# BVTED: A Specialized Bilingual (Chinese-English) Dataset for Vulnerability Triple Extraction Tasks
## 3 Materials and Methods
### Statistics of Entities and Relations  
As mentioned in the proposed ontology, we use 13 kinds of entity types and 15 kinds of relationships to describe the vulnerability knowledge in CNNVD. Table 1 shows the details of each kind of entity type. The 13 entity types in this dataset are Product, Definition, Vendor, Location, Vulnerability, Version, Part, Consequence, Attacker, Method Reason, Condition and Time. The columns of Table 1 from the left to the right correspond to the following: the Chinese entity type, the English entity type and entity meaning, the abbreviation for the English entity type, the Chinese example for the corresponding entity, the English example for the corresponding entity. We use industrial standard BIO text labeling mode to label the CNNVD vulnerability text. We combine the \"B-\" with the Chinese entity type or with the abbreviation for the English entity type as the entity start tag, such as \"B-Product\" or \"B-P\". Similarly, the other inside entity tags are combined with \"I-\" and the Chinese entity type or with the abbreviation for the English entity type, such as \"I-Product\" or \"I-P\". In this dataset, we count and rank the frequency of each entity type and show them in Figure 5. In our dataset, there are a total of 97,391 entities, with an average of 3.5 entities in every sentence. As shown in Figure 5, the three most numerous entity types in our dataset are \"Product\", \"Definition\" and \"Vendor\" (32,328, 19,887 and 13,279, respectively), indicating that, on average, each sentence contains 1.2 \"Product\" entities. \"Reason\", \"Condition\" and \"Time\" are the minimum three entity types, and the frequency of these  
Figure 4: Data transfer for annotation. (a) Json format annotation for Chinese vulnerability sentence. (b) CoNLL format annotation for Chinese vulnerability sentence. is 318, 156 and 82, respectively. It is worth mentioning that there is a data imbalance where the number of the last six kinds of entities is less than 1200. As a result, some data augmentation technologies need to be applied to alleviate this data imbalance problem during the model training stage. In this section, 15 kinds of entity relationships will be introduced to describe the organization of the vulnerability entities. Although the ontology in Figure 1 shows 26 entity relationships, there are 15 non-repetitive entity relationship types since the head entity and tail entity of the same relationship can be different. They are chosen because these 15 relationships are those mostly described in the CNNVD vulnerability textual description, and other relationships are rarely involved. In order to facilitate the understanding of the relations, Table 2 presents the details of each relationship. Figure 6 is a frequency bar chart of the relationships in the dataset based on frequency ordering. We counted 69,614 relationships from 27,311 non-repeated sentences in our dataset, which means there are 2.55 triples in one sentence on average. As shown in this figure, the number of each of the last six relationships is less than 1000, which may become an important factor affecting the training and extraction effects of these relations. Nonetheless, these relationships are still non-negligible components in describing the relationship between the vulnerability-related entities. As mentioned in the entity annotation work, data augmentation techniques are also needed in the training phase for these types of relationships.