Forecasting the Spreading of Technologies in Research Communities

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution

Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution

An Approach to Publish Statistics from Open-Access Journals Using Linked Data Technologies

Semantic Web encourages digital libraries which include open access journals, to collect, link and share their data across the web in order to ease its processing by machines and humans to get better queries and results. Linked Data technologies enable connecting structured data across the web using the principles and recommendations set out by Tim Berners-Lee in 2006. Several universities develop knowledge, through scholarship and research, under open access policies and use several ways to disseminate information. Open access journals collect, preserve and publish scientific information in digital form using a peer review process. The evaluation of the usage of this kind of publications needs to be expressed in statistics and linked to external resources to give better information about the resources and their relationships. The statistics expressed in a data mart facilitate queries about the history of journals usage by several criteria. This data linked to another datasets gives more information such as: the topics in the research, the origin of the authors, the relation to the national plans, and the relations about the study curriculums. This paper reports a process for publishing an open access journal data mart on the Web using Linked Data technologies in such a way that it can be linked to related datasets. Furthermore, methodological guidelines are presented with related activities. The proposed process was applied extracting statistical data from a university open journal system and publishing it in a SPARQL endpoint using the open source edition of the software OpenLink Virtuoso. In this process the use of open standards facilitates the creation, development and exploitation of knowledge. The RDF Data Cube vocabulary has been used as a model for publishing the multidimensional data on the Web. The visualization was made using CubeViz a faceted browser filtering observations to be presented interactively in charts. The proposed process help to publish statistical datasets in an easy way.This work has been partially supported by the Prometeo Project by SENESCYT, Ecuadorian Government

An Approach to Publish Scientific Data of Open-Access Journals Using Linked Data Technologies

Semantic Web encourages digital libraries, including open access journals, to collect, link and share their data across the Web in order to ease its processing by machines and humans to get better queries and results. Linked Data technologies enable connecting related data across the Web using the principles and recommendations set out by Tim Berners-Lee in 2006. Several universities develop knowledge through scholarship and research with open access policies for the generated knowledge, using several ways to disseminate information. Open access journals collect, preserve and publish scientific information in digital form related to a particular academic discipline in a peer review process having a big potential for exchanging and spreading their data linked to external resources using Linked Data technologies. Linked Data can increase those benefits with better queries about the resources and their relationships. This paper reports a process for publishing scientific data on the Web using Linked Data technologies. Furthermore, methodological guidelines are presented with related activities. The proposed process was applied extracting data from a university Open Journal System and publishing in a SPARQL endpoint using the open source edition of OpenLink Virtuoso. In this process, the use of open standards facilitates the creation, development and exploitation of knowledge.This research has been partially supported by the Prometeo project by SENESCYT, Ecuadorian Government and by CEDIA (Consorcio Ecuatoriano para el Desarrollo de Internet Avanzado) supporting the project: “Platform for publishing library bibliographic resources using Linked Data technologies”

TechMiner: Extracting Technologies from Academic Publications

In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision

Transforming Library Catalogs into Linked Data

Traditionally, in most digital library environments, the discovery of resources takes place mostly through the harvesting and indexing of the metadata content. Such search and retrieval services provide very effective ways for persons to find items of interest but lacks the ability to lead users looking for potential related resources or to make more complex queries. In contrast, modern web information management techniques related to Semantic Web, a new form of the Web, encourages institutions, including libraries, to collect, link and share their data across the web in order to ease its processing by machines and humans offering better queries and results increasing the visibility and interoperability of the data. Linked Data technologies enable connecting related data across the Web using the principles and recommendations set out by Tim Berners-Lee in 2006, resulting on the use of URIs (Uniform Resource Identifier) as identifiers for objects, and the use of RDF (Resource Description Framework) for links representation. Today, libraries are giving increasing importance to the Semantic Web in a variety of ways like creating metadata models and publishing Linked Data from authority files, bibliographic catalogs, digital projects information or crowdsourced information from another projects like Wikipedia. This paper reports a process for publishing library metadata on the Web using Linked Data technologies. The proposed process was applied for extracting metadata from a university library, representing them in RDF format and publishing them using a Sparql endpoint (an interface to a knowledge database). The library metadata from a subject were linked to external sources such us another libraries and then related to the bibliography from syllabus of the courses in order to discover missing subjects and new or out of date bibliography. In this process, the use of open standards facilitates the exploitation of knowledge from libraries.This research has been partially supported by the Prometeo project by SENESCYT, Ecuadorian Government, by CEDIA (Consorcio Ecuatoriano para el Desarrollo de Internet Avanzado) supporting the project: “Platform for publishing library bibliographic resources using Linked Data technologies” and by the project GEODAS-BI (TIN2012-37493-C03-03) supported by the Ministry of Economy and Competitiveness of Spain (MINECO)

The Impact on Citation Analysis Based on Ontology and Linked Data

Research focus: The aim of this chapter is to introduce a new citation analysis and service framework based on the semantic web technologies (e.g., ontology and linked data). Research methods: This research project is based on a review of relevant literature and a series of experimental results based on ontology and linked data. Motivation: Traditional citation analysis methods and tools are overly dependent on citation databases, and traditional citation information service may ignore the semantics of knowledge resources and lack ability to store and query data in a machine-readable mode. Findings: The findings underline that the new citation analysis and service system framework based on ontology and linked data are feasible, which can integrate information requirements and knowledge services, and provide users with more personalized and comprehensive services

Publishing a Scorecard for Evaluating the Use of Open-Access Journals Using Linked Data Technologies

Open access journals collect, preserve and publish scientific information in digital form, but it is still difficult not only for users but also for digital libraries to evaluate the usage and impact of this kind of publications. This problem can be tackled by introducing Key Performance Indicators (KPIs), allowing us to objectively measure the performance of the journals related to the objectives pursued. In addition, Linked Data technologies constitute an opportunity to enrich the information provided by KPIs, connecting them to relevant datasets across the web. This paper describes a process to develop and publish a scorecard on the semantic web based on the ISO 2789:2013 standard using Linked Data technologies in such a way that it can be linked to related datasets. Furthermore, methodological guidelines are presented with activities. The proposed process was applied to the open journal system of a university, including the definition of the KPIs linked to the institutional strategies, the extraction, cleaning and loading of data from the data sources into a data mart, the transforming of data into RDF (Resource Description Framework), and the publication of data by means of a SPARQL endpoint using the OpenLink Virtuoso application. Additionally, the RDF data cube vocabulary has been used to publish the multidimensional data on the web. The visualization was made using CubeViz a faceted browser to present the KPIs in interactive charts.This work has been partially supported by the Prometeo Project by SENESCYT, Ecuadorian Government

Automatic Classification of Springer Nature Proceedings with Smart Topic Miner

The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this process is typically carried out manually by expert editors, leading to high costs and slow throughput. In this paper we present Smart Topic Miner (STM), a novel solution which uses semantic web technologies to classify scholarly publications on the basis of a very large automatically generated ontology of research areas. STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family. It analyses in real time a set of publications provided by an editor and produces a structured set of topics and a number of Springer Nature Classification tags, which best characterise the given input. In this paper we present the architecture of the system and report on an evaluation study conducted with a team of Springer Nature editors. The results of the evaluation, which showed that STM classifies publications with a high degree of accuracy, are very encouraging and as a result we are currently discussing the required next steps to ensure large-scale deployment within the company

Challenges as enablers for high quality linked data: Insights from the semantic publishing challenge

While most challenges organized so far in the Semantic Web domain are focused on comparing tools with respect to different criteria such as their features and competencies, or exploiting semantically enriched data, the Semantic Web Evaluation Challenges series, co-located with the ESWC Semantic Web Conference, aims to compare them based on their output, namely the produced dataset. The Semantic Publishing Challenge is one of these challenges. Its goal is to involve participants in extracting data from heterogeneous sources on scholarly publications, and producing Linked Data that can be exploited by the community itself. This paper reviews lessons learned from both (i) the overall organization of the Semantic Publishing Challenge, regarding the definition of the tasks, building the input dataset and forming the evaluation, and (ii) the results produced by the participants, regarding the proposed approaches, the used tools, the preferred vocabularies and the results produced in the three editions of 2014, 2015 and 2016. We compared these lessons to other Semantic Web Evaluation Challenges. In this paper, we (i) distill best practices for organizing such challenges that could be applied to similar events, and (ii) report observations on Linked Data publishing derived from the submitted solutions. We conclude that higher quality may be achieved when Linked Data is produced as a result of a challenge, because the competition becomes an incentive, while solutions become better with respect to Linked Data publishing best practices when they are evaluated against the rules of the challenge