摘要
【目的】从大规模的甲骨文基础数据中发现实体间的语义关联,为甲骨文研究提供语义支持。【方法】在文本挖掘的基础上,结合语义Web技术,将实体及其关系RDF化并在生成的RDF集合中进行语义搜索,利用本体关系和本体推理挖掘RDF对象间显式或隐式的语义关系。【结果】该方法在甲骨文文献和甲骨卜辞上的语义挖掘平均F1值分别达到74.49%和70.61%,满足甲骨文信息处理的需求。【局限】利用本体实现语义挖掘时分别基于本体库中的三个不同本体,未将本体进行集成。【结论】实体RDF化可以提供规范的结构化语义描述;Lar KC体系适用于甲骨文大规模语义处理。
[Objective] Find the semantic relations among large-scale Oracle Bone Inscription （OBI） data in order to provide semantic analysis function for OBI research. [Methods] Based on text mining, combined with the semantic Web technology, implement semantic search on the data set of RDF-based entities and their relationships. And using Ontology relationships and Ontology reasoning to extract explicit or implicit semantic relationships among RDF objects [Results] Experimental results show that the F-Measure can reach 74.49% on OBI literature semantic mining and 70.61% on OBI semantic mining, which satisfy the need of OBI information processing. [Limitations] Semantic mining is based on three different Ontologies instead of an integrated one. [Conclusions] RDF can provide a structured semantic specification description and the LarKC system is suitable for large-scale OBI semantic processing.
出处
《现代图书情报技术》
CSSCI
2015年第2期7-14,共8页
New Technology of Library and Information Service
基金
国家自然科学基金项目"基于甲骨文语料库的计算机辅助考释技术研究"(项目编号:60875081)
河南省教育厅科技重点项目"基于本体和实例的甲骨文机器翻译关键技术研究"(项目编号:14A520038)的研究成果之一
关键词
甲骨文信息处理
本体
语义挖掘
LarKC
Oracle Bone Inscriptions information processing
Ontology
Semantic mining
LarKC
