A Double Classification of Common Pitfalls in Ontologies

The application of methodologies for building ontologies has improved the ontology quality. However, such a quality is not totally guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or worst practices in the modelling. In this context, our aim in this paper is twofold: (1) to provide a catalogue of common worst practices, which we call pitfalls, and (2) to present a double classification of such pitfalls. These two products will serve in the ontology development in two ways: (a) to avoid the appearance of pitfalls in the ontology modelling, and (b) to evaluate and correct ontologies to improve their quality

Pattern for Re-engineering a Classification Scheme, which Follows the Adjacency List Data Model, to a Taxonomy

This pattern for re-engineering non-ontological resources (pr-nor) fits in the schema re-engineering category proposed by [3]. The pattern defines a procedure that transforms the classification scheme components into ontology representational primitives. This pattern comes from the experience of ontology engineers in developing ontologies using classification schemes in several projects (seemp 1 , neon 2 , and knowledge web 3 ). The pattern is included in a pool of patterns, which is a key element of our method for re-engineering non-ontological resources into ontologies [2]. The patterns generate the ontologies at a conceptualization level, independent of the ontology implementation language

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Methylation changes associated with early maturation stages in the Atlantic salmon

<p>Abstract</p> <p>Background</p> <p>Early maturation in the Atlantic salmon is an interesting subject for numerous research lines. Prior to sea migration, parr can reach sexual maturation and successfully fertilize adult female eggs during the reproductive season. These individuals are known as precocious parr, mature parr or "sneakers". Reasons for early maturation are unknown and this transitory stage is usually considered to be a threshold trait. Here, we compare methylation patterns between mature and immature salmon parr from two different rivers in order to infer if such methylation differences may be related to their maturation condition. First we analyzed genetic differences between rivers by means of AFLPs. Then, we compared the DNA methylation differences between mature and immature parrs, using a Methylation-Sensitive Amplified Polymorphism (MSAP), which is a modification of the AFLPs method by making use of the differential sensitivity of a pair of restriction enzymes isoschizomeres to cytosine methylation. The tissues essayed included brain, liver and gonads.</p> <p>Results</p> <p>AFLPs statistical analysis showed that there was no significant differentiation between rivers or a significant differentiation between maturation states in each river. MSAP statistical analysis showed that among the three tissues sampled, the gonads had the highest number of significant single-locus variation among populations with 74 loci followed by brain with 70 and finally liver with only 12. Principal components analysis (PCA) of the MSAP profiles revealed different profiles among different tissues (liver, brain and testis) clearly separating maturation states in the testis tissue when compared to the liver.</p> <p>Conclusions</p> <p>Our results reveal that genetically-similar mature and immature salmon parr present high levels of DNA methylation variation in two of the three analyzed tissues. We hypothesize that early maturation may be mostly mediated by epigenetic processes rather than by genetic differences between parrs. To our knowledge this is the first study that attempt to link phenotypic plasticity in salmonids and epigenetic changes.</p

A Pattern Based Approach for Re-engineering Non-Ontological Resources into Ontologies

With the goal of speeding up the ontology development process, ontology engineers are starting to reuse as much as possible available ontologies and non-ontological resources such as classification schemes, thesauri, lexicons and folksonomies, that already have some degree of consensus. The reuse of such non-ontological resources necessarily involves their re-engineering into ontologies. Non-ontological resources are highly heterogeneous in their data model and contents: they encode different types of knowledge, and they can be modeled and implemented in different ways. In this paper we present (1) a typology for non-ontological resources, (2) a pattern based approach for re-engineering non-ontological resources into ontologies, and (3) a use case of the proposed approach

On the development of a trait-based approach for studying Neotropical bats

New World bats are involved in key ecological processes and are good indicators of environmental changes. Recently, trait-based approaches have been used in several taxa to better understand mechanisms underlying species assemblages, biotic interactions, environmental relationships and ecosystem functions. However, despite the relevance of bats on ecosystem dynamics, so far, there is no conceptual framework that relies on the measurement of bat traits to address functional studies. Here, we present a set of 50 bat biological traits, which are suitable to assess environmental stressors and can potentially affect ecological processes. Several examples were provided to show the applicability of this framework in the study of Neotropical bat ecology. We suggest some considerations regarding trait-based approach including the importance of intraspecific variation, correlations between traits, response-effect framework, global dataset, and future directions to assess the reliability of functional relations across species and Neotropical regions by using traits. This could be helpful in tackling ecological questions associated with community assembly and habitat filtering, species diversity patterns along environmental gradients, and ecological processes. We envision this paper as a first step toward an integrative bat functional trait protocol held up with solid evidence

Results of Taxonomic Evaluation of RDF(S) and DAML+OIL Ontologies using RDF(S) and DAML+OIL Validation Tools and Ontology Platforms Import Services

Before using RDF(S) and DAML+OIL ontologies in Semantic Web applications, its content should be evaluated from a knowledge representation point of view. In recent years, some RDF(S) and DAML+OIL ‘checkers’, ‘validators’, and ‘parsers’ have been created and several ontology platforms are able to import RDF(S) and DAML+OIL ontologies. Two are the experiments presented in this paper. The first one reveals that the majority of RDF(S) and DAML+OIL parsers (Validating RDF Parser, RDF Validation Service, DAML Validator, and DAML+OIL Ontology Checker) do not detect taxonomic mistakes in ontologies implemented in such languages. So, if such ontologies are imported by ontology platforms, are they able to detect such problems? The second experiment presented in this paper reveals that the majority of the ontology platforms (OilEd, OntoEdit, Protégé-2000, and WebODE) only detect a few of mistakes in concept taxonomies before importing them

Ontology Evaluation Functionalities of RDF(S), DAML+OIL, and OWL Parsers and Ontology Platforms

Before using ontologies in Semantic Web applications, ontology content and ontology tools (parsers, platforms, etc.) should be evaluated. In this paper we evaluate ontology evaluation functionalities of RDF(S), DAML+OIL, and OWL parsers and import services for such languages within ontology platforms. In recent years, some RDF(S), DAML+OIL, and OWL parsers have been created and several ontology platforms are able to import ontologies implemented in such languages. In this paper we present two experiments. The first one reveals that most RDF(S), DAML+OIL, and OWL parsers studied do not detect taxonomic problems, from a knowledge representation point of view, in ontologies implemented in such languages. So, if such ontologies are imported by ontology platforms, the question is: are they able to detect such problems? The second experiment presented in this paper reveals that most ontology platforms analyzed only detect a few of problems in concept taxonomies during ontology import

Building Ontology Networks: How to Obtain a Particular Ontology Network Life Cycle?

To build an ontology, ontology developers should devise first a concrete plan for the ontology development, that is, they should establish the ontology life cycle. To do this, ontology developers should answer two key questions: a) which ontology life cycle model is the most appropriate for their ontology project? and b) which particular activities should be carried out in their ontology life cycle? In this paper we present a set of guidelines to help ontology developers and also naïve users answer such questions

Towards a Glossary of Activities in the Ontology Engineering Field

The Semantic Web of the future will be characterized by using a very large number of ontologies embedded in ontology networks. It is important to provide strong methodological support for collaborative and context-sensitive development of networks of ontologies. This methodological support includes the identification and definition of which activities should be carried out when ontology networks are collaboratively built. In this paper we present the consensus reaching process followed within the NeOn consortium for the identification and definition of the activities involved in the ontology network development process. The consensus reaching process here presented produces as a result the NeOn Glossary of Activities. This work was conceived due to the lack of standardization in the Ontology Engineering terminology, which clearly contrasts with the Software Engineering field. Our future aim is to standardize the NeOn Glossary of Activities