# An Open Relation Extraction System for Web Text Information
## 1 Introduction  
Information and knowledge are the basis for the development of human society. Text records 80 ([[https://breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/](https://breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/)]([https://breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/](https://breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/)), accessed on 5 May 2022) percent of the information of human civilization. The core task of information extraction (IE) is to obtain structured triples from unstructured text. It relies on two fundamental tasks: entity recognition and relation extraction. [PERSON] et al. [1] proposed an entity recognition method that performs well. For relation extraction, new relation prediction is a challenge. Traditional relation extraction mainly adopts supervised learning methods for predefined relations. Its essence is to transform relation extraction into relation classification. There are two paradigms: pipeline relation extraction [2] and joint relation extraction [3]. Traditional RE performs well but faces two challenges. The first challenge is that predefined relation classifications do not work well on new relation extraction tasks. The second challenge is that relational data relies too much on manual cleaning and labeling, which is costly. In addition, for large-scale knowledge bases such as Wikidata, manual annotation would be challenging to accomplish. To solve this problem, [PERSON] [4] first proposed the concept of open information extraction. That is, extracting structured relational facts from open and growing unstructured text. Information extraction should not be limited to a small set of known relations. RE should be able to extract a wide variety of relations in a text. The scope of its research is that the entity pair of the relation is known, and the relationship type between the entity pair is unlimited. Open-domain relation extraction should meet three academic requirements: automation, non-homologous corpus, and high efficiency. * Automation The open relation extraction system can execute automatically, and the algorithm only needs to go through the corpus once for triple tuples extraction. It should be based on an unsupervised extraction strategy and cannot be a predefined relation. In addition, the cost of manually constructing training samples is small, and only a tiny number of initialization seeds need to be labeled or a small number of extraction templates need to be defined. * Non-homologous corpus The goal of open-domain relation extraction is domain-independent and should not be limited to a particular domain. In addition, it is important to avoid using domain-dependent deep syntactic analysis tools, such as syntactic analysis. * Efficiency Open information extraction systems handle large-scale corpora, and a high efficiency is required to ensure a rapid response. Prioritizing shallow syntactic features is also necessary. The open-domain relation extraction methodology mainly includes four methods: based learning, clause, rule, and tuple association. OLIIE [5] is a knowledge base based on the open information extraction system REVERB [6]. Its idea is to learn a pattern to extract relations, which requires high generalization and robustness of the pattern. ReNoun [7] is an open information extraction system that entirely focuses on noun relation extraction. In this system, a high-precision lexical template is first formulated manually. Then, based on the template, the little relation tuples are mined, and the confidence is marked. This work introduces a distant supervision method that reduces the reliance on labeled data. Many complex sentences have relations associated with multiple clauses, and the model has a low performance when dealing with complex sentences.