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OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

机译：ontoilper：基于本体和归纳逻辑编程的系统，用于从文本中提取实体和关系

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Named entity recognition (NER) and relation extraction (RE) are two important subtasks in information extraction (IE). Most of the current learning methods for NER and RE rely on supervised machine learning techniques with more accurate results for NER than RE. This paper presents OntoILPER a system for extracting entity and relation instances from unstructured texts using ontology and inductive logic programming, a symbolic machine learning technique. OntoILPER uses the domain ontology and takes advantage of a higher expressive relational hypothesis space for representing examples whose structure is relevant to IE. It induces extraction rules that subsume examples of entities and relation instances from a specific graph-based model of sentence representation. Furthermore, OntoILPER enables the exploitation of the domain ontology and further background knowledge in the form of relational features. To evaluate OntoILPER, several experiments over the TREC corpus for both NER and RE tasks were conducted and the yielded results demonstrate its effectiveness in both tasks. This paper also provides a comparative assessment among OntoILPER and other NER and RE systems, showing that OntoILPER is very competitive on NER and outperforms the selected systems on RE.
机译：命名实体识别（ner）和关系提取（重新）是信息提取中的两个重要子任务（即）。大多数新的学习方法，并重新依赖于监督机器学习技术，比重新获得更准确的结果。本文介绍了使用本体和电感逻辑编程从非结构化文本提取实体和关系实例的系统，这是一种符号机器学习技术。 ontoilper使用域本体学，并利用更高的表达关系假设空间，用于表示其结构与IE相关的示例。它引起了从基于图形表示的特定图形模型的实体和关系实例的区别的提取规则。此外，onToilper以关系特征的形式开发域本体论和进一步的背景知识。为了评估intoilper，进行了对TREC和RE任务的TREC语料库的几个实验，并且所产生的结果在两个任务中展示了其有效性。本文还提供了欠电机和其他网德和RE系统中的比较评估，显示unitoilper对ner非常竞争，并且优越在RE上的所选系统。

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