A Semantic Web Approach to Heterogeneous Metadata Integration

[[abstract]]Heterogeneous metadata integration is an issue in digital libraries. Mapping is often used for an integrated metadata access, but the implicit knowledge and relations embedded in metadata are ignored. This paper aims to present a semantic web approach to heterogeneous metadata integration of biodiversity repositories. First, implicit knowledge and relations in metadata are extracted out and transformed into a shared ontology with expression of RDF and OWL languages. Next the shared ontology plays an inter-lingua role in harmonizing heterogeneous metadata to achieve an ontology mapping with a unified view. Then the shared ontology is expressed by SWRL for inference query to offer in-depth semantic discovery. Finally four question answering oriented queries are employed to examine the feasibility of the shared ontology for heterogeneous metadata integration.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

A linked data-driven & service-oriented architecture for sharing educational resources

The two fundamental aims of managing educational resources are to enable resources to be reusable and interoperable and to enable Web-scale sharing of resources across learning communities. Currently, a variety of approaches have been proposed to expose and manage educational resources and their metadata on the Web. These are usually based on heterogeneous metadata standards and schemas, such as IEEE LOM or ADL SCORM, and diverse repository interfaces such as OAI-PMH or SQI. Also, there is still a lack of usage of controlled vocabularies and available data sets that could replace the widespread use of unstructured text for describing resources. On the other hand, the Linked Data approach has proven that it offers a set of successful principles that have the potential to alleviate the aforementioned issues. In this paper, we introduce an architecture and prototype which is fundamentally based on (a) Linked Data principles and (b) Service-orientation to resolve the integration issues for sharing educational resources

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

Training Neural Language Models with SPARQL queries for Semi-Automatic Semantic Mapping

Abstract Knowledge graphs are labeled and directed multi-graphs that encode information in the form of entities and relationships. They are gaining attention in different areas of computer science: from the improvement of search engines to the development of virtual personal assistants. Currently, an open challenge in building large-scale knowledge graphs from structured data available on the Web (HTML tables, CSVs, JSONs) is the semantic integration of heterogeneous data sources. In fact, such diverse and scattered information rarely provide a formal description of metadata that is required to accomplish the integration task. In this paper we propose an approach based on neural networks to reconstruct the semantics of data sources to produce high quality knowledge graphs in terms of semantic accuracy. We developed a neural language model trained on a set of SPARQL queries performed on knowledge graphs. Through this model it is possible to semi-automatically generate a semantic map between the attributes of a data source and a domain ontology

An Infrastructure for acquiring high quality semantic metadata

Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a erification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation omparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata