# OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework
## 2 Related Works
### KGQA Systems  
In recent years, KGQA systems have emerged as a means of utilizing KGs as knowledge bases to meet the query needs of users. There are three different types of approaches for KGQA: template construction, semantic parsing, and information retrieval. The template construction-based methods are realized by predefining question templates and query statements. The natural question is matched with the question template, and then the answer to the question is queried in the knowledge graph according to the query statements. The template construction-based methods offer several advantages, including fast response times, high precision, and suitability for domain-specific knowledge question answering scenarios. However, the construction of templates requires a significant amount of manpower, making it challenging to use in open-domain question answering. The semantic parsing-based methods parse natural questions into corresponding logical expressions and then map them into structured queries to obtain answers from the KG. Early semantic parsing relied on manually constructed lexicons, syntactic templates, and grammar rules to train parsers. It was typically limited to specific domains and specific logical forms, lacking domain adaptability. With the rapid development of deep learning, neural networks have gradually been applied to semantic parsing tasks. [PERSON] et al. [10] proposed a model based on an encoder-decoder architecture, utilizing LSTM to encode the input question and decode the logical form. [PERSON] et al. [11] discovered that the beam width size in sequence-to-sequence and beam search methods has an impact on the accuracy of the model. They introduced a logical form reordering model based on graph attention and Transformers to capture hierarchical structural information of logical forms. The limited quantity and high cost of annotated logical form data have become a bottleneck for supervised semantic parsing models. Researchers have started exploring weakly supervised semantic parsing methods, considering learning a semantic parser using only question-answering pairs. This approach often maps the questions to graph-structured logical forms. As the complexity of the questions increases, the search space for query graphs becomes significantly large. [PERSON] et al. [12] transformed the process of semantic parsing into the generation of query graphs and proposed the STAGG framework for staged query graph generation. [PERSON] et al. [13] proposed an improved staged query graph generation method. They utilized a beam search algorithm and semantic matching models to expand the core inference chain while adding constraints during the expansion process, rather than adding constraints after generating the core inference chain. [PERSON] et al. [14] adopted a two-stage approach for query graph generation. In the first stage, an encoder-decoder model is employed to predict an abstract query graph for the question. The abstract query graph is then used to constrain the generation of candidate query graphs, preventing the generation of noisy candidates. In the second stage, the candidate query graphs are ranked to obtain the best query graph. [PERSON] et al. [15] proposed a two-stage ranking model to select the optimal query graph. Initially, they selected the top k candidates from the candidate query graphs, and then they further ranked the top k candidates based on answer type information, ultimately selecting the best query graph. However, supervised semantic parsing models face limitations due to the scarcity and high cost of annotated logical form data. Weakly supervised methods that use only question-answer pairs have been explored, but they face challenges in convergence when dealing with large search spaces for query graphs. Information retrieval-based KGQA retrieves question-related subgraphs from a KG based on the information contained in the question and then searches for answers within these subgraphs. [PERSON] et al.