# OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework
## 2 Related Works
### Domain-Specific KGs  
Knowledge graphs can be classified into open-world knowledge graphs and domain-specific knowledge graphs. Except for open-world knowledge graphs such as Freebase [1] and DBpedia [2], most knowledge graphs are domain-specific. They effectively facilitate domain conceptualization and data management. The construction of a domain-specific knowledge graph involves two core steps [3]: (1) formalizing concepts and subsequently designing the ontology to describe the corresponding logic design; (2) mining semantic relationships among entities to represent the physical design on multiple data sources.  
Domain-specific knowledge graphs have been applied in various fields such as healthcare, education, engineering, and finance, employing different approaches to design logic concepts. [PERSON] et al. [4] integrated genotypic and phenotypic databases to construct a drug-disease knowledge graph, inferring the drug indications based on the captured genotypic and phenotypic features. [PERSON] et al. [5] established a Chinese marine medicine ontology, leveraging Neo4j to build the knowledge graph. This framework facilitated the retrieval of the data from the Marine Traditional Chinese Medicine atlas database. To effectively manage marine accident knowledge, [PERSON] et al. [6] constructed the knowledge graph by combining top-down and bottom-up methods. Due to the professionalism of knowledge in the field of ocean engineering, the known methods cannot be directly applicable to the KG in this field.  
Knowledge graph technology plays a crucial role in the extraction and integration of diverse data sources. In a study by [PERSON] et al. [7], a multi-data knowledge graph was constructed to detect ocean oil spills by incorporating vectors, text, and other data sources. [PERSON] et al. [8] integrated tabular data and SPARQL endpoints to build an ecotoxicology knowledge graph, improving the prediction of ecotoxicological effects. [PERSON] et al. [9] leveraged public KG resources related to COVID-19 information from Scientific Open Data and Publications, employing it to analyze the drug interactions. These studies underscored that most domain-specific knowledge graphs are composed of multiple data. However, extracting valuable knowledge from such data is a complex task and is faced with domain-specific challenges, particularly extracting entities and their underlying relations when dealing with unbalanced data.  
In summary, knowledge graph construction methods are divided into a top-down approach (ontology layer to data layer) [5; 6; 7; 8] and a bottom-up approach (data layer to ontology layer) [4; 9]. The top-down method involves building concepts and gradually refining them with data, while the bottom-up method organizes inductive entities and gradually abstracts the upper-level concepts. The ontology-guided knowledge extraction process is more suitable for constructing domain knowledge graphs. However, due to the diverse data patterns and complex application requirements in the real world, previous works usually build knowledge graphs by designing specific frameworks to manually construct knowledge graphs. Our approach differs from their work in two key aspects. First, our work emphasizes a balanced approach between automation and human participation for the rapid construction of domain knowledge graphs. Second, we focus on the extraction of heterogeneous multi-source data, which presents a more complex and informative scene. Therefore, we introduce a data-augmentation method to facilitate the process from domain ontology to a data layer of knowledge. Moreover, given the absence of a mature ontology structure and knowledge network in the field of ocean engineering, our research contributes to advancing domain expansion and the practical application of knowledge graphs in engineering.