Hypothesis generation and data quality assessment through association mining

Chen, Ping; García, Walter Baluja

doi:10.1109/coginf.2010.5599828

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…Based on this definition, we also considered the problems of spurious annotation and inaccurate annotation (inaccurate labeling and inaccurate classification) identified in Lei et al [40] related to the semantic accuracy dimension. The other articles [1,8,10,14,17,36,50,65] provide metrics for this dimension.…”

Section: Semantic Accuracymentioning

confidence: 99%

“…of instances * balanced distance metric total no. of instances 17 [40] -SA4: detection of misuse of properties 18 by using profiling statistics, which support the detection of discordant values or misused properties and facilitate to find valid values for specific properties [10] -SA5: ratio of the number of semantically valid rules 19 to the number of nontrivial rules [14] Example. Let us assume that the ID of the flight between Paris and New York is A123, while in our search engine the same flight instance is represented as A231 (possibly manually introduced by a data acquisition error).…”

Section: Semantic Accuracymentioning

confidence: 99%

“…Table 7 shows the complete list of 18 LD quality dimensions along with their respective occurrence in the included approaches. This table can be intuitively divided into the following three groups: (i) a set of ap-proaches focusing only on trust [11,15,20,21,22,23,24,27,32,59]; (ii) a set of approaches covering more than four dimensions [8,17,18,19,30,31,36,45,65] and (iii) a set of approaches focusing on very few and specific dimensions [1,10,14,25,30,40,48,50,53,55,64].…”

Section: Dimensionsmentioning

confidence: 99%

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Quality assessment for Linked Data: A Survey

et al. 2015

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The development and standardization of semantic web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.

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Section: Semantic Accuracymentioning

confidence: 99%

Section: Semantic Accuracymentioning

confidence: 99%

Section: Dimensionsmentioning

confidence: 99%

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Quality assessment for Linked Data: A Survey

et al. 2015

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“…With concepts semantically organized and correlated in a semantic network, the intuition for generating hypotheses is that if two concepts are associated, maybe their semantically connected neighbors (children and siblings) are also associated. We have the following hypothesis generation methods (Chen et al 2010), with u. Induction is useful when the direct observation of v is difficult or impossible when v is an abstract concept.…”

Section: Hypothesis Generationmentioning

confidence: 99%

Adaptive Study Design Through Semantic Association Rule Analysis

Chen

Ding

García

2011

International Journal of Software Science and Computational Intelligence

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Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper presents a semantic network-based association analysis model including three spreading activation methods. It applies this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design especially useful in medical studies. The approach is evaluated on a real public health dataset, the Heartfelt study, and the experiment shows promising results.

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Section: Hypothesis Generationmentioning

confidence: 99%

Adaptive Study Design Through Semantic Association Rule Analysis

Chen

Ding

García

2013

Advances in Abstract Intelligence and Soft Computing

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Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. In this paper we present a semantic network based association analysis model including three spreading activation methods, and apply this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design that is especially useful in medical studies. We evaluate our approach on a real public health dataset, the Heartfelt study, and the experiment shows promising results.

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Hypothesis generation and data quality assessment through association mining

Cited by 5 publications

References 15 publications

Quality assessment for Linked Data: A Survey

Quality assessment for Linked Data: A Survey

Adaptive Study Design Through Semantic Association Rule Analysis

Adaptive Study Design Through Semantic Association Rule Analysis

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