基于本体的电子政务知识共享机制研究

政府中大部分的知识是隐性知识,如何把公务员的隐性知识显性化,有效的知识管理显得很重要.在借鉴"百度知道和百度百科"知识共享原理的基础上,通过电子政务知识管理系统,创建一个公务员分享隐性知识经验的平台.结合对电子政务主题词表、本体映射表、政务本体库的综合运用,对政务知识库进行规范化处理,为公务员共享政务知识提供更加规范有效的途径.     

An efficiency relation-specific graph transformation network for knowledge graph representation learning

Knowledge graph representation learning (KGRL) aims to infer the missing links between target entities based on existing triples. Graph neural networks (GNNs) have been introduced recently as one of the latest trendy architectures serves KGRL task using aggregations of neighborhood information. However, current GNN-based methods have fundamental limitations in both modelling the multi-hop distant neighbors and selecting relation-specific neighborhood information from vast neighbors. In this study, we propose a new relation-specific graph transformation network (RGTN) for the KGRL task. Specifically, the proposed RGTN is the first pioneer model that transforms a relation-based graph into a new path-based graph by generating useful paths that connect heterogeneous relations and multi-hop neighbors. Unlike the existing GNN-based methods, our approach is able to adaptively select the most useful paths for each specific relation and to effectively build path-based connections between unconnected distant entities. The transformed new graph structure opens a new way to model the arbitrary lengths of multi-hop neighbors which leads to more effective embedding learning. In order to verify the effectiveness of our proposed model, we conduct extensive experiments on three standard benchmark datasets, e.g., WN18RR, FB15k-237 and YAGO-10-DR. Experimental results show that the proposed RGTN achieves the promising results and even outperforms other state-of-the-art models on the KGRL task (e.g., compared to other state-of-the-art GNN-based methods, our model achieves 2.5% improvement using H@10 on WN18RR, 1.2% improvement using H@10 on FB15k-237 and 6% improvement using H@10 on YAGO3-10-DR).     

Hotness prediction of scientific topics based on a bibliographic knowledge graph

As a part of innovation in forecasting, scientific topic hotness prediction plays an essential role in dynamic scientific topic assessment and domain knowledge transformation modeling. To improve the topic hotness prediction performance, we propose an innovative model to estimate the co-evolution of scientific topic and bibliographic entities, which leverages a novel dynamic Bibliographic Knowledge Graph (BKG). Then, one can predict the topic hotness by using various kinds of topological entity information, i.e., TopicRank, PaperRank, AuthorRank, and VenueRank, along with pre-trained node embedding, i.e., node2vec embedding, and different pooling techniques. To validate the proposed method, we constructed a new BKG by using 4.5 million PubMed Central publications plus MeSH (Medical Subject Heading) thesaurus and witnessed the essential prediction improvement with extensive experiment outcomes over 10 years observations.     

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.     

基于多Agent和Ontology的虚拟企业知识管理系统模型研究

针对虚拟企业知识管理对分布性、异构性、安全性、智能性等各方面的要求,构建基于多Agent和Ontology的知识管理系统模型,并详述虚拟企业与成员企业中的多个Agent是如何相互协作完成一项知识请求的过程.     

Cascade embedding model for knowledge graph inference and retrieval

Knowledge graphs are widely used in retrieval systems, question answering systems (QA), hypothesis generation systems, etc. Representation learning provides a way to mine knowledge graphs to detect missing relations; and translation-based embedding models are a popular form of representation model. Shortcomings of translation-based models however, limits their practicability as knowledge completion algorithms. The proposed model helps to address some of these shortcomings.The similarity between graph structural features of two entities was found to be correlated to the relations of those entities. This correlation can help to solve the problem caused by unbalanced relations and reciprocal relations. We used Node2vec, a graph embedding algorithm, to represent information related to an entity's graph structure, and we introduce a cascade model to incorporate graph embedding with knowledge embedding into a unified framework. The cascade model first refines feature representation in the first two stages (Local Optimization Stage), and then uses backward propagation to optimize parameters of all the stages (Global Optimization Stage). This helps to enhance the knowledge representation of existing translation-based algorithms by taking into account both semantic features and graph features and fusing them to extract more useful information. Besides, different cascade structures are designed to find the optimal solution to the problem of knowledge inference and retrieval.The proposed model was verified using three mainstream knowledge graphs: WIN18, FB15K and BioChem. Experimental results were validated using the hit@10 rate entity prediction task. The proposed model performed better than TransE, giving an average improvement of 2.7% on WN18, 2.3% on FB15k and 28% on BioChem. Improvements were particularly marked where there were problems with unbalanced relations and reciprocal relations. Furthermore, the stepwise-cascade structure is proved to be more effective and significantly outperforms other baselines.     

Building ontology-based knowledge maps to assist knowledge process outsourcing decisions

Organizational knowledge exists in different types of knowledge retainers. Efforts are being made to preserve this knowledge because of its value to the organization. In this paper we present a methodology for codifying the knowledge of a domain. This methodology is based on an ontology for the domain in question, from which different types of knowledge items are extracted. These knowledge items represent the different types of knowing that are embedded in the organization's structure and its processes. From an analysis of a process instance described in the ontology, different knowledge items can be extracted and represented as knowledge maps. These maps represent the internal competencies of the organization as they relate to certain processes and hence they can be used to provide inputs in the decision-making process, for example, knowledge process outsourcing decisions. The purpose of this paper is to present an ontology-driven methodology for extracting different knowledge items and representing them as knowledge maps.     

科研档案管理知识图谱构建研究

为推动科研档案精细化管理与智能化服务,基于已有的档案本体系统和标准,继承利用EAD、DCMI、VIVO、SWRC、Schema.org等现有较为通用的本体,与科研档案为中心的知识单元进行集成、关联和融合,应用编辑工具Protégé、建模语言OWL建立了计算机可理解的科研档案知识图谱语义模型,丰富并规范了科研档案中蕴含知识单元及其语义关系,主要包括7个核心类、8个一级对象属性及21个数据属性,为科研档案的精细化加工、语义化描述组织与智能化应用服务等提供了基础。     

Research of Chinese intangible cultural heritage knowledge graph construction and attribute value extraction with graph attention network

The development of digital technology promotes the construction of the Intangible cultural heritage (ICH) database but the data is still unorganized and not linked well, which makes the public hard to master the overall knowledge of the ICH. An ICH knowledge graph (KG) can help the public to understand the ICH and facilitate the protection of the ICH. However, a general framework of ICH KG construction is lacking now. In this study, we take the Chinese ICH (nation-level) as an example and propose a framework to build a Chinese ICH KG combining multiple data sources from Baike and the official website, which can extend the scale of the KG. Besides, the data of ICH grows daily, requiring us to design an efficient model to extract the knowledge from the data to update the KG in time. The built KG is based on the triple 〈entity, attribute, attribute value〉 and we introduce the attribute value extraction (AVE) task. However, the public Chinese ICH annotated AVE corpus is lacking. To solve that, we construct a Chinese ICH AVE corpus based on the Distant Supervision (DS) automatically rather than employing traditional manual annotation. Currently, AVE is usually seen as the sequence tagging task. In this paper, we take the ICH AVE as a node classification task and propose an AVE model BGC, combining the BiLSTM and graph attention network, which can fuse and utilize the word-level and character-level information by means of the ICH lexicon generated from the KG. We conduct extensive experiments and compare the proposed model with other state-of-the-art models. Experimental results show that the proposed model is of superiority.     

A systematic study of knowledge graph analysis for cross-language plagiarism detection

Cross-language plagiarism detection aims to detect plagiarised fragments of text among documents in different languages. In this paper, we perform a systematic examination of Cross-language Knowledge Graph Analysis; an approach that represents text fragments using knowledge graphs as a language independent content model. We analyse the contributions to cross-language plagiarism detection of the different aspects covered by knowledge graphs: word sense disambiguation, vocabulary expansion, and representation by similarities with a collection of concepts. In addition, we study both the relevance of concepts and their relations when detecting plagiarism. Finally, as a key component of the knowledge graph construction, we present a new weighting scheme of relations between concepts based on distributed representations of concepts. Experimental results in Spanish–English and German–English plagiarism detection show state-of-the-art performance and provide interesting insights on the use of knowledge graphs.     

A neural knowledge graph evaluator: Combining structural and semantic evidence of knowledge graphs for predicting supportive knowledge in scientific QA

Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing, however, their focus was more on semantics than on graph structure. This study, by contrast, takes a special interest in exploring the structural patterns of knowledge graphs. Inspired by human cognitive processes, we propose novel methods of feature extraction for capturing the local and global structural information of knowledge graphs. These features not only exhibit good indicative power, but can also facilitate text analysis with explainable meanings. Moreover, aiming to better combine structural and semantic evidence for prediction, we propose a Neural Knowledge Graph Evaluator (NKGE) which showed superior performance over existing methods. Our contributions include a novel set of interpretable structural features and the effective NKGE for compatibility evaluation between knowledge graphs. The methods of feature extraction and the structural patterns indicated by the features may also provide insights for related studies in computational modeling and processing of knowledge.     

Towards travel recommendation interpretability: Disentangling tourist decision-making process via knowledge graph

Understanding tourists’ decision-making processes, in which many factors ranging from functional attributes to geographical configurations are highly intertwined, has long been a crux for tourism management. Existing studies are typically based on manual surveys that extract the intricate psychological or behavioural mechanisms, but the huge expense of the required samplings limits the generalization and comprehensiveness of the findings. This study proposes a novel explainable recommendation method—Knowledge-Graph-aware Disentangled Auto-Encoder (KGDAE)—to automatically unravel the tourists’ decision processes from massive historical behaviour data. Based on the constructed tourism-KG that integrates multidimensional factors into 23 types of entities corresponding to 37 semantic and geographic relationships, KGDAE realizes a macro-micro supervised disentangled learning for the interaction of multiple determinants. Macroscopically, the hierarchical attention mechanisms are designed to distinguish the dominance of either functional or geographical factors, and capture the effect of the residential environment; microscopically, the preference-propagation-based technique is introduced to infer the fine-grained characteristics and relations of tourist interests on the tourism-KG. Extensive experiments show that KGDAE can effectively restore tourists’ decision processes according to two empirical studies while boosting the recommendation performance compared to multiple state-of-the-art methods with an increase of 1∼19%. Furthermore, the advantaged interpretability also guarantees the robustness of sparse recommendation scenario to achieve the lowest degradation at 7.8%.     

Causality-based CTR prediction using graph neural networks

As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention from both academia and industry. Recent studies have been reported to establish CTR prediction models in the graph neural networks (GNNs) framework. However, most of GNNs-based models handle feature interactions in a complete graph, while ignoring causal relationships among features, which results in a huge drop in the performance on out-of-distribution data. This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising. In our model, a structured representation learning method (GraphFwFM) is designed to capture high-order representations on feature graph based on causal discovery among field features in gated graph neural networks (GGNNs), and GraphSAGE is employed to obtain graph representations of users and ads. Experiments conducted on three public datasets demonstrate the superiority of Causal-GNN in AUC and Logloss and the effectiveness of GraphFwFM in capturing high-order representations on causal feature graph.     

Modeling of musculoskeletal structure and function using a modular bond graph approach

The dynamic effects of muscle strength, timing of muscle activations, and body geometry have been modeled for a wide variety of human activities. These types of models require the development of complex system equations that account for the effects of rigid-body dynamics, musculotendon actuators, passive and active resistance to motion, and other physiological structures. One way in which model refinement can be expedited is through the use of bond graph modeling techniques. While bond graph techniques have been used extensively in a broad variety of applications, they have been used only sparingly in the field of biomechanics, despite the potential suitability of a modular, multidomain approach to the modeling of musculoskeletal function. In the current paper, bond graph modules representing muscle function and rigid-body motions of underlying bone structures are introduced. The system equations generated with the use of these models are equivalent to those developed with more traditional techniques, but the modules can be more easily used in conjunction with control models of neuromuscular function for the simulation of overall dynamic motor performance.     

基于知识网络平台的协同知识创新系统的构建

协同知识创新系统,是辅助知识网络成员实现知识的获取、存储和创新的虚拟知识网络环境。针对该方面的研究还处于定性研究阶段的现状,综合运用计算机网络技术、知识分类技术及智能协作技术,对协同知识创新系统进行了深入分析和讨论。设计了知识网络平台体系结构,构建了基于知识网络的协同知识创新系统体系架构,并依此设计了协同知识创新系统的虚拟知识网络工作环境。     

企业知识资本管理与知识库建设

本文集中论述了关于企业知识资本的概念、国外著名公司关于知识资本管理和知识库建设的宝贵经验和富集信息。