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。     

Knowledge graph embedding model with attention-based high-low level features interaction convolutional network

Knowledge graphs are sizeable graph-structured knowledge with both abstract and concrete concepts in the form of entities and relations. Recently, convolutional neural networks have achieved outstanding results for more expressive representations of knowledge graphs. However, existing deep learning-based models exploit semantic information from single-level feature interaction, potentially limiting expressiveness. We propose a knowledge graph embedding model with an attention-based high-low level features interaction convolutional network called ConvHLE to alleviate this issue. This model effectively harvests richer semantic information and generates more expressive representations. Concretely, the multilayer convolutional neural network is utilized to fuse high-low level features. Then, features in fused feature maps interact with other informative neighbors through the criss-cross attention mechanism, which expands the receptive fields and boosts the quality of interactions. Finally, a plausibility score function is proposed for the evaluation of our model. The performance of ConvHLE is experimentally investigated on six benchmark datasets with individual characteristics. Extensive experimental results prove that ConvHLE learns more expressive and discriminative feature representations and has outperformed other state-of-the-art baselines over most metrics when addressing link prediction tasks. Comparing MRR and Hits@1 on FB15K-237, our model outperforms the baseline ConvE by 13.5% and 16.0%, respectively.     

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).     

Representation of legal knowledge for conceptual retrieval

The law is a near perfect application area for knowledge representation. Legal knowledge representation is needed in conceptual legal information retrieval systems and in legal reasoning systems. We review the knowledge representation aspects of four such systems: Waterman and Peterson's Legal Decisionmaking System, Hafner's Legal Information Retrieval System, McCarty's TAXMAN, and the deBessonet representation of the Louisiana Civil Code (CCLIPS).     

Generating knowledge aware explanation for natural language inference

Natural language inference (NLI) is an increasingly important task of natural language processing, and the explainable NLI generates natural language explanations (NLEs) in addition to label prediction, to make NLI explainable and acceptable. However, NLEs generated by current models often present problems that disobey of commonsense or lack of informativeness. In this paper, we propose a knowledge enhanced explainable NLI framework (KxNLI) by leveraging Knowledge Graph (KG) to address these problems. The subgraphs from KG are constructed based on the concept set of the input sequence. Contextual embedding of input and the graph embedding of subgraphs, is used to guide the NLE generation by using a copy mechanism. Furthermore, the generated NLEs are used to augment the original data. Experimental results show that the performance of KxNLI can achieve state-of-the-art (SOTA) results on the SNLI dataset when the pretrained model is fine-tuned on the augmented data. Besides, the proposed mechanism of knowledge enhancement and rationales utilization can achieve ideal performance on vanilla seq2seq model, and obtain better transfer ability when transferred to the MultiNLI dataset. In order to comprehensively evaluate generated NLEs, we design two metrics from the perspectives of the accuracy and informativeness, to measure the quality of NLEs, respectively. The results show that KxNLI can provide high quality NLEs while making accurate prediction.     

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.     

Division of labour and sharing of knowledge for synchronous collaborative information retrieval

Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR. Finally we conclude with a discussion on some possible future trends for these two coordination techniques.     

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.     

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.     

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.     

Building a K-Pop knowledge graph using an entertainment ontology

Recently, the Korean popular (K-Pop) music industry has grown into a popular subculture among teenagers and young adults worldwide, which resulted in widespread interest in the fashion and style of idolised Korean singers and groups. Although English social media websites provide some content related to K-Pop, these websites lack diversity and rapid updating of information compared to local Korean websites. This study introduces a K-Pop knowledge graph, which is the basis for describing various objects and their relationships. All contents of the knowledge graph can be distributed and shared across various applications. To do so, this study proposes a semantic data model to represent a comprehensive profile for singers and groups, their activities, organisations and entertainment content. The knowledge graph is created by aggregating a set of relevant datasets from various data sources. In addition, Gnosis, which is a news application, demonstrates how this knowledge graph can be used in a real-world service.     

Learning a merge model for multilingual information retrieval

This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we present a number of features that may influence the MLIR merging process. These features are mainly extracted from three levels: query, document, and translation. After the feature extraction, we then use the FRank ranking algorithm to construct a merge model. To the best of our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. In our experiments, three test collections for the task of crosslingual information retrieval (CLIR) in NTCIR3, 4, and 5 are employed to assess the performance of our proposed method. Moreover, several merging methods are also carried out for a comparison, including traditional merging methods, the 2-step merging strategy, and the merging method based on logistic regression. The experimental results show that our proposed method can significantly improve merging quality on two different types of datasets. In addition to the effectiveness, through the merge model generated by FRank, our method can further identify key factors that influence the merging process. This information might provide us more insight and understanding into MLIR merging.     

An IPC-based vector space model for patent retrieval

Determining requirements when searching for and retrieving relevant information suited to a user’s needs has become increasingly important and difficult, partly due to the explosive growth of electronic documents. The vector space model (VSM) is a popular method in retrieval procedures. However, the weakness in traditional VSM is that the indexing vocabulary changes whenever changes occur in the document set, or the indexing vocabulary selection algorithms, or parameters of the algorithms, or if wording evolution occurs. The major objective of this research is to design a method to solve the afore-mentioned problems for patent retrieval. The proposed method utilizes the special characteristics of the patent documents, the International Patent Classification (IPC) codes, to generate the indexing vocabulary for presenting all the patent documents. The advantage of the generated indexing vocabulary is that it remains unchanged, even if the document sets, selection algorithms, and parameters are changed, or if wording evolution occurs. Comparison of the proposed method with two traditional methods (entropy and chi-square) in manual and automatic evaluations is presented to verify the feasibility and validity. The results also indicate that the IPC-based indexing vocabulary selection method achieves a higher accuracy and is more satisfactory.     

Reinforcement learning with dynamic completion for answering multi-hop questions over incomplete knowledge graph

Text-enhanced and implicit reasoning methods are proposed for answering questions over incomplete knowledge graph (KG), whereas prior studies either rely on external resources or lack necessary interpretability. This article desires to extend the line of reinforcement learning (RL) methods for better interpretability and dynamically augment original KG action space with additional actions. To this end, we propose a RL framework along with a dynamic completion mechanism, namely Dynamic Completion Reasoning Network (DCRN). DCRN consists of an action space completion module and a policy network. The action space completion module exploits three sub-modules (relation selector, relation pruner and tail entity predictor) to enrich options for decision making. The policy network calculates probability distribution over joint action space and selects promising next-step actions. Simultaneously, we employ the beam search-based action selection strategy to alleviate delayed and sparse rewards. Extensive experiments conducted on WebQSP, CWQ and MetaQA demonstrate the effectiveness of DCRN. Specifically, under 50% KG setting, the Hits@1 performance improvements of DCRN on MetaQA-1H and MetaQA-3H are 2.94% and 1.18% respectively. Moreover, under 30% and 10% KG settings, DCRN prevails over all baselines by 0.9% and 1.5% on WebQSP, indicating the robustness to sparse KGs.     

P企业知识团队的生成及知识创新的模型与机制/P P/FONT /P

企业知识团队是企业知识创新的重要载体。本文首先分析了两种不同企业知识团队--“同血型”和“混血型”知识团队的生成过程,并建立了生成模型。在此基础上,分析了两类企业知识团队知识创新的机制,认为是由互馈机制、弥补机制、催化机制、保障机制、协同演化机制及控制机制组合而成,并建立了两类企业知识团队知识创新的模型。     

A reputation-based model for mobile agent migration for information search and retrieval

Mobile agent technology has been used in various applications including e-commerce, information processing, distributed network management, and database access. Information search and retrieval can be conducted by mobile agents in a decentralized system. As compared with the client/server model, the mobile agent approach has an advantage of saving network bandwidth and offering flexibility in information search and retrieval. In this paper, we present a model for mobile agents to select the most reputable information host to search and retrieve information. We use opinion-based belief structure to represent, aggregate and calculate the reputation of an information host. Since reputation is a multi-faced concept, our approach first allows the users to rank each information host's quality of service based on a set of evaluation categories. Then, a comprehensive, final reputation of the host is obtained by aggregating those specific category reputations. To recognize the subjective nature of a reputation, the transferable belief model is used to represent and rank the category reputation. Experiments are conducted using the Aglets technology to illustrate mobile agent migration.     

RelaGraph: Improving embedding on small-scale sparse knowledge graphs by neighborhood relations

Learning a continuous dense low-dimensional representation of knowledge graphs (KGs), known as knowledge graph embedding (KGE), has been viewed as the key to intelligent reasoning for deep learning (DL) and gained much attention in recent years. To address the problem that the current KGE models are generally ineffective on small-scale sparse datasets, we propose a novel method RelaGraph to improve the representation of entities and relations in KGs by introducing neighborhood relations. RelaGraph extends the neighborhood information during entity encoding, and adds the neighborhood relations to mine deeper level of graph structure information, so as to make up for the shortage of information in the generated subgraph. This method can well represent KG components in a vector space in a way that captures the structure of the graph, avoiding underlearning or overfitting. KGE based on RelaGraph is evaluated on a small-scale sparse graph KMHEO, and the MRR reached 0.49, which is 34 percentage points higher than that of the SOTA methods, as well as it does on several other datasets. Additionally, the vectors learned by RelaGraph is used to introduce DL into several KG-related downstream tasks, which achieved excellent results, verifying the superiority of KGE-based methods.     

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.