LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

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

Linked education: interlinking educational resources and the web of data

Research on interoperability of technology-enhanced learning (TEL) repositories throughout the last decade has led to a fragmented landscape of competing approaches, such as metadata schemas and interface mechanisms. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de-facto standard for sharing data on the Web and offers a large potential to solve interoperability issues in the field of TEL. In this paper, we describe a general approach to exploit the wealth of already existing TEL data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain. This approach has been implemented in the context of the mEducator project where data from a number of open TEL data repositories has been integrated, exposed and enriched by following Linked Data principles

Increasing information feed in the process of structural steel design

Research initiatives throughout history have shown how a designer typically makes associations and references to a vast amount of knowledge based on experiences to make decisions. With the increasing usage of information systems in our everyday lives, one might imagine an information system that provides designers access to the ‘architectural memories’ of other architectural designers during the design process, in addition to their own physical architectural memory. In this paper, we discuss how the increased adoption of semantic web technologies might advance this idea. We investigate to what extent information can be described with these technologies in the context of structural steel design. This investigation indicates significant possibilities regarding information reuse in the process of structural steel design and, by extent, in other design contexts as well. However, important obstacles and question remarks can still be outlined as well

Interlinking educational data to web of data

With the proliferation of educational data on the Web, publishing and interlinking eLearning resources have become an important issue nowadays. Educational resources are exposed under heterogeneous Intellectual Property Rights (IPRs) in different times and formats. Some resources are implicitly related to each other or to the interest, cultural and technical environment of learners. Linking educational resources to useful knowledge on the Web improves resource seeking. This becomes crucial for moving from current isolated eLearning repositories towards an open discovery space, including distributed resources irrespective of their geographic and system boundaries. Linking resources is also useful for enriching educational content, as it provides a richer context and other related information to both educators and learners. On the other hand, the emergence of the so-called "Linked Data" brings new opportunities for interconnecting different kinds of resources on the Web of Data. Using the Linked Data approach, data providers can publish structured data and establish typed links between them from various sources. To this aim, many tools, approaches and frameworks have been built to first expose the data as Linked Data formats and to second discover the similarities between entities in the datasets. The research carried out for this PhD thesis assesses the possibilities of applying the Linked Open Data paradigm to the enrichment of educational resources. Generally speaking, we discuss the interlinking educational objects and eLearning resources on the Web of Data focusing on existing schemas and tools. The main goals of this thesis are thus to cover the following aspects: -- Exposing the educational (meta)data schemas and particularly IEEE LOM as Linked Data -- Evaluating currently available interlinking tools in the Linked Data context -- Analyzing datasets in the Linked Open Data cloud, to discover appropriate datasets for interlinking -- Discussing the benefits of interlinking educational (meta)data in practice

Extending the design process into the knowledge of the world

Research initiatives throughout history have shown how a designer typically makes associations and references to a vast amount of knowledge based on experiences to make decisions. With the increasing usage of information systems in our everyday lives, one might imagine an information system that provides designers access to the ‘architectural memories’ of other architectural designers during the design process, in addition to their own physical architectural memory. In this paper, we discuss how the increased adoption of semantic web technologies might advance this idea. We briefly discuss how such a semantic web of building information can be set up, and how this can be linked to a wealth of information freely available in the Linked Open Data (LOD) cloud

SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge

Measuring Accuracy of Triples in Knowledge Graphs

An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples

Technical evaluation of the mEducator 3.0 linked data-based environment for sharing medical educational resources

mEducator 3.0 is a content sharing approach for medical education, based on Linked Data principles. Through standardization, it enables sharing and discovery of medical information. Overall the mEducator project seeks to address the following two different approaches, mEducator 2.0, based on web 2.0 and ad-hoc Application Programmers Interfaces (APIs), and mEducator 3.0, which builds upon a collection of Semantic Web Services that federate existing sources of medical and Technology Enhanced Learning (TEL) data. The semantic mEducator 3.0 approach It has a number of different instantiations, allowing flexibility and choice. At present these comprise of a standalone social web-based instantiation (MetaMorphosis+) and instantiations integrated with Drupal, Moodle and OpenLabyrinth systems. This paper presents the evaluation results of the mEducator 3.0 Linked Data based environment for sharing medical educational resources and focuses on metadata enrichment, conformance to the requirements and technical performance (of the MetaMorphosis+ and Drupal instantiations)