# OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework
## 1 Introduction  
The intelligent question-answering (QA) system holds significant importance in various fields, including ocean engineering. The utilization of oceanic knowledge to augment the effectiveness of QA systems is still an open problem. The concept of knowledge graph (KG) originates from knowledge engineering and has been regarded as the development of knowledge engineering in the era of big data. Generally, KG can be classified into domain-specific knowledge graphs and open-world knowledge graphs. The domain-specific KG is oriented to specific fields, which are mainly used for complex application analysis or auxiliary decision-making. It has the characteristics of high expert participation, complex knowledge structure, high knowledge quality, and fine knowledge granularity. Comparatively, the open-world KG is public-oriented and does not require a professional background or industry knowledge. Ocean engineering refers to engineering projects on the development, utilization, and protection of the ocean, including project information, marine resources, human activities, etc. The ocean engineering knowledge graph project involves various subfields such as industrial sea, transportation, tourism, and entertainment. So, an ocean engineering knowledge graph is a typical domain-specific KG. Constructing such a domain-specific knowledge graph requires analyzing and clarifying the different relations between various concepts and establishing ontology. Currently, the QA systems in ocean engineering primarily rely on information systems that utilize relational databases. However, a large amount of useful information is usually hidden in unstructured data, such as public websites and engineering documents. Building a knowledge graph can transform the massive multiple heterogeneous unstructured data into knowledge and organize them by establishing a semantic network. Therefore, constructing a domain-specific knowledge graph has become a hot topic in intelligent ocean engineering. As far as we know, there is still no general method for developing knowledge graphs or intelligent QA applications in ocean engineering. To develop an intelligent QA system for ocean engineering, we face the following challenges:  
* How to develop a domain ontology for ocean engineering in the absence of standard knowledge definitions. * How to extract knowledge from multi-source heterogeneous ocean engineering data. * How to establish an intelligent ocean engineering QA system that satisfies the needs of professional fields and provides effective knowledge services. To address the above challenges, we propose an intelligent knowledge graph question-answering (KGQA) framework for ocean engineering. First, we summarize and classify the concepts and construct an ontology of ocean engineering. Second, we employ techniques in natural language processing to extract knowledge and integrate it with existing databases, resulting in the development of a KG dedicated to ocean engineering. Finally, we adopt an intent recognition model to capture user intentions and implement a keyword-matching method to fill templates. Answer entities are then retrieved from the knowledge graph by Cypher statements, and natural sentences are generated with knowledge representation of four tuples based on the answer entities. Our main contributions can be summarized as follows:  
* We integrate different types of concepts and data and establish the ocean engineering domain's ontology. Accordingly, we propose a novel approach to build knowledge graphs in the ocean engineering field based on marine text feature enhancement. * For the first time in ocean engineering, we present an answer recommendation module based on intent recognition. The intent recognition module improves the efficiency of question parsing and achieves an answer retrieval accuracy of 89.6%.