OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors &amp; Common Patterns

Rector, Alan L.; Drummond, Nick; Horridge, Matthew; Rogers, Jamie; Knublauch, Holger; Stevens, Robert; Wang, Hai; Wroe, Chris

doi:10.1007/978-3-540-30202-5_5

Cited by 202 publications

(184 citation statements)

References 5 publications

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“…One is that reasoning systems themselves involve a logic of negation which does not gell with the uses of negation in standard terminologies. Second, the treatment of negation in popular computational idioms such as OWL DL itself involves non-trivial (and sometimes confusingly documented) features which set traps for inexpert users [8].…”

Section: Introductionmentioning

confidence: 99%

Negative findings in electronic health records and biomedical ontologies: A realist approach

Ceusters

Elkin

Smith

2007

International Journal of Medical Informatics

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PURPOSE-A substantial fraction of the observations made by clinicians and entered into patient records are expressed by means of negation or by using terms which contain negative qualifiers (as in "absence of pulse" or "surgical procedure not performed"). This seems at first sight to present problems for ontologies, terminologies and data repositories that adhere to a realist view and thus reject any reference to putative non-existing entities. Basic Formal Ontology (BFO) and Referent Tracking (RT) are examples of such paradigms. The purpose of the research here described was to test a proposal to capture negative findings in electronic health record systems based on BFO and RT.METHODS-We analysed a series of negative findings encountered in 748 sentences taken from 41 patient charts. We classified the phenomena described in terms of the various top-level categories and relations defined in BFO, taking into account the role of negation in the corresponding descriptions. We also studied terms from SNOMED-CT containing one or other form of negation. We then explored ways to represent the described phenomena by means of the types of representational units available to realist ontologies such as BFO.RESULTS-We introduced a new family of 'lacks' relations into the OBO Relation Ontology. The relation lacks_part, for example, defined in terms of the positive relation part_of, holds between a particular p and a universal U when p has no instance of U as part. Since p and U both exist, assertions involving 'lacks_part' and its cognates meet the requirements of positivity.CONCLUSION-By expanding the OBO Relation Ontology, we were able to accommodate nearly all occurrences of negative findings in the sample studied.

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Section: Introductionmentioning

confidence: 99%

Negative findings in electronic health records and biomedical ontologies: A realist approach

Ceusters

Elkin

Smith

2007

International Journal of Medical Informatics

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“…The development of ontologies using directly ontology languages, such as OWL DL, is not as trivial as reported in [23]. Alan Rector et al [23] present the most common problems, errors, and misconceptions on understanding OWL DL as well as tips on how to avoid such pitfalls in building OWL DL ontologies.…”

Section: Motivationmentioning

confidence: 99%

“…Alan Rector et al [23] present the most common problems, errors, and misconceptions on understanding OWL DL as well as tips on how to avoid such pitfalls in building OWL DL ontologies. In [23] it is stated that for most people it is very difficult to understand the logical meaning and potential inferred statements of any DL formalism, including OWL DL. Such a paper mentions that one of the most common errors in building ontologies in OWL is to omit the disjointness axioms, when taxonomies are being modeled within ontologies.…”

Section: Motivationmentioning

confidence: 99%

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

Gómez-Pérez

Montiel-Ponsoda

et al. 2008

Knowledge Engineering: Practice and Patterns

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Abstract. Experiments in the reuse of Ontology Design Patterns (ODPs) have revealed that users with different levels of expertise in ontology modelling face difficulties when reusing ODPs. With the aim of tackling this problem we propose a method and a tool for supporting a semi-automatic reuse of ODPs that takes as input formulations in natural language (NL) of the domain aspect to be modelled, and obtains as output a set of ODPs for solving the initial ontological needs. The correspondence between ODPs and NL formulations is done through Lexico-Syntactic Patterns, linguistic constructs that convey the semantic relations present in ODPs, and which constitute the main contribution of this paper. The main benefit of the proposed approach is the use of non-restricted NL formulations in various languages for obtaining ODPs. The use of full NL poses challenges in the disambiguation of linguistic expressions that we expect to solve with user interaction, among other strategies.

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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%

“…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.…”

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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OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors & Common Patterns

Cited by 202 publications

References 5 publications

Negative findings in electronic health records and biomedical ontologies: A realist approach

Negative findings in electronic health records and biomedical ontologies: A realist approach

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

Did You Validate Your Ontology? OOPS!

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