Quality evaluation is a fundamental problem in the field of linguistic description of data. In this work, we analyze the concept of quality and study different approaches to measure quality. Although most of the appro...
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ISBN:
(纸本)9781467374293
Quality evaluation is a fundamental problem in the field of linguistic description of data. In this work, we analyze the concept of quality and study different approaches to measure quality. Although most of the approaches considered focused on time series data, that are one of the most frequent datasets in real application domains, they can be used for quality assessment of linguistic descriptions generated for any type of data.
In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influenti...
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ISBN:
(纸本)9781467383967
In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter. According to our knowledge this is the first work that combines social analytics with the Linked Open Data (LOD) cloud.
Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. A key factor in the practical success of fuzzy DLs is the availability of highly impl...
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Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. A key factor in the practical success of fuzzy DLs is the availability of highly implemented reasoners. This paper studies two optimisation techniques (ABox partitioning based on individual groups and optimisation problem partitioning) in the setting of the fuzzy ontology reasoner fuzzy DL. We study the applicability of these techniques in expressive fuzzy DL languages, proposing a new strategy, and perform an empirical evaluation proving that they are not helpful in practice so far.
Comprehending software clones is necessary for a number of activities in software development. The comprehension of software clones is challenged by the sheer volume of data and the complexity of the information conte...
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Comprehending software clones is necessary for a number of activities in software development. The comprehension of software clones is challenged by the sheer volume of data and the complexity of the information content in that data. Visualization, or visual data analysis, takes advantage of human cognitive skills to discover unstructured insights from the visual presentations of complex and voluminous data. In this paper, we survey the existing literature on visualization of software clones. We gather the insights provided, and put that information in context of actual information needs systematically derived from the clone management goals. This framework allows us to better understand the role a visualization may play in achieving a specific user goal, identify potential gaps between existing types of visualization and information needs, and find complementary non-redundant subsets of visualizations for each user goal.
In this paper, we introduce a new Gaussian Process (GP) classification method for multisensory data. The proposed approach can deal with noisy and missing data. It is also capable of estimating the contribution of eac...
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In this paper, we introduce a new Gaussian Process (GP) classification method for multisensory data. The proposed approach can deal with noisy and missing data. It is also capable of estimating the contribution of each sensor towards the classification task. We use Bayesian modeling to build a GP-based classifier which combines the information provided by all sensors and approximates the posterior distribution of the GP using variational Bayesian inference. During its training phase, the algorithm estimates each sensor's weight and then uses this information to assign a label to each new sample. In the experimental section, we evaluate the classiication performance of the proposed method on both synthetic and real data and show its applicability to different scenarios.
The goal of this paper is to bridge the paradigms of Linked Data and Conceptual Modeling, which have been developed from quite distinct concerns, although certain opportunities may stimulate the evolution of the Web t...
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The goal of this paper is to bridge the paradigms of Linked Data and Conceptual Modeling, which have been developed from quite distinct concerns, although certain opportunities may stimulate the evolution of the Web towards a new type of knowledge space driven by diagrammatic models. To this end, the work at hand investigates structural patterns in a multitude of modeling languages accumulated over time within the Open Model Initiative Laboratory and defines for each pattern transformation rules that produce graph-based model serializations, thus enabling the processing of diagrammatic models using the Web of Data tech-nological space and practices.
To overcome the inability of Description Logics (DLs) to represent vague or imprecise information, several fuzzy extensions have been proposed in the literature. In this context, an important family of reasoning algor...
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To overcome the inability of Description Logics (DLs) to represent vague or imprecise information, several fuzzy extensions have been proposed in the literature. In this context, an important family of reasoning algorithms for fuzzy DLs is based on a combination of tableau algorithms and Operational Research (OR) problems, specifically using Mixed Integer Linear Programming (MILP). In this paper, we present a MILP-based tableau procedure that allows to reason within fuzzy ALCB, i.e., ALC with individual value restrictions. Interestingly, unlike classical tableau procedures, our tableau algorithm is deterministic, in the sense that it defers the inherent non-determinism in ALCB to a MILP solver.
The Business Process Modelling and Notation (BPMN) is a widely-accepted standard for process modelling, which can be used to model the clinical processes contained in guidelines. computer systems based on guidelines n...
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The Business Process Modelling and Notation (BPMN) is a widely-accepted standard for process modelling, which can be used to model the clinical processes contained in guidelines. computer systems based on guidelines need to embed these clinical processes, e.g. using a computer-Interpretable Guideline (CIG) language. However, encoding guidelines in a CIG language is a difficult task which is usually performed by technical staff. Building on our previous work, the transformation-based refinement of guideline models, in this paper we describe an algorithm to transform BPMN models into the SDA CIG language. The use of BPMN has the potential to empower clinicians in the modelling task. In combination with the transformation algorithm, this can lead to an increased adoption of CIG languages, SDA and others.
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining ...
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ISBN:
(纸本)9781450328630
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining is often only interested in one of the two types e.g. in natural language text when looking at text mining. Regardless of which type is being investigated, any techniques used have to deal with noise caused by fragments of the other type i.e. methods interested in natural language have to deal with technical fragments and vice versa. This paper proposes an approach to classify unstructured data, e.g. development documents, into natural language text and technical information using a mixture of text heuristics and agglomerative hierarchical clustering. The approach was evaluated using 225 manually annotated text passages from developer emails and issue tracker data. Using white space tokenization as a basis, the overall precision of the approach is 0.84 and the recall is 0.85.
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