Search results

The algorithm of disjoint literature‐based knowledge discovery provides a convenient, efficient and effective auxiliary method for scientific research. Based on an analysis of Swanson's A‐B‐C model of disjoint literature‐based knowledge discovery and Gordon's intermediate literature theory, this paper seeks to propose a more comprehensive compound correlation model for disjoint literature‐based knowledge discovery.

A new algorithm of vector space model (VSM) based disjoint literature‐based knowledge discovery is designed to implement the compound correlation model.

The validity tests showed that this new model not only simulated both of Swanson's early and well‐known discoveries of Raynaud's disease‐fish oil and migraine‐magnesium connections successfully, but also applied to knowledge discovery in the agricultural economics literature in the Chinese language.

Although the workload was reduced to the minimum under the compound correlation model compared with other algorithms and models, part of the work needed some manual intervention in the process of disjoint literature‐based knowledge discovery with the VSM‐based compound correlation model.

The algorithm was capable of knowledge discovery with a large‐scale dataset and had an advantage in identifying a series of hidden connections among a set of literatures. Therefore, application of the model might be extended to more fields.

Traditional two‐step knowledge discovery procedures were integrated into the model, which contained open and closed disjoint literature‐based knowledge discovery.

Examines the relationship between systems and their users from the knowledge discovery perspective. Recently knowledge discovery in databases has made important progress, but it may also bring some potential problems to database design, such as issues related to database security, as an unauthorized user may derive highly sensitive knowledge from unclassified data. There is a need for a comprehensive study on knowledge discovery in human‐computer symbiosis. Borrowing terms from algorithm design and artificial intelligence literature, proposes a notion called database‐user adversarial partnership. This notion is general enough to cover various knowledge discovery and security issues related to databases and their users. Furthermore, the notion of database‐user adversarial partnership can be further generalized into system‐user adversarial partnership. Opportunities provided by knowledge discovery techniques and potential social implications are discussed and illustrated by examples.

The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics.

This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1).

The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment.

This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

The purpose of this paper is to synthesize existing research on research data management (RDM), academic scholarship and knowledge management and provide a conceptual framework for an institutional research data management support-system (RDMSS) for systems development, managerial and academic use.

Viewing RDMSS from multiple theoretical perspectives, including data management, knowledge management, academic scholarship and the practice-based perspectives of knowledge and knowing, this paper conceptually explores the systems’ elements needed in the development of an institutional RDM service by considering the underlying data discovery and application issues, as well as the nature of academic scholarship and knowledge creation, discovery, application and sharing motivations in a university environment.

The paper provides general criteria for an institutional RDMSS framework. It suggests that RDM in universities is at the very heart of the knowledge life cycle and is a central ingredient to the academic scholarships of discovery, integration, teaching, engagement and application.

This is a conceptual exploration and as a result, the research findings may lack generalisability. Researchers are therefore encouraged to further empirically examine the proposed propositions.

The broad RDMSS framework presented in this paper can be compared with the actual situation at universities and eventually guide recommendations for adaptations and (re)design of the institutional RDM infrastructure and knowledge discovery services environment. Moreover, this paper will help to address some of the identified underlying scholarship and RDM disciplinary divides and confusion constraining the effective functioning of the modern day university’s RDM and data discovery environment.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Purpose – To resent approaches and some research results of various research areas contributing to knowledge discovery from different sources, different data forms, on different scale, and for different purpose. Design/methodology/approach – Contribute to knowledge management by applying knowledge discovery approaches to enable computer search for the relevant knowledge whereas the humans give just broad directions. Findings – Knowledge discovery techniques proved to be very appropriate for many problems related to knowledge management. Surprisingly, it is often the case that already relatively simple approaches provide valuable results. Research limitations/implications – Still there are many open problems and scalability issues that arise when dealing with real‐world data and especially in the areas involving text and network analysis. Practical implications – Each problem should be handled with care, taking into account different aspects and selecting/extending the most appropriate available methods or developing some new approaches. Originality/value – This paper provides an interesting collection of selected knowledge discovery methods applied in different context but all contributing in some way to knowledge management. Several of the reported approaches were developed in collaboration with the authors of the paper with especial emphases on their usability for practical problems involving knowledge management.

Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process.

The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation.

Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development).

The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners.

This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories.

This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.

Despite growth in use of inter-organisational relationships for knowledge co-creation, many collaborations struggle to realise the synergistic benefits of these networks. This paper aims to explore the evolving dialectic tensions evident within an inter-organisational relationship and the governance consideration to optimise the knowledge process.

A longitudinal case of a university-industry knowledge network is selected for study. The single case analysis aligns with the dialectical epistemology, which dismisses the expectation of homogeny or constancy across network cases.

The research highlights the circular condition between dialectic tensions evident within inter-organisational relations and the governance mechanisms developed to synthesis the network knowledge discovery capability. The research shows that these tensions are a natural part of the network existence and often advantageous to knowledge creation. The research also highlights that governance is required at multiple levels within the network entity to optimise knowledge exchange and discovery.

The research adds to the limited application of dialectical thinking to inter-organisational networks. It highlights the structural and relational governance mechanisms that interplay to optimise their knowledge process capability. The research also highlights the multiple levels within networks at which tensions can originate, requiring knowledge governance at the micro, meso and macro level to address the complexity of the inter-organisational relationship. This research provides a better understanding of how knowledge within inter-organisational relations can be managed for mutual benefit and value creation.

The purpose of this paper is to focus on the distinction between smart specialisation and smart specialisation policy and it studies under what conditions a smart specialisation policy is necessary.

A conceptual framework is built based on historical evidence of successful dynamics of structural changes at regional level qualified as “smart specialisation”. The identification of market and coordination failures that are likely to impede the occurrence of spontaneous process of smart specialisation makes a good case for a smart specialisation policy.

The paper highlights important design principles for the policy process that should help to minimise potential risks of policy failures and policy capture.

The paper does assess the effect of smart specialisation on innovation and growth at regional level because it is too early to observe and measure effects. The paper confines itself to conjectures about the effects of such a policy.

The paper makes recommendations and explains some of the practicalities about the implementation of the policy at regional level.

The paper is one of the first dealing with the topic of smart specialisation policy.