25 Matching Annotations
  1. Sep 2020
    1. We analyze the structural elements which comprise a standard scientific paper. Previous analysis generally has focused on one element of a paper at a time. However, the title, author list, affiliation, abstract, text, tables, graphs, charts, photographs and references all represent possible data resources for investigation. After specifying those elements, we focus successively on the history, normative tradition, and sociological analysis of a selection of those elements.
    1. Scientific knowledge constitutes a complex system that has recently been the topic of in-depth analysis. Empirical evidence reveals that little is known about the dynamic aspects of human knowledge. Precise dissection of the expansion of scientific knowledge could help us to better understand the evolutionary dynamics of science. In this paper, we analyzed the dynamic properties and growth principles of the MEDLINE bibliographic database using network analysis methodology. The basic assumption of this work is that the scientific evolution of the life sciences can be represented as a list of co-occurrences of MeSH descriptors that are linked to MEDLINE citations. The MEDLINE database was summarized as a complex system, consisting of nodes and edges, where the nodes refer to knowledge concepts and the edges symbolize corresponding relations. We performed an extensive statistical evaluation based on more than 25 million citations in the MEDLINE database, from 1966 until 2014. We based our analysis on node and community level in order to track temporal evolution in the network. The degree distribution of the network follows a stretched exponential distribution which prevents the creation of large hubs. Results showed that the appearance of new MeSH terms does not also imply new connections. The majority of new connections among nodes results from old MeSH descriptors. We suggest a wiring mechanism based on the theory of structural holes, according to which a novel scientific discovery is established when a connection is built among two or more previously disconnected parts of scientific knowledge. Overall, we extracted 142 different evolving communities. It is evident that new communities are constantly born, live for some time, and then die. We also provide a Web-based application that helps characterize and understand the content of extracted communities. This study clearly shows that the evolution of MEDLINE knowledge correlates with the network’s structural and temporal characteristics.
    1. The NLM Catalog provides access to NLM bibliographic data for journals, books, audiovisuals, computer software, electronic resources and other materials. Links to the library's holdings in LocatorPlus, NLM's online public access catalog, are also provided.
  2. Aug 2020
    1. One way to think about "core" biodiversity data is as a network of connected entities, such as taxa, taxonomic names, publications, people, species, sequences, images, and collections that form the "biodiversity knowledge graph". Many questions in biodiversity informatics can be framed as paths in this graph. This article explores this futher, and sketches a set of services and tools we would need in order to construct the graph. New information In order to build a usable biodiversity knowledge graph we should adopt JSON-LD for biodiversity data, develop reconciliation services to match entities to identifiers, and a use a mixture of document and graph databases to store and query the data. To bootstrap this project we can create wrappers around each major biodiversity data provider, and a central cache that is both a document store and a simple graph database. This power of this approach should be showcased by applications that use the central cache to tackle specific problems, such as augmenting existing data.
    1. The success of distributed and semantic-enabled systems relies on the use of up-to-date ontologies and mappings between them. However, the size, quantity and dynamics of existing ontologies demand a huge maintenance effort pushing towards the development of automatic tools supporting this laborious task. This article proposes a novel method, investigating different types of similarity measures, to identify concepts’ attributes that served to define existing mappings. The obtained experimental results reveal that our proposed method allows to identify the relevant attributes for supporting mapping maintenance, since we found correlations between ontology changes affecting the identified attributes and mapping changes.
    1. As the amount of scholarly communication increases, it is increasingly difficult for specific core scientific statements to be found, connected and curated. Additionally, the redundancy of these statements in multiple fora makes it difficult to determine attribution, quality, and provenance. To tackle these challenges, the Concept Web Alliance has promoted the notion of nanopublications (core scientific statements with associated context). In this document, we present a model of nanopublications along with a NamedGraph/RDF serialization of the model. Importantly, the serialization is defined completely using already existing community developed technologies. Finally, we discuss the importance of aggregating nano-publications and the role that the ConceptWiki plays in facilitating it.
    1. SKOS—Simple Knowledge Organization System—provides a model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, folksonomies, and other similar types of controlled vocabulary. As an application of the Resource Description Framework (RDF), SKOS allows concepts to be composed and published on the World Wide Web, linked with data on the Web and integrated into other concept schemes. This document is a user guide for those who would like to represent their concept scheme using SKOS. In basic SKOS, conceptual resources (concepts) are identified with URIs, labeled with strings in one or more natural languages, documented with various types of note, semantically related to each other in informal hierarchies and association networks, and aggregated into concept schemes. In advanced SKOS, conceptual resources can be mapped across concept schemes and grouped into labeled or ordered collections. Relationships can be specified between concept labels. Finally, the SKOS vocabulary itself can be extended to suit the needs of particular communities of practice or combined with other modeling vocabularies. This document is a companion to the SKOS Reference, which provides the normative reference on SKOS.
  3. May 2020
  4. Mar 2020