Predicate constraints based question answering over knowledge graph

Generally, QA systems suffer from the structural difference where a question is composed of unstructured data, while its answer is made up of structured data in a Knowledge Graph (KG). To bridge this gap, most approaches use lexicons to cover data that are represented differently. However, the existing lexicons merely deal with representations for entity and relation mentions rather than consulting the comprehensive meaning of the question. To resolve this, we design a novel predicate constraints lexicon which restricts subject and object types for a predicate. It facilitates a comprehensive validation of a subject, predicate and object simultaneously. In this paper, we propose Predicate Constraints based Question Answering (PCQA). Our method prunes inappropriate entity/relation matchings to reduce search space, thus leading to an improvement of accuracy. Unlike the existing QA systems, we do not use any templates but generates query graphs to cover diverse types of questions. In query graph generation, we put more focus on matching relations rather than linking entities. This is well-suited to the use of predicate constraints. Our experimental results prove the validity of our approach and demonstrate a reasonable performance compared to other methods which target WebQuestions and Free917 benchmarks.     

HCA: Hierarchical Compare Aggregate model for question retrieval in community question answering

We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical Compare Aggregate (HCA) model that can handle this problem without using any task-specific features. We first split questions into sentences and compare every sentence pair of the two questions using a proposed Word-Level-Compare-Aggregate model called WLCA-model and then the comparison results are aggregated with a proposed Sentence-Level-Compare-Aggregate model to make the final decision. To handle the insufficient training data problem, we propose a sequential transfer learning approach to pre-train the WLCA-model on a large paraphrase detection dataset. Our experiments on two editions of the Semeval benchmark datasets and the domain-specific AskUbuntu dataset show that our model outperforms the state-of-the-art models.     

A study on customer,supplier, and competitor knowledge using the knowledge chain model

In the new economy, firms are willing to pay abundant premiums for the significant entrepreneurial capacities of management and staff in order to develop, build, protect, transfer and integrate knowledge. Although companies and scholars have indeed recognized the value of knowledge management, they have not generally included customer, supplier, and competitor knowledge, preferring to emphasize the process of knowledge acquisition and sharing that takes place within organizations. Thus, this study proposes a conceptual framework, and uses interpretative case studies, to explore how an enterprise obtains the three types of external knowledge. Moreover, through the following five primary activities – acquisition, selection, generation, internalization, and externalization – this study will illustrate how enterprises apply the internal knowledge chain to transform their customer, supplier, and competitor knowledge to enhance enterprise competitiveness.     

一种开放式的知识工作建模方法

知识工作的不确定性和复杂性相对较高,这给知识工作建模方法的研究带来了相当的难度,为此,提出一种开放式的知识工作建模方法。基于对知识工作基本特征的理解,确定知识工作模型的组成要素、各要素的属性及其关联关系;通过对各组成要素状态转换方式的分析,说明知识工作模型的动态运行机制。这种开放式的知识工作建模方法更切合知识工作的特征和本质,有助于知识工作的管理实践。     

悄然兴起的科学知识图谱

科学知识图谱是显示科学知识的发展进程与结构关系的一种图形。它的悄然兴起,一方面是揭示科学知识及其活动规律的科学计量学从数学表达转向图形表达的产物,另一方面又是显示科学知识地理分布的知识地图转向以图象展现知识结构关系与演进规律的结果。这里,在介绍有关科学知识图谱基本概念的基础上,从数据库、数据格式及存取,数据分析算法,可视化和互动设计,科学计量学等方面阐述了有关科学知识地图绘制的最新进展,并展望了其应用前景。其进展表明,无论是对于科学技术研究,还是对于企业技术创新,科学知识图谱都是一种有效的知识管理工具。     

ARL: An adaptive reinforcement learning framework for complex question answering over knowledge base

Recently, reinforcement learning (RL)-based methods have achieved remarkable progress in both effectiveness and interpretability for complex question answering over knowledge base (KBQA). However, existing RL-based methods share a common limitation: the agent is usually misled by aimless exploration, as well as sparse and delayed rewards, leading to a large number of spurious relation paths. To address this issue, a new adaptive reinforcement learning (ARL) framework is proposed to learn a better and interpretable model for complex KBQA. First, instead of using a random walk agent, an adaptive path generator is developed with three atomic operations to sequentially generate the relation paths until the agent reaches the target entity. Second, a semantic policy network is presented with both character-level and sentence-level information to better guide the agent. Finally, a new reward function is introduced by considering both the relation paths and the target entity to alleviate sparse and delayed rewards. The empirical results on five benchmark datasets show that our model is more effective than state-of-the-art approaches. Compared with the strong baseline model SRN, the proposed model achieves performance improvements of 23.7% on MetaQA-3 using the metric Hits@1.     

Generative retrieval for conversational question answering

Effective passage retrieval is crucial for conversation question answering (QA) but challenging due to the ambiguity of questions. Current methods rely on the dual-encoder architecture to embed contextualized vectors of questions in conversations. However, this architecture is limited in the embedding bottleneck and the dot-product operation. To alleviate these limitations, we propose generative retrieval for conversational QA (GCoQA). GCoQA assigns distinctive identifiers for passages and retrieves passages by generating their identifiers token-by-token via the encoder–decoder architecture. In this generative way, GCoQA eliminates the need for a vector-style index and could attend to crucial tokens of the conversation context at every decoding step. We conduct experiments on three public datasets over a corpus containing about twenty million passages. The results show GCoQA achieves relative improvements of +13.6% in passage retrieval and +42.9% in document retrieval. GCoQA is also efficient in terms of memory usage and inference speed, which only consumes 1/10 of the memory and takes in less than 33% of the time. The code and data are released at https://github.com/liyongqi67/GCoQA.     

Boosting question answering over knowledge graph with reward integration and policy evaluation under weak supervision

Among existing knowledge graph based question answering (KGQA) methods, relation supervision methods require labeled intermediate relations for stepwise reasoning. To avoid this enormous cost of labeling on large-scale knowledge graphs, weak supervision methods, which use only the answer entity to evaluate rewards as supervision, have been introduced. However, lacking intermediate supervision raises the issue of sparse rewards, which may result in two types of incorrect reasoning path: (1) incorrectly reasoned relations, even when the final answer entity may be correct; (2) correctly reasoned relations in a wrong order, which leads to an incorrect answer entity. To address these issues, this paper considers the multi-hop KGQA task as a Markov decision process, and proposes a model based on Reward Integration and Policy Evaluation (RIPE). In this model, an integrated reward function is designed to evaluate the reasoning process by leveraging both terminal and instant rewards. The intermediate supervision for each single reasoning hop is constructed with regard to both the fitness of the taken action and the evaluation of the unreasoned information remained in the updated question embeddings. In addition, to lead the agent to the answer entity along the correct reasoning path, an evaluation network is designed to evaluate the taken action in each hop. Extensive ablation studies and comparative experiments are conducted on four KGQA benchmark datasets. The results demonstrate that the proposed model outperforms the state-of-the-art approaches in terms of answering accuracy.     

知识发展的类生物模型

 分析了关于知识本质和发展规律的相关研究成果,提出知识发展的关键过程知识创造和知识扩散的本质是在知识之间产生新的关联,并将知识的发展过程与生物的繁衍进化进行了类比,归纳出知识的发展具有有机性、外生性、惯性、变异性以及逻辑谐和性的特点,并构建了知识发展的类生物模型。     

基于知识平衡计分卡的知识管理模型

知识管理与平衡计分卡虽然都是目前理论研究的热点,但是把二者结合起来研究的还很鲜见。本文对平衡计分卡和知识管理进行整合研究,提出了知识平衡计分卡(KBSC)和基于KBSC的知识管理模型,并介绍了应用这个模型的步骤与方法。     

在线知识社区协作冲突影响因素研究-以Wikipedia为例

基于社会认知理论,构建了在线知识社区协作冲突影响模型,探讨了知识异质性、群体分化、隐匿性、任务复杂性和协调机制对协作冲突的影响机制。以364个英文版Wikipedia条目为样本,采用层次回归分析进行了实证检验,结果表明：知识异质性和群体分化与协作冲突呈正向关系;隐匿性与协作冲突呈倒U型关系;任务复杂性正向调节知识异质性、隐匿性与协作冲突之间的关系;协调机制正向调节知识异质性与协作冲突之间的关系,负向调节群体分化与协作冲突之间的关系。     

企业知识转移生态学模型

高效知识转移已经成为企业建立竞争优势的关键因素之一。文章基于生态学的视角,提出了企业知识具有散落分布性、嵌入依附性、动态继承性和增量积累性等四方面生态学特征。在此基础上,构建了企业知识转移生态学模型,在该模型中,知识个体、知识种群与知识群落间知识的相互联系构成了企业知识链与知识网。其中,个体知识转移强调员工自身知识的迁移与完善,种群知识转移侧重团队知识的整合与创造,而群落知识转移则关注企业知识应用的整体效率。此外,研究还发现,企业生态系统中的知识在转移过程中还呈现出一些超生态学特征。最后,文章结合思科公司知识转移的案例,提出了促进企业知识生态系统知识转移效率的启示与建议。     

Automatic categorization of questions for user-interactive question answering

Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions.     

知识资源池:知识创新和共享的宏观机制模型

知识的创新和共享不仅仅是企业和组织的需求,更应该是社会和国家的需要。知识的创新及其在国家范围内的共享能促进国家知识优势的形成;知识资源池就是以大学为“催化剂”和中介的一种社会知识创新与共享机制。大学作为社会知识管理的中心是其职责所在,也是其自身的优势所决定的;大学自身的知识管理要与社会知识管理结合进行,其他组织的知识管理也必须融合到社会知识管理的大系统中。     

知识链组织之间的知识分工决策模型研究

摘要：本文研究了知识链组织在研究与开发协作中,开发活动必须由代理组织完成,而研究活动既可由核心企业完成,也可由代理组织完成的知识分工决策过程,分析了研究与开发完全委托和部分委托不同情形下的最优支付。研究表明,如果核心企业从事研究活动所获得的技术能力增长收益越高,研究阶段向开发阶段转移知识的效率越高,研究活动相对于开发活动的重要性越高,则核心企业越倾向于把研究活动控制在企业内部。       

企业知识转移的情境分析模型

企业知识的发展与转移依赖于企业特定的情境，本文从文化、战略、组织结构和过程、环境、技术和运营等五方面来构建知识的情境维度，分析情境与企业知识转移的互动关系，提出企业知识转移的情境模型和相应的二种情境模式，并进行相关的案例研究。     

知识转化灰箱模型与企业知识管理策略的研究

本文基于知识管理理论和认知学理论,对隐性和显性知识转化过程进行了深度剖析,提出了“假隐性知识”、“假显性知识”的概念和知识转化过程的灰箱模型,并阐述了该模型在制定企业知识管理策略中的应用。     

Counterfactual can be strong in medical question and answering

Medical question and answering is a crucial aspect of medical artificial intelligence, as it aims to enhance the efficiency of clinical diagnosis and improve treatment outcomes. Despite the numerous methods available for medical question and answering, they tend to overlook the data generation mechanism’s imbalance and the pseudo-correlation caused by the task’s text characteristics. This pseudo-correlation is due to the fact that many words in the question and answering task are irrelevant to the answer but carry significant weight. These words can affect the feature representation and establish a false correlation with the final answer. Furthermore, the data imbalance mechanism can cause the model to blindly follow a large number of classes, leading to bias in the final answer. Confounding factors, including the data imbalance mechanism, bias due to textual characteristics, and other unknown factors, may also mislead the model and limit its performance.In this study, we propose a new counterfactual-based approach that includes a feature encoder and a counterfactual decoder. The feature encoder utilizes ChatGPT and label resetting techniques to create counterfactual data, compensating for distributional differences in the dataset and alleviating data imbalance issues. Moreover, the sampling prior to label resetting also helps us alleviate the data imbalance issue. Subsequently, label resetting can yield better and more balanced counterfactual data. Additionally, the construction of counterfactual data aids the subsequent counterfactual classifier in better learning causal features. The counterfactual decoder uses counterfactual data compared with real data to optimize the model and help it acquire the causal characteristics that genuinely influence the label to generate the final answer. The proposed method was tested on PubMedQA, a medical dataset, using machine learning and deep learning models. The comprehensive experiments demonstrate that this method achieves state-of-the-art results and effectively reduces the false correlation caused by confounders.     

面向新产品开发领域知识表示方法的比较研究

 分别介绍了几种常用的知识表示方法,在讨论了知识表示方法选择所应考虑的因素后,对这些方法进行了综合比较,分别指出了其优缺点。进而提出了利用本体来表示新产品开发领域的知识以解决其知识共享和知识重用的问题,并对其优势进行了分析。