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.     

概念知识地图协同创作过程中的群体决策模型

概念知识地图可以作为知识表达的基础,采用协同创作的方法可以很好地构建概念知识地图。本文介绍了概念知识地图的主体协同创作过程,建立了群体决策模型。根据有限理性理论,对主体决策能力和主体间的交流互动进行了分析。运用调查问卷的方法获取仿真参数,对某科研组织概念知识地图协同创作过程进行了仿真,验证了该模型的有效性。研究结果表明,要获得稳定的协作结果需要一定的时间;知识积累对协作具有正向促进作用;协作结果对主体的初始认知情况具有敏感性。本研究可为协同创作规则的制定和系统的设计提供参考。     

Organisational knowledge creation strategies: A conceptual framework

The main purposes of this article are to identify the dimensions of organisational knowledge creation strategies (EICE model: exploration, institutional entrepreneurship, combination, exploitation) and to clarify the relationship between the organisational knowledge creation strategies and its knowledge asset. For these purposes, guided by knowledge-based view and knowledge creation theory, we propose an EICE model and explore its effect on the knowledge asset. We further suggest the managerial implications and theoretical implications. The main managerial implication is that the knowledge creation strategies model may help manager to make effective strategies for knowledge creation within the organisation. The main contributions to organisation theory are extending Nonaka's knowledge creation theory to form a new strategic model for knowledge creation.     

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.     

跨国公司内部网络结点之间知识转移的影响因素分析——一个概念模型

根据企业的知识基础理论和网络组织理论,跨国公司可以被看作在全球范围内积累和运营知识的内外部网络的统一体。有效地在跨国公司内部网络结点之间进行知识的共享和转移,是跨国公司在更大范围内扩展其竞争优势的必要前提之一。跨国公司各内部网络结点所积累的国别性专有知识产生于其所在国特定的社会政治、经济和文化背景之下,因此,跨边界知识转移的有效性必然受到一系列与此相关因素的影响,本文分析了这些因素的影响,并给出一个概念模型。     

Towards conceptual clarity for ‘tacit knowledge’: a review of empirical studies

Tacit knowledge is important for organizations and management, but we lack adequate theory, and find conflicting claims about the concept. A review of empirical studies of tacit knowledge phenomena found the term has been applied to both articulable and inarticulable knowledge. It is suggested that in the interests of clarity use of the term should be limited to the latter. Tacit knowledge appears due to both experience with the particular objects it is applied to, and to general experiences. A solution to the problem of tacit knowledge explication is proposed, and implications of the study for both managerial and academic practice are outlined.

     

Query Generation and Buffer Mechanism: Towards a better conversational agent for legal case retrieval

In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. In practice, a suitable workflow can provide guidelines for constructing a machine-mediated agent replacing of human agents. Therefore, we conduct a comparison analysis and summarize two challenges when directly applying the conversational agent workflow in web search to legal case retrieval: (1) It is complex for agents to express their understanding of users’ information need. (2) Selecting a candidate case from the SERPs is more difficult for agents, especially at the early stage of the search process. To tackle these challenges, we propose a suitable conversational agent workflow in legal case retrieval, which contains two additional key modules compared with that in web search: Query Generation and Buffer Mechanism. A controlled user experiment with three control groups, using the whole workflow or removing one of these two modules, is conducted. The results demonstrate that the proposed workflow can actually support conversational agents working more efficiently, and help users save search effort, leading to higher search success and satisfaction for legal case retrieval. We further construct a large-scale dataset and provide guidance on the machine-mediated conversational search system for legal case retrieval.     

地理距离、法律制度临近与国际知识扩散模式

以41个国家对3个创新领先国的专利引用数据为基础,采用非线性混合回归模型研究知识扩散的地理比邻效应和制度临近模式。研究结果表明:国际知识溢出不仅具有地理比邻特征,且法律起源制度具有制度临近效应,具有相同的法律起源的国家之间具有更高的知识扩散强度;而知识产权保护的制度临近对知识溢出没有影响。进一步的研究还表明,地理因素和法律制度因素在知识扩散中不具有互补效应和替代效应,说明知识扩散所表现出的地理比邻和制度临近效应通过不同扩散机制展开,未来有必要对制度比邻的渠道开展进一步的深化研究。最后文章根据研究结果提出了相关政策建议。     

Protecting knowledge: How legal requirements to reveal information affect the importance of secrecy

Most firms use secrecy to protect their knowledge from potential imitators. However, the theoretical foundations for secrecy have not been well explored. We extend knowledge protection literature and propose theoretical mechanisms explaining how information visibility influences the importance of secrecy as a knowledge protection instrument. Building on mechanisms from information economics and signaling theory, we postulate that secrecy is more important for protecting knowledge for firms that have legal requirements to reveal information to shareholders. Furthermore, we argue that this effect is contingent on the location in a technological cluster, on a firm’s investment in fixed assets and on a firm’s past innovation performance. We test our hypotheses using a representative sample of 683 firms in Germany between 2005 and 2013. Our results support the moderation effect of a technological cluster and a firm’s investment in fixed assets. Our findings inform both academics and managers on how firms balance information disclosure requirements with the use of secrecy as a knowledge protection instrument.     

A usage study of retrieval modalities for video shot retrieval

As an information medium, video offers many possible retrieval and browsing modalities, far more than text, image or audio. Some of these, like searching the text of the spoken dialogue, are well developed, others like keyframe browsing tools are in their infancy, and others not yet technically achievable. For those modalities for browsing and retrieval which we cannot yet achieve we can only speculate as to how useful they will actually be, but we do not know for sure. In our work we have created a system to support multiple modalities for video browsing and retrieval including text search through the spoken dialogue, image matching against shot keyframes and object matching against segmented video objects. For the last of these, automatic segmentation and tracking of video objects is a computationally demanding problem which is not yet solved for generic natural video material, and when it is then it is expected to open up possibilities for user interaction with objects in video, including searching and browsing. In this paper we achieve object segmentation by working in a closed domain of animated cartoons. We describe an interactive user experiment on a medium-sized corpus of video where we were able to measure users’ use of video objects versus other modes of retrieval during multiple-iteration searching. Results of this experiment show that although object searching is used far less than text searching in the first iteration of a user’s search it is a popular and useful search type once an initial set of relevant shots have been found.     

A common interface for accessing document retrieval systems and DBMS for retrieval of bibliographic data

Due to their ready availability, database management systems are being applied to bibliographic databases with increasing frequency. This is being done in spite of the fact that although DBMS query languages tend to be very powerful, they are far too complex for the casual user. It is proposed that PSI, an existing virtual-system intermediary for document retrieval systems, be extended to include access to DBMS containing bibliographic data in order to circumvent the complexity problem or the casual user. PSI currently provides a common command language for access to multiple document retrieval systems. It is shown that PSI could be extended to provide this same command language to access DBMS, whether the DBMS are relational or network.     

Contextualization models for XML retrieval

In a hierarchical XML structure, surrounding elements form the context of an XML element. In document-oriented XML, the context is a part of the semantics of the element and augments its textual information. The process of taking the context of the element into account in element scoring is called contextualization. This study extends the concept of contextualization and presents a classification of contextualization models. In an XML collection, elements are of different granularity, i.e. lower level elements are shorter and carry less textual information. Thus, it seems credible that contextualization interacts differently with diverse elements. Even if it is known that contextualization leads to improved effectiveness in element retrieval, the improvement on different granularity levels has not been investigated. This study explores the effect of contextualization on these levels. Further, a parameterized framework for testing contextualization is presented.     

基于知识管理的隐性知识显性化研究

本文主要讨论了隐性知识的内涵、特征，分析了傀性知识显性化的主要障碍，提出了隐性知识显性化的模型以及措施。     

A framework for conceptual modeling in OOP

There are several language constructs and mechanisms that provide some sort of support for conceptual modeling in object-oriented programming: the Liskov substitution principle, the Meyer programming by contract, the Beta inner construct, interfaces in C# and Java, and the separation of subtype hierarchies from subclass hierarchies as in Timor and Sather. All these mechanisms and constructs are powerful and useful tools to enforce the conceptual modeling trait of the inheritance mechanism in object-oriented programming. Their purpose is to ensure semantic compatibility of classes related by an inheritance hierarchy. When applied independently, these mechanisms can lead to more correct inheritance hierarchies that are easy to understand, use, and extend. This article discusses the different mechanisms and studies the interaction between them. It investigates whether conceptual modeling can be satisfactorily achieved by using such tools. It will be shown that the interaction between these tools might lead to contradictions and might prevent legitimate inheritance hierarchies. This article proposes a framework for a conceptual modeling mechanism that will better support conceptual modeling at the language level.     

Predicting associated statutes for legal problems

Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although a few previous studies focused on assisting professionals in the retrieval of related legal documents, to our knowledge, no previous studies could provide relevant statutes to the general public using problem statements. In this work, we design a text mining based method, the three-phase prediction (TPP) algorithm, which allows the general public to use everyday vocabulary to describe their problems and find pertinent statutes for their cases. The experimental results indicate that our approach can help the general public, who are not familiar with professional legal terms, to acquire relevant statutes more accurately and effectively.     

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.     

Ontology refinement for improved information retrieval

Ontologies are frequently used in information retrieval being their main applications the expansion of queries, semantic indexing of documents and the organization of search results. Ontologies provide lexical items, allow conceptual normalization and provide different types of relations. However, the optimization of an ontology to perform information retrieval tasks is still unclear. In this paper, we use an ontology query model to analyze the usefulness of ontologies in effectively performing document searches. Moreover, we propose an algorithm to refine ontologies for information retrieval tasks with preliminary positive results.