# BVTED: A Specialized Bilingual (Chinese-English) Dataset for Vulnerability Triple Extraction Tasks
###### Abstract
Common implementation methods include the pipeline method and the joint extraction method. For the field of cybersecurity, in addition to designing an effective information extraction model, it is also necessary to have enough field annotation data as a training corpus to train or fine-tune the model. CTI, usually compiled by experts in the field of cybersecurity, may be a barrier to the general reader because of the expertise needed. In addition, due to individual differences in writing habits, language use and analytical focus, the intelligence content shows significant diversity in consistency and accuracy. Due to the complexity of entities in the field of cybersecurity, the task of extracting cybersecurity knowledge from various channels requires an ontology model that can effectively organize cybersecurity entities and relations as a guide. The ontology can accurately cover the information of the domain entity type, entity relation type and so on. Extracting information from a corpus presents three main challenges. First, in the field of Chinese cybersecurity, there is no open dataset that satisfies both entity and relation extraction tasks [5]. Existing datasets are either not open or only support general NER tasks, and do not adequately support NER, relation extraction, or joint entity-relation extraction tasks in cybersecurity [6]. Second, the structure of network security text data is complex, with many descriptions mixing Chinese and English [7]. This includes software and vulnerabilities that have both Chinese and English names, and the extensive use of nested structures, abbreviations and obscure terms, which significantly increases the difficulty of entity and relationship extraction. Finally, the Chinese CTI corpus is more complicated than the English CTI corpus, adding another layer of difficulty to the extraction process [8]. To address the aforementioned challenges, we present the Bilingual (Chinese-English) Vulnerability Triple Extraction Dataset (BVTED), the first known dataset capable of supporting Chinese cybersecurity entity-relation triple extraction tasks. We develop an ontology model for describing cybersecurity intelligence knowledge, encompassing all relevant entity and relation types necessary for knowledge extraction. Given the dataset's char acteristics, which include a substantial amount of mixed Chinese and English texts, we trained five deep learning-based named entity recognition models, two standalone relation extraction models, and two joint entity-relation extraction models. The experimental results validate the effectiveness of the BVTED dataset, demonstrating its potential for advancing research in cybersecurity intelligence. This work provides a significant scientific contribution by addressing the lack of open, comprehensive datasets in Chinese cybersecurity knowledge triple extraction tasks. By offering a robust dataset and demonstrating its utility through extensive experiments, we enhance the ability to mine, analyze and utilize cybersecurity knowledge, thereby advancing the field of cyber threat intelligence. The remainder of this paper is organized as follows:  
Section 2 reviews related work, including the existing datasets and information extraction techniques in the cybersecurity domain; Section 3 introduces the construction, annotation and statistical analysis of the BVTED dataset; Section 4 introduces the experimental settings and evaluation metrics in this paper and evaluates the final experimental results; Section 5 summarizes the contributions and limitations of our research; Section 6 provides a detailed outline of our future research directions.