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Breaking boundaries: Investigating the formation of cross-domain collaboration on social media platforms 打破边界:调查社交媒体平台上跨领域协作的形成
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-01 Epub Date: 2025-11-13 DOI: 10.1016/j.dss.2025.114574
Mengxiao Zhu , Lin Liu , Chunke Su
Creators on social media platforms are increasingly engaging in collaborative content generation. Given the recognized value of integrating diverse perspectives and expertise from different domains, such as fostering innovation, improving content quality, and expanding audience engagement, this study aims to investigate the decision-making dynamics among creators involved in cross-domain collaboration. Drawing on social identity theory, we examine the effect of content domain differentiation on the formation of collaborative relationships and how creators' attributes of content diversity and influencing power alter these effects. Our data were collected from Bilibili, one of the largest Chinese video-sharing platforms, which offers a joint submission feature allowing multiple creators to publish their generated videos. We employ exponential random graph models (ERGMs) to analyze the formation of a collaboration network comprising 2490 creators. The findings reveal that content domain differentiation is negatively related to the formation of collaborative relationships, indicating that cross-domain collaborative relationships are less likely to occur compared to within-domain ones on social media. Furthermore, content diversity mitigates the negative effect of content domain differentiation, suggesting that creators with higher content diversity are more inclined to engage in cross-domain collaborations. Regarding influencing power, creators with less reach and activeness are more likely to participate in cross-domain collaboration. Interestingly, creators with institutional authority are less likely to form cross-domain collaborations, whereas those with individual authority are more likely, compared to non-authority creators. This study highlights the challenges in fostering cross-domain collaborative relationships on social media and elucidates actionable strategies to promote such collaborations.
社交媒体平台上的创作者越来越多地参与到协作内容生成中。鉴于整合来自不同领域的不同观点和专业知识的公认价值,例如促进创新、提高内容质量和扩大受众参与度,本研究旨在调查参与跨领域合作的创作者之间的决策动态。本文以社会认同理论为基础,考察了内容领域分化对协作关系形成的影响,以及创作者的内容多样性属性和影响力如何改变这些影响。我们的数据来自Bilibili,这是中国最大的视频分享平台之一,该平台提供联合提交功能,允许多个创作者发布自己制作的视频。我们使用指数随机图模型(ergm)来分析由2490个创建者组成的协作网络的形成。研究发现,内容领域分化与协作关系的形成呈负相关,表明在社交媒体上,跨领域的协作关系比领域内的协作关系更不容易发生。此外,内容多样性可以缓解内容领域分化的负面影响,表明内容多样性越高的创作者更倾向于进行跨领域合作。在影响力方面,覆盖面和活跃度较低的创作者更有可能参与跨领域合作。有趣的是,与非权威的创造者相比,拥有机构权威的创造者不太可能形成跨领域合作,而拥有个人权威的创造者则更有可能形成跨领域合作。本研究强调了在社交媒体上培养跨领域合作关系所面临的挑战,并阐明了促进这种合作的可行策略。
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引用次数: 0
Published content vs. live-streamed: Empirical from digital content activities in online healthcare communities 发布内容与直播:来自在线医疗保健社区数字内容活动的经验
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.dss.2025.114577
Liuan Wang , Yuke Luo , Linan Zhang
Online Health Communities (OHCs) serve as a platform for individuals seeking health support, where the exchange of health information harbors the potential to generate substantial social value. Existing literature reveals that previous research has primarily focused on the incentives for doctors' information sharing. However, the mechanism through which this information sharing translates into tangible benefits for hospitals remains unclear. To address these gaps, this study interprets it as a kind of digital content activity (DCA) and delves into its impact on hospitals' online demand in OHC. Analyzing data from over 2000 active hospitals on a leading OHC in China, our findings indicate that hospitals' published content activity consistently increases their online demand in OHC. In contrast, live-streamed content activity decreases online demand in the short term, yet this impact turns positive in the long term. Furthermore, a hospital's organizational capital enhances the impact of live-streamed content activity, while the hospital's reputation strengthens the long-term positive impact of published content activity. This study offers a novel perspective for understanding knowledge sharing within OHCs, providing practical insights for OHCs and hospitals.
在线卫生社区(OHCs)是寻求卫生支持的个人的平台,其中卫生信息的交换具有产生巨大社会价值的潜力。现有文献显示,以往的研究主要集中在医生信息共享的激励机制上。然而,这种信息共享转化为医院切实利益的机制尚不清楚。为了解决这些差距,本研究将其解释为一种数字内容活动(DCA),并深入研究其对OHC中医院在线需求的影响。通过分析中国一家领先的OHC上2000多家活跃医院的数据,我们的研究结果表明,医院发布的内容活动持续增加了他们对OHC的在线需求。相比之下,直播内容活动在短期内会减少在线需求,但从长期来看,这种影响会转为积极。此外,医院的组织资本增强了直播内容活动的影响力,而医院的声誉增强了发布内容活动的长期积极影响。本研究为理解OHCs内部的知识共享提供了一个新的视角,为OHCs和医院提供了实用的见解。
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引用次数: 0
What makes a well-performing NFT collection initial offering campaign: Evidence from OpenSea Drop 是什么让NFT系列的首次发行活动表现良好:来自OpenSea的证据掉落
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.dss.2025.114575
Zhichao Wu , Xi Zhao , Xiaoni Lu
The Drop feature on OpenSea provides creators with a standardized tool for designing NFT collection (NFTC) initial offering campaigns. This study examines the impact of campaign design elements on sales performance. Analyzing 693 NFTCs, we reveal an inverted U-shaped relationship between target size and sales outcomes, attributable to the balance between social proof and scarcity. Additionally, we observe a positive effect of incorporating pre-sale stages, which is driven by social proof. Notably, OpenSea's official certification, as a significant credibility signal, moderates these effects. This research advances the understanding of social proof theory within the Web3.0 context, offering actionable insights for NFT creators to optimize campaign strategies and for platform managers to enhance the effectiveness of the Drop feature.
OpenSea的Drop功能为创建者提供了一个标准化的工具来设计NFTC首次发行活动。本研究探讨活动设计元素对销售绩效的影响。通过对693个国家的分析,我们发现目标规模与销售结果之间存在倒u型关系,这可归因于社会认同与稀缺性之间的平衡。此外,我们观察到加入预售阶段的积极影响,这是由社会认同驱动的。值得注意的是,OpenSea的官方认证作为一个重要的可信度信号,缓和了这些影响。这项研究促进了对Web3.0背景下社会认同理论的理解,为NFT创作者优化活动策略和平台管理者提高Drop功能的有效性提供了可操作的见解。
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引用次数: 0
Beyond helpfulness votes: Examining the helpfulness of content in online customer review text 超越有用的投票:检查在线客户评论文本中内容的有用性
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-01 Epub Date: 2025-10-07 DOI: 10.1016/j.dss.2025.114546
Stefanie Erlebach , Kilian Züllig , Alexander Kupfer , Leonie Embacher , Steffen Zimmermann
Identifying the most helpful online customer reviews (OCRs) is crucial for online shopping sites aiming to support consumer purchase decisions. Equally important is understanding how OCR helpfulness varies across different types of goods. By focusing on the most informative part of OCRs – the OCR text – and applying a novel methodological approach, we provide this knowledge without relying on potentially biased, yet widely utilized, helpfulness votes. Grounded in the Elaboration Likelihood Model (ELM) of persuasion, we hypothesize that only selected thematic categories of OCR text are helpful, and that the type of goods moderates this helpfulness. Our findings reveal that product-related content (e.g., functionality or quality) is less helpful for experience goods than for search goods. Conversely, customer-related content (e.g., emotional attitudes or recommendations) is more helpful for experience goods than for search goods. Our contribution is threefold. First, we present an approach that allows the investigation of OCR helpfulness independent of potentially biased helpfulness votes in a generalizable, domain-independent setting. Second, using this approach, we provide insights into the helpfulness of OCR texts across thematic categories and types of goods. Third, we extend the application of the ELM by providing theoretically grounded explanations for the observed effects. From a practical perspective, our findings inform the design of OCR systems for online shopping sites that aim to provide consumers with the most helpful OCRs.
识别最有用的在线客户评论(ocr)对于旨在支持消费者购买决策的在线购物网站至关重要。同样重要的是理解OCR的有用性在不同类型的商品之间是如何变化的。通过关注OCR中信息量最大的部分——OCR文本,并应用一种新颖的方法,我们提供了这些知识,而不依赖于潜在的偏见,但广泛使用的有用的投票。在说服的细化可能性模型(ELM)的基础上,我们假设只有选定的OCR文本的主题类别是有用的,并且商品的类型调节了这种有用性。我们的研究结果表明,与产品相关的内容(例如,功能或质量)对体验商品的帮助小于搜索商品。相反,与顾客相关的内容(例如,情感态度或推荐)对体验商品比搜索商品更有帮助。我们的贡献是三重的。首先,我们提出了一种方法,允许在可推广的、领域独立的设置中独立于潜在偏见的有用性投票来调查OCR的有用性。其次,使用这种方法,我们可以深入了解跨主题类别和商品类型的OCR文本的帮助。第三,我们通过为观测到的效应提供理论基础的解释来扩展ELM的应用。从实际的角度来看,我们的研究结果为在线购物网站的OCR系统设计提供了信息,旨在为消费者提供最有用的OCR。
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引用次数: 0
ProMatch: A novel dynamic process-unpacking approach for two-way proactive recruitment ProMatch:一种新颖的动态流程拆解方法,用于双向主动招聘
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-01 Epub Date: 2025-11-13 DOI: 10.1016/j.dss.2025.114564
Xiaowei Shi , Cong Wang , Qiang Wei
Online recruitment platforms have revolutionized labor markets by enabling bidirectional engagement between job seekers and employers, but this transformation has also introduced complex decision-making challenges due to information overload and parallel decision processes. Existing research and algorithms often focus on static and one-way models, neglecting the dynamic feedback loops and preference adjustments inherent in two-way proactive recruitment. This study introduces ProMatch, a novel person-job matching approach designed to support decision-making for both sides. ProMatch formalizes recruitment as a multi-stage process involving intention formation, preference updates, and bilateral matching, capturing the sequential dependencies between decision outcomes. It also incorporates a dynamic preference learning mechanism grounded in self-regulation theory, which iteratively refines preferences using textual profiles, historical interactions, and feedback. Validation using a real-world IT enterprise dataset and a two-week field experiment demonstrates ProMatch’s effectiveness. Results show a 9% increase in click-through rates and a 20% improvement in interview-through rates, highlighting its ability to enhance prediction accuracy by dynamically modeling evolving preferences. ProMatch’s innovations offer actionable decision support for both job seekers and employers, ultimately improving recruitment efficiency and cost-effectiveness in modern recruitment ecosystems.
在线招聘平台通过实现求职者和雇主之间的双向互动,彻底改变了劳动力市场,但这种转变也带来了复杂的决策挑战,因为信息过载和决策过程并行。现有的研究和算法往往侧重于静态和单向模型,而忽略了双向主动招聘中固有的动态反馈循环和偏好调整。本研究引入ProMatch,一种新颖的个人-工作匹配方法,旨在支持双方的决策。ProMatch将招聘形式化为一个多阶段的过程,包括意向形成、偏好更新和双边匹配,捕捉决策结果之间的顺序依赖关系。它还结合了基于自我调节理论的动态偏好学习机制,该机制使用文本概要、历史交互和反馈迭代地改进偏好。使用真实的IT企业数据集和为期两周的现场实验验证了ProMatch的有效性。结果显示,点击率提高了9%,采访通过率提高了20%,突出了通过动态建模不断变化的偏好来提高预测准确性的能力。ProMatch的创新为求职者和雇主提供可操作的决策支持,最终提高现代招聘生态系统的招聘效率和成本效益。
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引用次数: 0
Enhancing decision support for bystander interventions: The role of victim emotional disclosure and collective signals in social media incivility 增强旁观者干预的决策支持:受害者情感披露和集体信号在社交媒体不文明中的作用
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-12-01 Epub Date: 2025-10-14 DOI: 10.1016/j.dss.2025.114556
Xiya Guo , Jiahua Jin , Le Wang , Xiangbin Yan
Effective support for victims of online incivility is crucial for maintaining healthy digital communities and improving individual well-being. However, the decision-making processes underlying bystander intervention in social media environments remain insufficiently understood. Drawing on signaling theory, this study investigates how different forms of victim self-disclosure—specifically, the type of negative emotion expressed (introverted versus extraverted) and the degree of collective tendency—affect bystander empathy, moral judgment, and the intention to provide social support. Through an online experiment with Chinese social media users, we found that victim disclosures characterized by introverted negative emotions and high collective tendencies elicit greater bystander empathy and stronger intentions to provide both informational and emotional support. Our findings elucidate the decision mechanisms through which bystanders interpret signals and decide to intervene, offering actionable insights for the design of decision support systems that can facilitate effective bystander responses and improve comment section management on social media platforms. These results have significant implications for the development of intelligent, context-aware DSS interfaces and algorithms aimed at fostering pro-social behavior and mitigating the escalation of online deviance.
对网络不文明行为受害者的有效支持对于维护健康的数字社区和改善个人福祉至关重要。然而,在社交媒体环境中旁观者干预的决策过程仍然没有得到充分的了解。基于信号理论,本研究探讨了不同形式的受害者自我表露,特别是消极情绪的表达类型(内向与外向)和集体倾向的程度如何影响旁观者共情、道德判断和提供社会支持的意愿。通过对中国社交媒体用户的在线实验,我们发现以内向负面情绪和高度集体倾向为特征的受害者披露引发了更大的旁观者同情和更强的提供信息和情感支持的意愿。我们的研究结果阐明了旁观者解释信号并决定干预的决策机制,为决策支持系统的设计提供了可操作的见解,这些系统可以促进有效的旁观者反应并改善社交媒体平台上的评论区管理。这些结果对于开发智能的、上下文感知的决策支持系统接口和算法具有重要意义,这些接口和算法旨在促进亲社会行为和减轻在线偏差的升级。
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引用次数: 0
All risks ain't the same – A risk facets perspective on AI-based decision support systems 不是所有的风险都一样——基于人工智能的决策支持系统的风险视角
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-12-01 Epub Date: 2025-10-15 DOI: 10.1016/j.dss.2025.114557
Jobin Strunk, Anika Nissen, Stefan Smolnik
Artificial intelligence-based decision support systems (AI-DSSs) transform decision-making across diverse contexts including healthcare, finance, and personalized product and service recommendations. Each context exposes users to a dominant risk facet, such as physical risk when using a health-related AI-DSS, financial risk when using a robo-advisor, or psychosocial risk when interacting with an AI-DSS integrated in a social app. Utilizing risk theory, we systematically analyze how different risk facets and severities influence trust in and advice taking from AI-DSSs. We conduct a between-subjects online experiment with 958 participants who interact with AI-DSSs, covering three major risk facets and two risk severities for each. Our results reveal that risk facets and severities partially jointly influence advice taking. Additionally, while advice taking in physical risk scenarios remains relatively stable across severity levels, financial and psychosocial contexts show significantly greater sensitivity to changes in risk severity. This highlights an interaction effect, demonstrating that the impact of risk severity on advice taking is partially influenced by the risk facet. Furthermore, we found that trust mediates the effect of risk facet and risk severities on advice taking. Our insights enhance the theoretical understanding of the interplay between risk, trust, and advice taking in human-AI-DSS interaction. We contribute by bridging critical gaps in current literature, enriching the discourse on AI-DSS trust and advice taking in risk-laden environments. This helps developers of AI-DSSs understand the influence of risk facets related to their service and adapt their digital offerings accordingly.
基于人工智能的决策支持系统(ai - dss)可以在不同的环境中转换决策,包括医疗保健、金融以及个性化产品和服务建议。每个情境都将用户暴露于一个主要的风险方面,例如使用与健康相关的AI-DSS时的身体风险,使用机器人顾问时的财务风险,或与集成在社交应用程序中的AI-DSS交互时的心理社会风险。利用风险理论,我们系统地分析了不同的风险方面和严重程度如何影响对AI-DSS的信任和从AI-DSS中获取建议。我们对958名与ai - dss互动的参与者进行了一项受试者间在线实验,涵盖了三个主要风险方面和每个风险的两个严重程度。我们的研究结果表明,风险方面和严重程度部分共同影响建议的采纳。此外,尽管在不同严重程度的物理风险情景中,建议的采纳相对稳定,但财务和社会心理环境对风险严重程度的变化表现出更大的敏感性。这突出了相互作用,表明风险严重程度对建议采纳的影响部分受到风险方面的影响。此外,我们发现信任在风险面和风险严重程度对建议采纳的影响中起中介作用。我们的见解增强了对人-人工智能-决策支持系统互动中风险、信任和建议采纳之间相互作用的理论理解。我们的贡献是弥合当前文献中的关键空白,丰富关于AI-DSS信任和在充满风险的环境中采纳建议的论述。这有助于AI-DSSs的开发人员了解与其服务相关的风险方面的影响,并相应地调整其数字产品。
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引用次数: 0
Consistency matters: Impacts of dimension-level characteristics on the helpfulness of multi-dimensional reviews 一致性问题:维度水平特征对多维回顾的帮助性的影响
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-12-01 Epub Date: 2025-10-27 DOI: 10.1016/j.dss.2025.114561
Jun Yang , Hongchen Duan , Demei Kong
Multi-dimensional (MD) rating systems are increasingly adopted by online platforms to capture product evaluations across multiple attributes. While this structured format enriches product information, it also makes intra-review inconsistencies salient, raising new questions about how such inconsistencies shape review helpfulness—a topic largely overlooked in prior research dominated by single-dimensional (SD) reviews. This study examines the effects of cross-dimensional inconsistencies (in ratings, sentiment, and informativeness) and a cross-modal inconsistency (rating–sentiment misalignment within a dimension) on the perceived helpfulness of MD reviews, drawing on cognitive dissonance theory. Using a large dataset from a leading Chinese automobile review platform, we find that cross-dimensional rating inconsistency can enhance review helpfulness by signaling realistic product trade-offs, whereas sentiment, informativeness, and cross-modal inconsistencies reduce helpfulness by triggering unresolved dissonance. We further uncover interactive effects among cross-dimensional inconsistencies: the positive effect of rating inconsistency diminishes in the presence of high sentiment or informativeness inconsistencies. Conversely, the negative effects of sentiment and informativeness inconsistencies are mitigated when they co-occur. Additionally, the impact of these inconsistencies varies depending on reviewer characteristics, product characteristics, and review order. These findings advance the literature on review helpfulness and MD rating systems by introducing cross-dimensional and cross-modal inconsistencies as key determinants and clarifying when inconsistency serves as a credibility signal versus a cognitive burden.
在线平台越来越多地采用多维(MD)评级系统来获取跨多个属性的产品评估。虽然这种结构化的格式丰富了产品信息,但它也使内部评论的不一致性变得突出,提出了新的问题,即这种不一致性是如何影响评论的有用性的——这是一个在以前由单维(SD)评论主导的研究中很大程度上被忽视的主题。本研究利用认知失调理论,考察了跨维度不一致(评分、情绪和信息性)和跨模态不一致(一个维度内的评分-情绪不一致)对医学博士评论的感知帮助性的影响。使用来自中国领先的汽车评论平台的大型数据集,我们发现跨维度评级不一致可以通过发出现实产品权衡的信号来增强评论的有用性,而情感、信息性和跨模态不一致通过触发未解决的不和谐而降低有用性。我们进一步揭示了跨维度不一致之间的互动效应:在高情绪或信息不一致的情况下,评级不一致的积极作用会减弱。相反,当情绪和信息不一致同时出现时,它们的负面影响会得到缓解。此外,这些不一致的影响取决于审稿人特征、产品特征和审稿人顺序。这些发现通过引入跨维度和跨模态的不一致性作为关键决定因素,并澄清了不一致性何时作为可信度信号而不是认知负担,从而推进了关于评论有用性和MD评级系统的文献。
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引用次数: 0
Uncertainty-aware augmented generation (UAG): A novel deep learning method for enriching in-conversation user intent toward improved LLM generation 不确定性感知增强生成(UAG):一种新的深度学习方法,用于丰富会话中用户意图,以改进LLM生成
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-12-01 Epub Date: 2025-10-25 DOI: 10.1016/j.dss.2025.114558
Xulei Jin , Lihua Huang , Tan Cheng , Shuaiyong Xiao , Chenghong Zhang , Yajing Wang
In the era of artificial intelligence generated content, accurate user intent detection and effective response generation have become critical capabilities for LLM-based service agents. However, due to users' limited familiarity with domain-specific knowledge, their underspecified queries often introduce intent uncertainty, impeding the generation of responses that are both contextually relevant and operationally executable. To address this challenge, we propose uncertainty-aware augmented generation (UAG), a novel deep learning method that jointly detects user intents and quantifies their associated uncertainty, thereby bridging the gap between user queries and enterprise-executable actions. UAG enhances intent detection along a predefined intent tree by incorporating two hierarchical consistency losses, and improves the quality of generated responses by leveraging salient intent paths—extracted using a proposed uncertainty-aware intent (UI) score—as an augmented prompt. Experiment results based on two datasets showed that UAG outperformed state-of-the-art alternative benchmarks, and explanatory analysis rendered insight on the role of uncertainty in user intent detection and response generation.
在人工智能生成内容的时代,准确的用户意图检测和有效的响应生成已经成为基于llm的服务代理的关键能力。然而,由于用户对特定领域知识的熟悉程度有限,他们未指定的查询通常会引入意图不确定性,从而阻碍了上下文相关和操作可执行的响应的生成。为了应对这一挑战,我们提出了不确定性感知增强生成(UAG),这是一种新颖的深度学习方法,可以联合检测用户意图并量化其相关的不确定性,从而弥合用户查询和企业可执行操作之间的差距。UAG通过结合两个层次一致性损失来增强沿预定义意图树的意图检测,并通过利用使用提议的不确定性感知意图(UI)分数提取的显著意图路径作为增强提示来提高生成响应的质量。基于两个数据集的实验结果表明,UAG优于最先进的替代基准,解释性分析深入了解了不确定性在用户意图检测和响应生成中的作用。
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引用次数: 0
Prompting large language models based on semantic schema for text-to-Cypher transformation towards domain Q&A 提示基于语义模式的大型语言模型,用于向领域问答进行文本到密码的转换
IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-12-01 Epub Date: 2025-10-06 DOI: 10.1016/j.dss.2025.114553
Yuwei Wan , Zheyuan Chen , Ying Liu , Chong Chen , Michael Packianather
Translating natural language inquiries into executable Cypher queries (text-to-Cypher) is a persistent bottleneck for non-technical teams relying on knowledge graphs (KGs) in fast-changing industrial settings. Rule and template converters need frequent updates as schemas evolve, while supervised and fine-tuned parsers require recurring training. This study proposes a schema-guided prompting approach, namely text-to-Cypher with semantic schema (T2CSS), to align large language models (LLMs) with domain knowledge for producing accurate Cypher. T2CSS distils a domain ontology into a lightweight semantic schema and uses adaptive filtering to inject the relevant subgraph and essential Cypher rules into the prompt for constraining generation and reducing schema-agnostic errors. This design keeps the prompt focused and within context length limits while providing the necessary domain grounding. Comparative experiments demonstrate that T2CSS with GPT-4 outperformed baseline models and achieved 86 % accuracy in producing correct Cypher queries. In practice, this study reduces retraining and maintenance effort, shortens turnaround times, and broadens KG access for non-experts.
在快速变化的工业环境中,将自然语言查询转换为可执行的Cypher查询(文本到Cypher)是依赖知识图(KGs)的非技术团队的一个持续瓶颈。规则和模板转换器需要随着模式的发展而频繁更新,而受监督和微调的解析器则需要反复训练。本研究提出了一种模式引导的提示方法,即文本到密码与语义模式(T2CSS),以使大型语言模型(llm)与领域知识保持一致,以产生准确的密码。T2CSS将领域本体提炼为轻量级语义模式,并使用自适应过滤将相关子图和基本Cypher规则注入到提示符中,以约束生成并减少模式不可知错误。这种设计使提示集中在上下文长度限制内,同时提供必要的领域基础。对比实验表明,具有GPT-4的T2CSS优于基线模型,在生成正确的Cypher查询方面达到86%的准确率。实际上,这项研究减少了再培训和维护工作,缩短了周转时间,并扩大了非专家的KG访问范围。
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引用次数: 0
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Decision Support Systems
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