Natural Language-Based Approach for Helping in the Reuse of Ontology Design Patterns

de, Guadalupe Aguado; Gómez-Pérez, Asuncíon; Montiel-Ponsoda, Elena; Suárez-Figueroa, Mari Carmen

doi:10.1007/978-3-540-87696-0_6

Cited by 26 publications

(11 citation statements)

References 15 publications

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“…Themis supports a list of possible tests expressions which are extracted from LSPs and that can be executed on an ontology. LSPs are understood as "formalized linguistic schemas or constructions derived from regular expressions in natural language that consist of certain linguistic and paralinguistic elements, following a specific syntactic order, and that permit to extract some conclusions about the meaning they express" [13]. The LSPs used by Themis were extracted from the CORAL corpus 8 which, based on the NeOn modelling components [14], analyses 834 ontology requirements in order to identify LSPs based on the goal that each requirement has regarding its implementation in an ontology, e.g., a relation between two concepts.…”

Section: A Test Designmentioning

confidence: 99%

Themis: a tool for validating ontologies through requirements

Fernández-Izquierdo¹,

García‐Castro²

2019

International Conferences on Software Engineering and Knowledge Engineering

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The validation of ontologies, whose aim is to check whether an ontology matches the conceptualization it is meant to specify, is a key activity for guaranteeing the quality of ontologies. This work is focused on the validation through requirements, with the aim of assuring, both the domain experts and ontology developers, that the ontologies they are building or using are complete regarding their needs. Inspired by software engineering testing processes, this work proposes a web-based tool called Themis, independent of any ontology development environment, for validating ontologies by means of the application of test expressions which, following lexicosyntactic patterns, represent the desired behaviour that will present an ontology if a requirement is satisfied.

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Section: A Test Designmentioning

confidence: 99%

Themis: a tool for validating ontologies through requirements

Fernández-Izquierdo¹,

García‐Castro²

2019

International Conferences on Software Engineering and Knowledge Engineering

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“…Ontology Design Patterns (ODPs) are essentally sets of conceptual patterns designed to help a user construct or refine a domain ontology. Tools for supporting the semi-automatic reuse of these have also been developed [179]. ese tools take as input text relevant to the domain, and obtain as output a set of ODPs for solving the initial ontological needs.…”

Section: Semi-automatic Ontology Creationmentioning

confidence: 99%

“…An alternative to ODPs for the non-expert user is the use of syntaxes or controlled languages specifically designed to make ontology languages more readable and understandable by others. Examples include Attempto Controlled English (ACE) [180], Rabbit [181], Sydney OWL Syntax [182], and CLOnE (Controlled Language for Ontology Editing) [183]. Some example sentences from these are shown in Table 6.4.…”

Section: Semi-automatic Ontology Creationmentioning

confidence: 99%

Natural Language Processing for the Semantic Web

Maynard

Bontcheva

Augenstein

2016

Synthesis Lectures on the Semantic Web: Theory and Technology

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is book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks.e first half of this book describes the basic NLP processing tools: tokenization, part-ofspeech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. e second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. ese chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. e book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future. KEYWORDSnatural language processing, semantic web, semantic search, social media analysis, text mining, linked data, entity linking, information extraction, sentiment analysis is book is a timely exposition of natural language processing and its role and importance for those seeking to apply semantic technologies. Clearly written with good coverage of the key topics and a comprehensive bibliography, the text will be invaluable for semantic web practitioners and more widely.

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“…The correct application of such methodologies benefits ontology quality. However, such quality is not totally guaranteed because developers must tackle a wide range of difficulties and handicaps when modelling ontologies [1,2,5,8]. These difficulties can imply the appearance of the so-called anomalies or bad practices in ontologies.…”

Section: Introductionmentioning

confidence: 99%

Did You Validate Your Ontology? OOPS!

Poveda‐Villalón

Suárez-Figueroa

Gómez-Pérez

2015

Lecture Notes in Computer Science

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Abstract. The application of methodologies for building ontologies can improve ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. Several authors have provided lists of typical anomalies detected in ontologies during the last decade. In this context, our aim in this paper is to describe OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.Keywords: pitfalls, bad practices, ontology evaluation, ontology engineering IntroductionThe growing interest during the last decades of practitioners in ontology development methodologies have supposed a step forward in transforming the art of building ontologies into an engineering activity. The correct application of such methodologies benefits ontology quality. However, such quality is not totally guaranteed because developers must tackle a wide range of difficulties and handicaps when modelling ontologies [1,2,5,8]. These difficulties can imply the appearance of the so-called anomalies or bad practices in ontologies. Therefore, it is important to evaluate the ontologies before using or reusing them in other ontologies or semantic applications. One of the crucial issues in ontology evaluation is the identification of anomalies in the ontologies. In this regard, it is worth mentioning that Rector et al. [8] describe a set of common errors made by developers during the ontology modelling. Moreover, Gómez-Pérez [4] proposes a classification of errors identified during the evaluation of different features such as consistency, completeness, and conciseness in ontology taxonomies. Finally, Poveda et al. [7] identify an initial catalogue of common pitfalls.In this context, our goal within this paper is to present an automated tool to help ontology practitioners by detecting common pitfalls during the ontology development. This tool is called OOPS! (OntOlogy Pitfall Scanner!) and represents a new option for ontology developers within ontology evaluation tools as it enlarges the list of errors detected by most recent and available works (e.g. MoKi 1 [6] and XD Analyzer 2 ). In addition, OOPS! can be executed independently of the ontology development plat-1 https://moki.fbk.eu/moki/tryitout/index.php/Main_Page (Last visit on 14-04-2012) 2 http://neon-toolkit.org/wiki/XDTools (Last visit on: 14-04-2012)

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Natural Language-Based Approach for Helping in the Reuse of Ontology Design Patterns

Cited by 26 publications

References 15 publications

Themis: a tool for validating ontologies through requirements

Themis: a tool for validating ontologies through requirements

Natural Language Processing for the Semantic Web

Did You Validate Your Ontology? OOPS!

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