Statistical algorithms for ontology-based annotation of scientific literature.
Statistical algorithms for ontology-based annotation of scientific literature.
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DOI：
10.1186/2041-1480-5-s1-s2
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发表时间：
2014
影响因子：
1.9
通讯作者：
Turner JA
中科院分区：
文献类型：
--
作者：
Chakrabarti C;Jones TB;Luger GF;Xu JF;Turner MD;Laird AR;Turner JA
本体将域中的关系编码为健壮的数据结构，可用于注释数据对象，包括科学论文，以简化搜索和元分析等任务。然而，注释过程在由人类执行时需要大量的时间和精力。文本挖掘算法可以促进这一过程，但它们主要基于关键字，同义词和语义匹配进行分析。它们不利用本体结构中嵌入的信息。我们提出了一个概率框架，有利于自动标注的文献间接建模的本体中不同类之间的限制。我们的研究重点是在认知范式本体（CogPO）中注释人类功能神经影像学文献。我们使用一种方法，结合了朴素贝叶斯的随机简单性和决策树的形式透明性。我们的数据结构很容易修改，以反映不断变化的领域知识。我们比较了朴素贝叶斯，贝叶斯决策树和约束决策树分类器的结果，这些分类器在F1-micco分数的质量度量方面保持了人类专家的循环。与传统的文本挖掘算法不同，我们的框架可以对本体中依赖关系编码的知识进行建模，尽管是间接的。我们成功地利用了这样一个事实，即CogPO明确规定了限制，并在专家策划的注释模式的形式隐式依赖。
Ontologies encode relationships within a domain in robust data structures that can be used to annotate data objects, including scientific papers, in ways that ease tasks such as search and meta-analysis. However, the annotation process requires significant time and effort when performed by humans. Text mining algorithms can facilitate this process, but they render an analysis mainly based upon keyword, synonym and semantic matching. They do not leverage information embedded in an ontology's structure. We present a probabilistic framework that facilitates the automatic annotation of literature by indirectly modeling the restrictions among the different classes in the ontology. Our research focuses on annotating human functional neuroimaging literature within the Cognitive Paradigm Ontology (CogPO). We use an approach that combines the stochastic simplicity of naïve Bayes with the formal transparency of decision trees. Our data structure is easily modifiable to reflect changing domain knowledge. We compare our results across naïve Bayes, Bayesian Decision Trees, and Constrained Decision Tree classifiers that keep a human expert in the loop, in terms of the quality measure of the F1-mirco score. Unlike traditional text mining algorithms, our framework can model the knowledge encoded by the dependencies in an ontology, albeit indirectly. We successfully exploit the fact that CogPO has explicitly stated restrictions, and implicit dependencies in the form of patterns in the expert curated annotations.