# Automated Construction Method of Knowledge Graphs for Pirate Events
## 1 Introduction  
Maritime shipping is the most important mode of international transportation in the world today and plays a vital role in the development of world trade and economies [1]. However, non-traditional security risks, mainly piracy and maritime terrorism, have become the main issues affecting maritime shipping security. Among them, pirate attacks have the widest impact. Once such attacks occur, they will not only cause property losses but also pose a great threat to the lives of the people on board and may even have an adverse impact on navigation and the environment [2]. Therefore, constructing a situational awareness model for pirate events using available information is particularly important in the early warning of pirate events and maritime crime cracking [3].  
Much of the current research focuses on automated processing, fusing, and reasoning from different data sources in maritime surveillance to assist official decision making. Using only the physical movement data from traditional physical sensors (i.e., hard data) makes full-scale situational awareness difficult [4]. Thus, soft data need to be incorporated into maritime threat situational awareness models. Here, soft data refer to structured text data, semi-structured and unstructured news, social media data, maritime incident reports, andso on [5; 6; 7; 8; 9; 10]. These soft data include structured text data, semi-structured and unstructured news, social media data, maritime incident reports, and other relevant sources. In response, this paper presents methods that can extract such knowledge and construct knowledge graphs for pirate events, which are conducive to subsequent maritime threat reasoning for maritime threat situational awareness in general.  
This paper presents an in-depth study on how to construct knowledge graphs for pirate events, and its contributions can be summarized as follows:  
1. A Pirate Event Model (PEM) is designed as an ontology model of the knowledge graphs for pirate events. Four types of entities are set: pem: Aggressor, pem: Victim, pem: Location, and pem: Date, alongside the entity attributes of these four types of entities. In addition, two major categories of 11 types of relations among entities are designed.
2. For entity extraction, the BERT-BiLSTM-CRF model is used for NER from the soft data of pirate events. In the entity linking process, the method of distance learning is introduced for entity linking model training to address the problem of the lack of large amount of labeled text data. In addition, the context attention mechanism is proposed to select the words that are helpful for disambiguation, so as to enhance the performance of entity linking model training.
3. For RE, based on the traditional sentence-level attention mechanism, a bag-level attention mechanism is further introduced, which emphasizes the sentences that are related to the bag label at the global level by calculating the correlation between sentences. As such, the information of the bag is expressed, while sentences that are not related to the bag label are suppressed. Furthermore, bags with the same labels are assembled as a bag-group, and comprehensive discriminative features are mined at the bag-group-level as high-quality features of the relations between representation entity pairs.
4. For the algorithms involved in instance layer filling, experiments were designed to verify the effectiveness and superiority of the proposed models, including NER, entity linking, and relation extraction models, at the same time. According to the given pirate event text, using the proposed NER, entity linking, and relation extraction models, knowledge triples can be accurately extracted, and a high-quality knowledge graph for pirate events is constructed.