Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Librarie

Sample data processing in an additive and reproducible taxonomic workflow by using character data persistently linked to preserved individual specimens

We present the model and implementation of a workflow that blazes a trail in systematic biology for the re-usability of character data (data on any kind of characters of pheno- and genotypes of organisms) and their additivity from specimen to taxon level. We take into account that any taxon characterization is based on a limited set of sampled individuals and characters, and that consequently any new individual and any new character may affect the recognition of biological entities and/or the subsequent delimitation and characterization of a taxon. Taxon concepts thus frequently change during the knowledge generation process in systematic biology. Structured character data are therefore not only needed for the knowledge generation process but also for easily adapting characterizations of taxa. We aim to facilitate the construction and reproducibility of taxon characterizations from structured character data of changing sample sets by establishing a stable and unambiguous association between each sampled individual and the data processed from it. Our workflow implementation uses the European Distributed Institute of Taxonomy Platform, a comprehensive taxonomic data management and publication environment to: (i) establish a reproducible connection between sampled individuals and all samples derived from them; (ii) stably link sample-based character data with the metadata of the respective samples; (iii) record and store structured specimen-based character data in formats allowing data exchange; (iv) reversibly assign sample metadata and character datasets to taxa in an editable classification and display them and (v) organize data exchange via standard exchange formats and enable the link between the character datasets and samples in research collections, ensuring high visibility and instant re-usability of the data. The workflow implemented will contribute to organizing the interface between phylogenetic analysis and revisionary taxonomic or monographic work

From innovation to diversification: a simple competitive model

Few attempts have been proposed in order to describe the statistical features and historical evolution of the export bipartite matrix countries/products. An important standpoint is the introduction of a products network, namely a hierarchical forest of products that models the formation and the evolution of commodities. In the present article, we propose a simple dynamical model where countries compete with each other to acquire the ability to produce and export new products. Countries will have two possibilities to expand their export: innovating, i.e. introducing new goods, namely new nodes in the product networks, or copying the productive process of others, i.e. occupying a node already present in the same network. In this way, the topology of the products network and the country-product matrix evolve simultaneously, driven by the countries push toward innovation.Comment: 8 figures, 8 table

A gap in competencies or in capabilities?: the role of regional universities in developing scientific and technological skills in Campania

A gap in competencies or in capabilities?: the role of regional universities in developing scientific and technological skills in Campania The paper assesses the role of universities in resolving the STEM (Science, Technology, Engineering and Mathematics) skills gap in the Campania region of Southern Italy. The results are shown to hinge on a doubled supply/demand model, involving a first upstream stage (logically if not chronologically) of derived demands for and supplies of STEM-based skill development within universities, and a second downstream stage of the usage of these skills in industrial firms. The main objective of this work is to re-examine the role of conventional ‘knowledge capital’ arguments for the role of universities in development processes in catching-up regions of the EU – i.e. human capital and R&D capital, or what will be identified here as ‘competencies’ – as against what we refer to as ‘capabilities’ arguments, reflected here in better ways in which universities might adapt to the actual needs of industry for highly skilled workers and research outcomes. The results suggest that the STEM skills gap is not clearly a deficiency just in capabilities, but more so in the links between capabilities and competencies. Moreover, the STEM universities are trying to feed the interaction with industry, however it is still left mostly to the personal relationships of the professors or their administrative counterparts, e.g. head of the T&T office, and/or to placement. Key words: Derived demand and supply, STEM subjects, Mezzogiorno region, skills gap, competencies and capabilities.

Climate Change and Biosphere Response: Unlocking the Collections Vault

Natural history collections (NHCs) are an important source of the long-term data needed to understand how biota respond to ongoing anthropogenic climate change. These include taxon occurrence data for ecological modeling, as well as information that can be used to reconstruct mechanisms through which biota respond to changing climates. The full potential of NHCs for climate change research cannot be fully realized until high-quality data sets are conveniently accessible for research, but this requires that higher priority be placed on digitizing the holdings most useful for climate change research (e.g., whole-biota studies, time series, records of intensively sampled common taxa). Natural history collections must not neglect the proliferation of new information from efforts to understand how present-day ecosystems are responding to environmental change. These new directions require a strategic realignment for many NHC holders to complement their existing focus on taxonomy and systematics. To set these new priorities, we need strong partnerships between NHC holders and global change biologists

Drivers and Impacts of R&D Adoption on Transport and Logistics Services

Actually, technologies and applications in industries are changing via business restructuring, new business models, new knowledge and supply chains. So R&D is not focused primarily on manufacturing industry as it used to be, but on different kinds of industries as logistics and transport (TLS). Nevertheless, the characteristics of the TLS industry determine the introduction of specific R&D solutions accordingly to sectors operations. The objective of this paper is to describe the R&D opportunities in the TLS industry and how managers use them to make their businesses more innovative and efficient. Using the Structure-Conduct-Performance (SCP) model the paper identifies the links between R&D adoption and innovation dynamics. Relating the findings, on the driver’s side there are three points that are worth mentioning: increasing market competition, the relationships of firms interacting with each other and the availability and quality of complementary assets such as employee skills and IT know-how. On the impacts’ side, firms advanced in terms of implementing R&D solutions are more likely to implement organizational changes. Finally, a set of recommendations on how to further improve the continuous innovation in the TLS industry is presented

Validity Issues in the Use of Social Network Analysis with Digital Trace Data

There is an exciting natural match between social network analysis methods and the growth of data sources produced by social interactions via information technologies, from online communities to corporate information systems. Information Systems researchers have not been slow to embrace this combination of method and data. Such systems increasingly provide “digital trace data” that provide new research opportunities. Yet digital trace data are substantively different from the survey and interview data for which network analysis measures and interpretations were originally developed. This paper examines 10 validity issues associated with the combination of digital trace data and social network analysis methods, with examples from the IS literature, to provide recommendations for improving the validity of future research

Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.Comment: 30 pages, 9 figures, 7 tables, no where submitted ye