175,498 research outputs found

    Annotation-based feature extraction from sets of SBML models

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    Background: Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. Results: In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Conclusions: Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison

    Ontology, a Mediator for Agent-Based Modeling in Social Science

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    Agent-Based Models are useful to describe and understand social, economic and spatial systems\' dynamics. But, beside the facilities which this methodology offers, evaluation and comparison of simulation models are sometimes problematic. A rigorous conceptual frame needs to be developed. This is in order to ensure the coherence in the chain linking at the one extreme the scientist\'s hypotheses about the modeled phenomenon and at the other the structure of rules in the computer program. This also systematizes the model design from the thematician conceptual framework as well. The aim is to reflect upon the role that a well defined ontology, based on the crossing of the philosophical and the computer science insights, can play to solve such questions and help the model building. We analyze different conceptions of ontology, introduce the \'ontological test\' and show its usefulness to compare models. Then we focus on the model building and show the place of a systematic ABM ontology. The latter process is situated within a larger framework called the \'knowledge framework\' in which not only the ontologies but also the notions of theory, model and empirical data take place. At last the relation between emergence and ontology is discussed.Ontology, Agent-Based Computational Economic, Agent-Based Model of Simulation, Model Design, Model Building, Knowledge Framework, Spatial Simulation, Social Simulation, Ontological Test

    From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data

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    We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of "pre-canned" queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice

    Towards a better labeling process for network security datasets

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    Most network security datasets do not have comprehensive label assignment criteria, hindering the evaluation of the datasets, the training of models, the results obtained, the comparison with other methods, and the evaluation in real-life scenarios. There is no labeling ontology nor tools to help assign the labels, resulting in most analyzed datasets assigning labels in files or directory names. This paper addresses the problem of having a better labeling process by (i) reviewing the needs of stakeholders of the datasets, from creators to model users, (ii) presenting a new ontology of label assignment, (iii) presenting a new tool for assigning structured labels for Zeek network flows based on the ontology, and (iv) studying the differences between generating labels and consuming labels in real-life scenarios. We conclude that a process for structured label assignment is paramount for advancing research in network security and that the new ontology-based label assignation rules should be published as an artifact of every dataset

    Ontology-based Why-Question Analysis Using Lexico-Syntactic Patterns

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    This research focuses on developing a method to analyze why-questions.  Some previous researches on the why-question analysis usually used the morphological and the syntactical approach without considering the expected answer types. Moreover, they rarely involved domain ontology to capture the semantic or conceptualization of the content. Consequently, some semantic mismatches occurred and then resulting not appropriate answers. The proposed method considers the expected answer types and involves domain ontology. It adapts the simple, the bag-of-words like model, by using semantic entities (i.e., concepts/entities and relations) instead of words to represent a query. The proposed method expands the question by adding the additional semantic entities got by executing the constructed SPARQL query of the why-question over the domain ontology. The major contribution of this research is in developing an ontology-based why-question analysis method by considering the expected answer types. Some experiments have been conducted to evaluate each phase of the proposed method. The results show good performance for all performance measures used (i.e., precision, recall, undergeneration, and overgeneration). Furthermore, comparison against two baseline methods, the keyword-based ones (i.e., the term-based and the phrase-based method), shows that the proposed method obtained better performance results in terms of MRR and P@10 values

    Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web

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    Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions

    An agent-based implementation of hidden Markov models for gas turbine condition monitoring

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    This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner
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