# Joint Entity-Relation Extraction for Knowledge Graph Construction in Marine Ranching Equipment
## 1 Introduction  
Marine ranching, a cornerstone of China's Blue Granary strategy, has emerged as a transformative approach to modernize marine fisheries, enhance aquaculture productivity, and promote ecological sustainability [1]. The rapid development of related equipment, such as intelligent feeding systems, deep-sea cages, and multi-functional platforms, has significantly improved operational efficiency [2, 3]. However, the exponential growth of domain-specific knowledge remains fragmented, with critical information dispersed across heterogeneous sources including enterprise records, experts' experiences, academicliterature, and technical standards. This fragmentation impedes intelligent decision-making, real-time monitoring, and knowledge sharing, thereby limiting the full potential of marine ranching industrialization. A knowledge graph (KG) is a state-of-the-art semantic network paradigm; it employs graph structures to visualize relationships between entities, demonstrating advantages in intuitiveness, efficiency, and scalability [4]. The concept of KG was first proposed by Google in 2012 and applied in the search engine domain [5]. Nowadays, knowledge graphs are classified into general knowledge graphs and vertical knowledge graphs. General knowledge graphs do not target specific domains and have relatively low requirements for the accuracy of knowledge. They emphasize the breadth of knowledge and have a wide coverage. Examples include DBpedia [6], Yago [7], Freebase [8], and Wikidata [9]. Vertical knowledge graphs, on the other hand, are oriented towards a specific domain and emphasize the depth of knowledge. They have higher requirements for the professionalism and accuracy of knowledge. Examples include IMDB (Internet Movie Database) [10], MusicBrainz [11], and Chinese medical knowledge graphs. Recent advancements in vertical knowledge graph applications demonstrate their versatility in various industries. For example, in manufacturing, [PERSON] et al. [12] automated OPC UA (OLE for Process Control Unified Architecture) information modeling via KG to unify heterogeneous equipment data, while [PERSON] et al. [13] integrated geometric and assembly process data through a KG-based semantic model (KG-ASM). For agriculture, [PERSON] et al. [14] constructed a knowledge graph of agricultural engineering technology based on large language model. In the other marine domain, [PERSON] et al. [15] provide an overview of China's policies on the development of marine ranching over the past two decades. Their study clarifies the current status, research hotspots, and future directions of marine ranching research. Additionally, [PERSON] et al. [16] established a knowledge graph construction and application framework for maritime accidents to facilitate the extraction and management of maritime knowledge from unstructured texts. Despite these advancements, existing research predominantly focuses on structured data from product design or assembly processes, neglecting the unique challenges of marine equipment domains where unstructured text dominates and entities exhibit complex interdependencies. Traditional extraction methods suffer from error propagation and inefficiency in handling such scenarios, while deep learning models like BERT-BiLSTM-CRF face limitations in parameter efficiency and contextual dependency modeling. Compared with the aforementioned research, this article focuses on the study of joint extraction methods for knowledge in the domain of marine ranching equipment. By designing targeted questionnaires for diverse users and employees, several limitations in existing vertical KG have been identified as follows: (1) Limited data volume and limited knowledge scope. (2) Ambiguity in the structure of the knowledge framework. (3) Low efficiency and accuracy in knowledge extraction. (4) Difficult updates and maintenance. Consequently, there is an urgent need for a specialized KG framework tailored to marine ranching equipment.