This paper proposes a robust methodology for integrating process-specific data and domain expert knowledge into linked knowledge graphs. These graphs utilize an ontology that provides a standardized vocabulary for material science and facilitates the...
General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students&r...
The rapid development of artificial intelligence technology is driving the intelligentization process across various fields, particularly in knowledge graph construction, where significant achievements have been made. However, research on hyper-relat...
With the wide application of computer-aided manufacturing (CAM) software, manufacturing enterprises have accumulated a wealth of numerical control (NC) programming data, providing valuable knowledge resources for new products’ development. Effi...
To address the challenges in multimodal information integration and the inefficiency of knowledge transfer in the construction technology disclosure of deep foundation pit projects, an intelligent generation method based on graph rule reasoning and t...
The constantly updating big data in the ocean engineering domain has challenged the traditional manner of manually extracting knowledge, thereby underscoring the current absence of a knowledge graph framework in such a special field. This paper propo...
In the construction industry, the professional evaluation of construction schemes represents a crucial link in ensuring the safety, quality and economic efficiency of the construction process. However, due to the large number and diversity of constru...
Water conservancy safety management faces several challenges. These include the integration of multi-source heterogeneous data and inefficient knowledge utilization. To address these issues, this study proposes a knowledge graph (KG) construction met...
In producing English-Chinese bilingual maps, it is usually necessary to translate English place names into Chinese. However, pipeline-based methods for translating place names splits the place name translation task into multiple sub-tasks, carries th...
In this paper, a civil aviation travel question and answer (Q&A) method based on integrating knowledge graphs and deep learning technology is proposed to establish a highly efficient travel information Q&A platform and quickly and accurately...
In railway operations and safety oversight, vast amounts of accident-related data are recorded in unstructured textual formats, posing challenges for efficient information extraction and analysis. To address this, we constructed a knowledge graph fro...
Addressing the challenges of dense conceptual content and intricate knowledge relations in computer science textbooks, where traditional pipeline-based information extraction suffers from error propagation and semantic decoupling, this paper proposes...
Existing soil and water conservation standards suffer from fragmentation, inconsistent cross-referencing, and limited machine interpretability, hindering efficient regulatory compliance and decision making. To address these challenges, we developed S...
Smart city planning involves a wide range of knowledge domains. However, general intelligent Question Answering systems often fall short when applied to this domain, and the relevant studies are not yet sufficient. To this end, this paper constructs...
Geometry problem understanding (GPU) is a fundamental task in machine intelligence for problem-solving, requiring more accurate and complete information extraction than general natural language understanding tasks. This paper proposes a knowledge and...
To address the two fundamental contradictions in distribution network digital simulation systems: “diversified simulation requirements versus standardized configuration” and “massive simulation outputs versus sparse decision-making...
Simulation-based scenario testing is central to the safety validation of autonomous driving systems (ADS), where abstract scenarios must be instantiated into executable concrete scenarios. A key challenge in this process is road matching, which aims...
Augmented reality maintenance guidance (ARMG) provides support for industrial maintenance by converting knowledge into instructions and integrating virtual information with physical equipment. However, constructing guidance processes still depends on...
In the product design and manufacturing process, the effective management and representation of system requirements (SRs) are crucial for ensuring product quality and consistency. However, current methods are hindered by document ambiguity, weak requ...
Industrial PLC programming faces persistent difficulties: lengthy development cycles, low fault tolerance, and cross-platform incompatibility among vendors. While LLMs show promise for automated code generation, their direct application is hindered b...
Crisis management in smart cities demands coherent, interoperable, and reusable semantic models to represent complex systems, their risks, crisis situations, interdependencies, and decision-making processes across all lifecycle phases, i.e., preventi...
This paper presents a lightweight semantic retrieval framework driven by an in-memory knowledge graph (IMKG) to overcome the limitations of traditional keyword matching and the prohibitive hardware costs of deep learning models in digitizing ancient...
High-temperature creep data for aero-engine fasteners are essential for life assessment and structural safety, but such records are often archived as heterogeneous tables, reports and figures with weak cross-record associations. This study develops a...
Event detection is a crucial task in natural language processing, and it plays a significant role in numerous applications, such as information retrieval, question answering, and situational awareness. Real-world tasks typically require robust models...
To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to descr...
Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categorie...
Age-related macular degeneration (AMD) is a leading cause of irreversible blindness, yet current tools for forecasting treatment outcomes remain limited by either the opacity of deep learning or the rigidity of rule-based systems. To address this gap...
Knowledge extraction serves as a critical technique for constructing domain-specific knowledge graphs and enabling intelligent text parsing. In the vertical agricultural domain, wheat variety approval texts contain abundant agronomic trait entities a...
As the frequency of cyberattacks rises, extracting actionable cyber threat intelligence (CTI) from diverse online sources has become critical for proactive threat detection and defense. Named entity recognition (NER) serves as a foundational task in...
Dynamic warehouse task allocation must coordinate loading, transport and unloading robots under changing conditions. We propose a versioned neuro-symbolic architecture in which DeepSeek proposes facts and restricted rules, an authenticated semantic s...
Retrieval-augmented generation (RAG) is increasingly used to ground large language model (LLM) tutors, but comparing tutoring architectures is expensive, because every design decision ordinarily requires a fresh study with students. We present a repr...