Shen Ying, Chen Daoyuan, Tang Buzhou, Yang Min, Lei Kai
Shenzhen Key Lab for Information Centric Networking & Blockchain Technology (ICNLAB), School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, 518055, Shenzhen, People's Republic of China.
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), 518055, Shenzhen, People's Republic of China.
J Biomed Semantics. 2018 Aug 10;9(1):20. doi: 10.1186/s13326-018-0188-7.
Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.
We propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.
We leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate ).
熵在计算机科学和信息论中越来越受欢迎，因为它可用于衡量知识库（尤其是本体）的可预测性和冗余性。然而，当前评估本体的熵应用仅考虑单点连通性而非路径连通性，并且它们为每个实体和路径赋予相等的权重。
我们提出了一种基于熵感知路径的（EAPB）本体质量度量方法，通过考虑不同顶点之间的路径信息以及路径中包含的文本信息来计算整个网络的连通性路径和不同节点之间的动态权重。从基于结构的嵌入和基于文本的嵌入中获得的信息与熵计算的连通性矩阵相乘。EAPB根据最新标准进行了分析评估。我们基于以下三个方面对真实世界的医学本体和一个合成本体进行了实证分析：本体统计信息（数据量）、熵评估（数据质量）和案例研究（本体结构和文本可视化）。这些方面相互证明了所提出度量方法的可靠性。实验结果表明，所提出的EAPB能够有效地评估本体，尤其是医学信息学领域的本体。
我们利用路径信息和文本信息来丰富网络表示学习并辅助熵计算。语义网的分析和评估不仅可以受益于结构信息，还可以受益于文本信息。我们相信EAPB有助于管理本体开发和评估项目。我们的结果是可重现的，并且在发表后将发布这项工作的源代码和本体。（源代码和本体：https://github.com/AnonymousResearcher1/ontologyEvaluate ）
