# BVTED: A Specialized Bilingual (Chinese-English) Dataset for Vulnerability Triple Extraction Tasks
###### Abstract  
Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset's value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity-relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence. cyber threat intelligence; information extraction; named entity recognition; relation extraction; Chinese-English hybrid vulnerability descriptions 202  
reports, and transform it into a standardized and structured knowledge organization form, has been of great significance and practical application value for cybersecurity research. As one of the most active research hotspots in the field of cybersecurity, the task of network threat intelligence knowledge extraction is to identify the security entities in the text by analyzing and learning the context information and classifying the semantic relations contained in it [4]. This task aims to extract knowledge from different sources, including different structural data, and deposit it in a knowledge graph. In the knowledge graph, knowledge is generally composed of the form of the entity relation triples \(\langle e_{1},r_{1,2},e_{2}\rangle\), where \(e_{1}\) represents the head entity and \(e_{2}\) is the tail entity, and \(r_{1,2}\) is the relation between the two. Data sources can be structured data (linked data, databases), semi-structured data (tables and lists in the web page), unstructured data (pure text data), etc. This task mainly includes two parts: named entity recognition (NER) and relation extraction (RE). The former is used to identify the boundaries of the entities and classify them into a predefined category, while the latter is used to identify whether there is a certain predefined relation type between the entities in the input text. An important task of CTI knowledge extraction is to identify important entities involved in the threat, such as attackers, network products, vulnerabilities, etc. At present, the more commonly used entity extraction method is based on the sequence prediction model, that is, to obtain the optimal annotated sequence for the input text sequence according to the context semantics. Entity extraction concerns the explicit knowledge of the intelligence text, the implicit semantic relation in the sentence and the interaction between the entities. The goal is to extract possible correlations between the entities, such as an attack organization vulnerability \"exploit\" relation, an attack organization \"Use\" relation of malicious software, a software and hardware product version of the \"has_version\" relation, etc. According to the different datasets used, it can be divided into three categories: template-based relation extraction, relation extraction based on supervised learning and relation extraction based on weak supervised learning.