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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.

Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

For companies that intend to respond to the modern conscious consumers' needs, a great competitive advantage is played on the ability to incorporate sustainability messages in marketing communications. The aim of this paper is to address this important priority in the web context, building a semantic algorithm that allows content managers to evaluate the quality of sustainability web contents for search engines, considering the current semantic web development.

Following the Design Science (DS) methodological approach, the study develops the algorithm as an artefact capable of solving a practical problem and improving the operation of content managerial process.

The algorithm considers multiple factors of evaluation, grouped in three parameters: completeness, clarity and consistency. An applicability test of the algorithm was conducted on a sample of web pages of the Google blog on sustainability to highlight the correspondence between the established evaluation factors and those actually used by Google.

Studying content marketing for sustainability communication constitutes a new field of research that offers exciting opportunities. Writing sustainability contents in an effective way is a fundamental step to trigger stakeholder engagement mechanisms online. It could be a positive social engineering technique in the hands of marketers to make web users able to pursue sustainable development in their choices.

This is the first study that creates a theoretical connection between digital content marketing and sustainability communication focussing, especially, on the aspects of search engine optimization (SEO). The algorithm of “Sustainability-contents SEO” is the first operational software tool, with a regulatory nature, that is able to analyse the web contents, detecting the terms of the sustainability language and measuring the compliance to SEO requirements.

There are many academic contributions dealing with the impact of additive manufacturing on supply chains (Ben-Ner and Siemsen, 2017; Durach, 2017; Gravier and Roethlein, 2018; Brown, 2018; Rogers et al., 2016; Sasson and Johnson, 2016; Nyman and Sarlin, 2014). But how future supply chain design may differ from today is still vague. In this article, possible scenarios are discussed and decision support is provided for the management, which is responsible for long-term strategic decisions.

This papers introduces the general characteristics of additive manufacturing and its next steps of development. Based on these technological assumptions various scenarios are systematically derived applying the standardized nomenclature of SCOR-model. Resulting threats and chances will be discussed and finally brought to a conclusion.

With the spread of additive manufacturing, the industry has the opportunity to pursue completely new approaches in terms of product development, design and product properties. This not only leads to new competitive models and the possibility of customer individualization of the products down to volume “1”. In addition, there are new models for supply chain management that can be used to react quickly and flexibly to customer requests. Already today new approaches for the cooperation between partners play an essential role.For start-ups, market entry should be simplified by using the resulting opportunities.

Future developments and especially the development speed of additive manufacturing are not predictable. Therefore, the expected scenarios may differ from reality and lead to a different supply chain design. There will also be industries that can use additive manufacturing much more intensively than others – not least because of the technological restrictions of the manufacturing process. Corporate culture and the overcoming of technical challenges are a decisive factor.

This paper gives supply chain management an outlook on future development opportunities. This enables management to set the right course for a future-oriented position today.

The changes in the supply chain will open up new business models while existing models will disappear. This leads to a change in the field of logistics but also for many technology providers. As a consequence, there will be serious changes (opportunities and risks) for the employees involved and their working environment.

This paper enables management to understand the scope and impact of upcoming changes. In this way, it significantly promotes awareness-raising and contributes to the future-oriented proceeding of companies.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

The purpose of this paper is to reuse learning resources from course module and forum discussion in ODL settings and structure it with ontological representation.

Thus, an ontology is designed by extending simple knowledge organization system specification to structure the learning resources. Furthermore, a semantic forum system is proposed as a front end mechanism to represent the ontological structure designed for the learner to easily access, search and navigate the relevant knowledge of interest. In addition, this study evaluates the effectiveness of the proposed system along with three variables, namely, learners’ perceptions, system design perceptions and system content perceptions. Accordingly, a close-ended online survey was developed and administered to 74 online learners.

The findings demonstrate positive perceptions of the proposed system which is based on ontological representation as an effective learning system that is able to enhance the understanding of courses taught.

This paper presents an ontological structure approach to add meaning to the learning resources, indexed in such a way that it can be reused, searched, processed and shared.

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

This paper aims to describe the “InDO: Institute Demographic Ontology” and demonstrates the InDO-based semiautomated process for both generating and extending a knowledge graph to provide a comprehensive resource for marginalized US graduate students. The knowledge graph currently consists of instances related to the semistructured National Science Foundation Survey of Earned Doctorates (NSF SED) 2019 analysis report data tables. These tables contain summary statistics of an institute’s doctoral recipients based on a variety of demographics. Incorporating institute Wikidata links ultimately produces a table of unique, clearly readable data.

The authors use a customized semantic extract transform and loader (SETLr) script to ingest data from 2019 US doctoral-granting institute tables and preprocessed NSF SED Tables 1, 3, 4 and 9. The generated InDO knowledge graph is evaluated using two methods. First, the authors compare competency questions’ sparql results from both the semiautomatically and manually generated graphs. Second, the authors expand the questions to provide a better picture of an institute’s doctoral-recipient demographics within study fields.

With some preprocessing and restructuring of the NSF SED highly interlinked tables into a more parsable format, one can build the required knowledge graph using a semiautomated process.

The InDO knowledge graph allows the integration of US doctoral-granting institutes demographic data based on NSF SED data tables and presentation in machine-readable form using a new semiautomated methodology.

This paper aims to focus on research regarding organizational learning (OL) and knowledge management (KM), and to specifically investigate whether OL has been conceptually absorbed by KM.

This study is based on 16,185 articles from the Scopus and ISI Web of Science databases, using VantagePoint 10.0 software. The method used in this study is a systematic literature review covering KM and OL publications from the 1970s, when the OL field started to grow, up to 2016.

Nuclear processes of OL, creation and acquisition of knowledge, have been conceptually absorbed by KM literature in the past years.

Only two databases have been considered, Scopus and ISI Web of Science, because of their academic prestige. However, these databases include a large number of articles on KM and OL. Search terms used could exclude some relevant terms, although all major descriptive terms have been included.

This paper identifies thematic clusters in KM and OL, evolution of both fields, most cited authors and representative journals by topic.

This is the first paper to jointly analyse the evolution of KM and OL. This paper shows a conceptual absorption of OL into KM, which may enrich academic discussion and also provide some clarity to the conceptualization of these two fields.