105,367 research outputs found

    Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes.

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    In the context of the classification task of data mining or machine learning, hierarchical feature selection methods exploit hierarchical relationships among features in order to select a subset of features without hierarchical redundancy. Hierarchical feature selection is a new research area in classification research, since nearly all feature selection methods ignore hierarchical relationships among features. This paper proposes two methods for constructing a network of features to be used by a Bayesian Network Augmented NaĂŻve Bayes (BAN) classifier, in datasets of aging-related genes where Gene Ontology (GO) terms are used as hierarchically related predictive features. One of the BAN network construction method relies on a hierarchical feature selection method to detect and remove hierarchical redundancies among features (GO terms); whilst the other BAN network construction method simply uses a conventional, flat feature selection method to select features, without removing the hierarchical redundancies associated with the GO. Both BAN network construction methods may create new edges among nodes (features) in the BAN network that did not exist in the original GO DAG (Directed Acyclic Graph), in order to preserve the generalization-specialization (ancestor-descendant) relationship among selected features. Experiments comparing these two BAN network construction methods, when using two different hierarchical feature selection methods and one at feature selection method, have shown that the best results are obtained by the BAN network construction method using one type of hierarchical feature selection method, i.e., select Hierarchical Information-Preserving features (HIP)

    Hierarchy Theory of Evolution and the Extended Evolutionary Synthesis: Some Epistemic Bridges, Some Conceptual Rifts

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    Contemporary evolutionary biology comprises a plural landscape of multiple co-existent conceptual frameworks and strenuous voices that disagree on the nature and scope of evolutionary theory. Since the mid-eighties, some of these conceptual frameworks have denounced the ontologies of the Modern Synthesis and of the updated Standard Theory of Evolution as unfinished or even flawed. In this paper, we analyze and compare two of those conceptual frameworks, namely Niles Eldredge’s Hierarchy Theory of Evolution (with its extended ontology of evolutionary entities) and the Extended Evolutionary Synthesis (with its proposal of an extended ontology of evolutionary processes), in an attempt to map some epistemic bridges (e.g. compatible views of causation; niche construction) and some conceptual rifts (e.g. extra-genetic inheritance; different perspectives on macroevolution; contrasting standpoints held in the “externalism–internalism” debate) that exist between them. This paper seeks to encourage theoretical, philosophical and historiographical discussions about pluralism or the possible unification of contemporary evolutionary biology

    Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

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    In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system

    Improving automation standards via semantic modelling: Application to ISA88

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    Standardization is essential for automation. Extensibility, scalability, and reusability are important features for automation software that rely in the efficient modelling of the addressed systems. The work presented here is from the ongoing development of a methodology for semi-automatic ontology construction methodology from technical documents. The main aim of this work is to systematically check the consistency of technical documents and support the improvement of technical document consistency. The formalization of conceptual models and the subsequent writing of technical standards are simultaneously analyzed, and guidelines proposed for application to future technical standards. Three paradigms are discussed for the development of domain ontologies from technical documents, starting from the current state of the art, continuing with the intermediate method presented and used in this paper, and ending with the suggested paradigm for the future. The ISA88 Standard is taken as a representative case study. Linguistic techniques from the semi-automatic ontology construction methodology is applied to the ISA88 Standard and different modelling and standardization aspects that are worth sharing with the automation community is addressed. This study discusses different paradigms for developing and sharing conceptual models for the subsequent development of automation software, along with presenting the systematic consistency checking methodPeer ReviewedPostprint (author's final draft
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