship).…”

Section: Related Workmentioning

confidence: 99%

Extracting Elements of Component-Based Systems from Natural Language Requirements

Lau

Nordin

2011

2011 37th EUROMICRO Conference on Software Engineering and Advanced Applications

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Abstract-Extracting keywords from requirements has been done for various modelling purposes, e.g. for defining objectoriented analysis and design models, but it has not been done for mapping requirements directly to (executable) component-based systems. In this paper we argue that the latter is possible if the underlying component model provides suitable encapsulation and hence separation of key elements of component-based systems. We show how we can extract keywords that correspond to elements of a component model that we have defined.

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“…Most of previous work deals with the generation of a well-formed model from the analysis of (un-)structured information: e.g., object oriented analysis model [IlOr05] and EER model [Buc+95,DuMe06,TjBe93,TsCY92]. Most of previous work deals with the generation of a well-formed model from the analysis of (un-)structured information: e.g., object oriented analysis model [IlOr05] and EER model [Buc+95,DuMe06,TjBe93,TsCY92].…”

Section: Application Of Nl In Databasesmentioning

confidence: 99%

Natural Language Processing and Information Systems

2007

Lecture Notes in Computer Science

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This special issue consists of four revised and extended papers selected from the 17th International conference on Applications of Natural Language to Information Systems (NLDB 2012), which was organized in June 2012 in Groningen, the Netherlands. The NLDB conferences 1 bring together researchers interested in the use of natural language processing techniques in applications such as database querying, information systems architecture, software development, and more recently, various applications dealing with automatic enrichment of and advanced search in large volumes of web content.Authors of a selected number of contributions to the conference (12 full papers, 24 short papers, and 17 poster presentations) were asked to develop their paper into a journal article. After additional reviewing, 4 papers were selected for this special issue.'A semi supervised learning model for mapping sentences to logical forms with ambiguous supervision' by Minh Le Nguyen and Akira Shimazu proposes a novel method for bootstrapping a semantic parser from a combination of semantically annotated, but not disambiguated, data and unlabeled data. 'Wikimantic: Toward effective disambiguation and expansion of queries' by Christopher Boston, Hui Fang, Sandra Carberry, Hao Wu, and Xitong Liu is concerned with microblog (tweet) retrieval, and shows that a query disambiguation and expansion method which uses Wikipedia as knowledge resource improves retrieval performance. 'Inducing the contextual and prior polarity of nouns from the induced polarity preference of verbs' by Manfred Klenner and Stefanos Petrakis presents a new method for learning the prior negative or positive sentiment of a noun, based on statistics obtained from a large corpus annotated with syntactic dependency relations. 'Multidimensional topic analysis in political texts' by Cäcilia Zirn and Heiner Stuckenschmidt addresses the issue of automatic content analysis in the social sciences. In particular, it shows that a technique for creating topic models based on party manifestos and coalition contracts is able to predict which party was primarily responsible for the text of which ministry.On behalf of the authors of the selected papers, we would like to thank all reviewers for their detailed and constructive comments on the submitted manuscripts.

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An Automated Multi-component Approach to Extracting Entity Relationships from Database Requirement Specification Documents

Cited by 8 publications

References 12 publications

Extracting conceptual models from user stories with Visual Narrator

Extracting conceptual models from user stories with Visual Narrator

Extracting Elements of Component-Based Systems from Natural Language Requirements

Natural Language Processing and Information Systems

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