# An Open Relation Extraction System for Web Text Information
## 1 Introduction
To improve the accuracy, [PERSON] [8] proposed a clause-based system, ClausIE, which converts the dependencies of input sentences into simple coherent clauses based on linguistic knowledge. Then, the argument information is extracted according to the clause type. The Stanford open information extractor was proposed by [PERSON] et al. [9]. In addition, the rule-based method is more effective in improving accuracy. [PERSON] [10] proposed a non-lexicalization rule to convert the dependency syntax tree into a relational directed graph for processing. It works well on multilingual tasks. Rule-based methods are more accurate for specific tasks but rely on human rules and are more complex. In order to improve the accuracy of extracting triples, a method based on tuple association has also been proposed to solve the problem of sentence association. The method reconstructs sentences, transforming a complex non-normalized sentence segment into a set of clauses with dependencies and easy-to-extract triples. [PERSON] et al. [11] proposed a method for the hierarchical simplification of complex sentences from the perspective of obtaining tuple associations and used syntactic and lexical patterns to predict the modification relationship between sentences. The process is sentence reconstruction, clause splitting, information reduction, and clause triple extraction. This method is beneficial in improving accuracy. The method based on rules, clauses, and obtaining tuple associations uses templates to extract relational tuples from a syntactic and lexical point of view. Learning-based methods learn a pattern from semantic information and use the learned pattern to extract tuples. At present, rule-based methods can achieve a high accuracy. Furthermore, learning-based methods such as Neural Snowball are effective, and form the baseline for new relation extraction tasks. This paper uses the Neural Snowball [12] as the open-domain RE baseline model for subsequent experiments. Most of these open-domain relation extraction methods only utilize a small amount of supervised data and do not effectively utilize the newly growing relational data. Most of the current open relation extraction methods only utilize a small amount of supervised data and do not utilize the setting of newly growing relational data. However, these new relational data can predict unseen relations, which is valuable. Based on the Bootstrap [13] algorithm, this paper comprehensively utilizes small-scale labeled data, large-scale unsupervised data, and few-sample new relational data. We propose a system that can realize data self-labeling and new relation extraction, ORES. Figure 1 shows the process of how ORES utilizes different kinds of data to learn new relations. After distant supervision obtains sentences, the model based on the self-sampling algorithm screens instances. Furthermore, a new-relation classifier is trained by inputting filtered high-confidence instances and large-scale supervised data. The classifier can iteratively discover more reliable instances with new relation facts and adapt them to open growth scenarios. In more detail, we design sample selector (SS), based on a Siamese network (SN) [14] to select high-confidence instances. The SN is used to classify whether existing instances and new ones express the same relation. The experimental results show that ORES can select high-quality instances and significantly improve extracting new relations through few-shot samples. In conclusion, our main contributions are as follows:  
* We propose a new open relation extraction system (ORES), a novel structure to train a neural relation classifier with few initial new relation instances by iteratively accumulating new instances and facts from unlabeled data with prior data knowledge of historical relations. We design three functional components of ORES to realize the mining of Web text information, automatic labeling, and extraction and output of new relation data.