Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

Abstract

In the process of bridge management, large amounts of domain information are accumulated, such as basic attributes, structural defects, technical conditions, etc. However, the valuable information is not fully utilized, resulting in insufficient knowledge service in the field of bridge management. To tackle these problems, this paper proposes a complex knowledge base question answering (C-KBQA) framework for intelligent bridge management based on multi-task learning (MTL) and cross-task constraints (CTC). First, with C-KBQA as the main task, part-of-speech (POS) tagging, topic entity extraction (TEE), and question classification (QC) as auxiliary tasks, an MTL framework is built by sharing encoders and parameters, thereby effectively avoiding the error propagation problem of the pipeline model. Second, cross-task semantic constraints are provided for different subtasks via POS embeddings, entity embeddings, and question-type embeddings. Finally, using template matching, relevant query statements are generated and interaction with the knowledge base is established. The experimental results show that the proposed model outperforms compared mainstream models in terms of TEE and QC on bridge management datasets, and its performance in C-KBQA is outstanding.

Knowledge Representation and Reuse of Ship Block Coating Based on Knowledge Graph

“…Then, the bottom-up method is employed for knowledge extraction. By cleaning the data, structured, semi-structured, and unstructured data are extracted using techniques such as D2R [32,33], extracting schema [34,35], and NLP [36] for triple extraction and refining the coating knowledge ontology. The extracted entities are then integrated into the ontology and stored in a graph database for visualization.…”

Dynamic data‐driven railway bridge construction knowledge graph update method

“…Currently, the KG of bridge construction has become a research hotspot, which can help the bridge construction to systematically grasp and integrate the knowledge in the bridge‐building field, including bridge structures, construction materials, construction processes, construction techniques, construction safety, and other aspects (Ding et al., 2016; Lin et al., 2019). However, existing research (Ren et al., 2019; Wu et al., 2021; Yang, Xiang, Li, et al., 2022; Yang, Yang, Li, et al., 2022) on bridge KG mainly focuses on integrating KG technology with historical data to provide information services for bridge management during the operational phase. There is less emphasis on the integration and analysis of bridge construction KG with dynamic monitoring data during the construction phase (such as predictive analysis).…”