# OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework
## 2 Related Works
### KGQA Systems
[16] proposed the GRAFT-Net method, which utilizes a personalized PageRank algorithm to expand from the topic entity to surrounding entities and obtain a subgraph of the KG. They also retrieved sentences related to the topic entity and added them to the subgraph, forming a heterogeneous graph. By classifying the entity nodes in the heterogeneous graph, they obtain the answers to the questions. Similarly, [PERSON] et al. [17] also employed the personalized PageRank algorithm to retrieve subgraphs. The GRAFT-Net method constructs subgraphs based on heuristic rules, leading to the inclusion of many irrelevant entities and relations. To address this issue, [PERSON] et al. [18] proposed the PullNet method, which dynamically iterates to construct small-scale subgraphs with high recall, making the answer selection process easier. [PERSON] et al. [19] pruned the question-related subgraphs to reduce their size. They employed two pruning strategies during the training process to remove incorrect answer paths. [PERSON] et al. [20] introduced a trainable subgraph retriever. Starting from the topic of entity, they performed a beam search at each step to select the top k relation expansions to extend the current subgraph. Experimental results show that their subgraph retrieval method outperforms the PullNet method. [PERSON] et al. [21] unified subgraph retrieval and subgraph reasoning in their UniKGQA model. It consists of two modules: semantic matching and matching information propagation. They used a unified model architecture to learn parameters. The model was pre-trained on the semantic matching task and then fine-tuned separately on subgraph retrieval and subgraph reasoning tasks. However, subgraph retrieval faces challenges including dealing with large-scale subgraphs and achieving high recall rates. Within the retrieved subgraphs, there may be multiple potential answer paths, requiring careful analysis and reasoning to select the correct path and extract accurate answers. In conclusion, the aforementioned methods exhibit their respective merits and limitations. Template-based methods are efficient and accurate for specific domain Q&A scenarios, but they necessitate substantial human effort in constructing the templates. In comparison to template-based methods, semantic parsing-based methods offer greater flexibility. However, supervised semantic parsing requires a substantial amount of manually annotated logical form data to train the semantic parser, which is challenging and costly in terms of annotation efforts. Additionally, due to the lack of intermediate supervision signals during the training process, weakly supervised semantic parsing encounters difficulties in achieving model convergence when dealing with large search spaces. Information retrieval-based methods often retrieve subgraphs from the KG, which can effectively narrow down the search space of the model. However, determining the appropriate size of the subgraph is a challenge. A subgraph that is too small may lead to decreased query accuracy, while a subgraph that is too large can lead to difficulties in model convergence. In contrast, the approach presented in this paper exhibits distinct advantages. Unlike the previous works, we introduce an intent-guided template construction method, where query templates are constructed based on predefined intent categories for the ocean engineering Q&A scenario. By filling in templates, convert user questions into query statements, and retrieve answers from the knowledge base. Compared with existing methods, this approach not only avoids the labor-intensive process of manual template construction but also mitigates issues such as search space explosion and difficulty in convergence. Furthermore, it is suitable for domain-specific Q&A tasks and exhibits high accuracy.