Linked Open Data Sources Research Articles

Open data dissemination at Eurostat: State of the art

Eurostat is charged with providing high-quality statistics for Europe, and the current information landscape is making it increasingly challenging to do so. This paper presents the Eurostat dissemination approach (including the traditional dissemination vectors), and thereafter proceeds to present the recent initiatives to make European statistics data and metadata available in the form of Linked Open Data (LOD). After presenting some of the main challenges for open data dissemination (complete reproducibility, availability of high-quality LOD, capacity to consume LOD and achieving meaningful mashups between official statistics LOD and other data sources), it concludes by noting the potential of LOD to foster transparency, reproducibility, collaboration, interdisciplinary research driving scientific advancements, and contributing to a broader understanding of complex scientific challenges.

Instance level analysis on linked open data connectivity for cultural heritage entity linking and data integration

In cultural heritage, many projects execute Named Entity Linking (NEL) through global Linked Open Data (LOD) references in order to identify and disambiguate entities in their local datasets. It allows users to obtain extra information and contextualise the data with it. Thus, the aggregation and integration of heterogeneous LOD are expected. However, such development is still limited partly due to data quality issues. In addition, analysis on the LOD quality has not sufficiently been conducted for cultural heritage. Moreover, most research on data quality concentrates on ontology and corpus level observations. This paper examines the quality of the eleven major LOD sources used for NEL in cultural heritage with an emphasis on instance-level connectivity and graph traversals. Standardised linking properties are inspected for 100 instances/entities in order to create traversal route maps. Other properties are also assessed for quantity and quality. The outcomes suggest that the LOD is not fully interconnected and centrally condensed; the quantity and quality are unbalanced. Therefore, they cast doubt on the possibility of automatically identifying, accessing, and integrating known and unknown datasets. This implies the need for LOD improvement, as well as the NEL strategies to maximise the data integration.

Sobre el enriquecimiento semántico de materias en un entorno Linked Open data

Se describe el principal objetivo de la aplicación de estrategias de reconciliación y enriquecimiento semántico de materias: ofrecer al usuario, siguiendo la filosofía de la Web semántica, posibles vías de consulta de temáticas análogas en otros sistemas de información ubicados en diferentes puntos de la Red. Estas técnicas de reconciliación y enriquecimiento deben conformar un procedimiento mixto de operaciones manuales, semiautomáticas y automáticas en las que participarán diferentes programas, aplicaciones y protocolos que permitirán el tratamiento de grandes conjuntos de datos con un coste final asumible por las instituciones culturales. Su aplicación se enmarca en una serie de directrices de buenas prácticas y recomendaciones, que van desde el W3C a Europeana, pasando por los principios FAIR. Se analizan algunos de los vocabularios de valores de materias españoles disponibles en Linked Open Data por sus relaciones multilingües y se señala la importancia de la disponibilidad de servicios de reconciliación normalizados para seleccionar las fuentes Linked Open Data para la reconciliación y enriquecimiento semántico. Se concluye que los sistemas de información de instituciones culturales deben disponer de funciones que involucren el uso de registros de autoridad de materia enriquecidos en la búsqueda, la navegación, la visualización y la exportación.

Rethinking Authority Reconciliation Process

Entity identity management and name reconciliation are intrinsic to both Linked Open Data (LOD) and traditional library authority control. Does this mean that LOD sources can facilitate authority control? This Emblematica Online case study examines the utility of five LOD sources for name reconciliation, comparing design differences regarding ontologies, linking models, and entity properties. It explores the challenges of name reconciliation in the LOD environment and provides lessons learned during a semi-automated name reconciliation process. It also briefly discusses the potential values and benefits of LOD authorities to the authority reconciliation process itself and library services in general.

A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage

Recently, many Resource Description Framework (RDF) data generation tools have been developed to convert geospatial and non-geospatial data into RDF data. Furthermore, there are several interlinking frameworks that find semantically equivalent geospatial resources in related RDF data sources. However, many existing Linked Open Data sources are currently sparsely interlinked. Also, many RDF generation and interlinking frameworks require a solid knowledge of Semantic Web and Geospatial Semantic Web concepts to successfully deploy them. This article comparatively evaluates features and functionality of the current state-of-the-art geospatial RDF generation tools and interlinking frameworks. This evaluation is specifically performed for cultural heritage researchers and professionals who have limited expertise in computer programming. Hence, a set of criteria has been defined to facilitate the selection of tools and frameworks. In addition, the article provides a methodology to generate geospatial cultural heritage RDF data and to interlink it with the related RDF data. This methodology uses a CIDOC Conceptual Reference Model (CRM) ontology and interlinks the RDF data with DBpedia. Although this methodology has been developed for cultural heritage researchers and professionals, it may also be used by other domain professionals.

RDF2Vec: RDF graph embeddings and their applications

Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs.We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs.We evaluate our approach on three different tasks: (i) standard machine learning tasks, (ii) entity and document modeling, and (iii) content-based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that pre-computed feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.

Enhancing cultural recommendations through social and linked open data

In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user’s current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

A Rule-Based Transducer for Querying Incompletely Aligned Datasets

A growing number of Linked Open Data sources (from diverse provenances and about different domains) that can be freely browsed and searched to find and extract useful information have been made available. However, access to them is difficult for different reasons. This study addresses access issues concerning heterogeneity. It is common for datasets to describe the same or overlapping domains while using different vocabularies. Our study presents a transducer that transforms a SPARQL query suitably expressed in terms of the vocabularies used in a source dataset into another SPARQL query suitably expressed for a target dataset involving different vocabularies. The transformation is based on existing alignments between terms in different datasets. Whenever the transducer is unable to produce a semantically equivalent query because of the scarcity of term alignments, the transducer produces a semantic approximation of the query to avoid returning the empty answer to the user. Transformation across datasets is achieved through the management of a wide range of transformation rules. The feasibility of our proposal has been validated with a prototype implementation that processes queries that appear in well-known benchmarks and SPARQL endpoint logs. Results of the experiments show that the system is quite effective in achieving adequate transformations.

Linked open data-based framework for automatic biomedical ontology generation

BackgroundFulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns.ResultsOur evaluation shows improved results in most of the tasks of ontology generation compared to those obtained by existing frameworks. We evaluated the performance of individual tasks (modules) of proposed framework using CDR and SemMedDB datasets. For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively. Additionally, the comparison with manually constructed baseline Alzheimer ontology shows F-measure of 72.48% in terms of concepts detection, 76.27% in relation extraction, and 83.28% in property extraction. Also, we compared our proposed framework with ontology-learning framework called “OntoGain” which shows that LOD-ABOG performs 14.76% better in terms of relation extraction.ConclusionThis paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle.

LOD for Data Warehouses: Managing the Ecosystem Co-Evolution

For more than 30 years, data warehouses (DWs) have attracted particular interest both in practice and in research. This success is explained by their ability to adapt to their evolving environment. One of the last challenges for DWs is their ability to open their frontiers to external data sources in addition to internal sources. The development of linked open data (LOD) as external sources is an excellent opportunity to create added value and enrich the analytical capabilities of DWs. However, the incorporation of LOD in the DW must be accompanied by careful management. In this paper, we are interested in managing the evolution of DW systems integrating internal and external LOD datasets. The particularity of LOD is that they contribute to evolving the DW at several levels: (i) source level, (ii) DW schema level, and (iii) DW design-cycle constructs. In this context, we have to ensure this co-evolution, as conventional evolution approaches are adapted neither to this new kind of source nor to semantic constructs underlying LOD sources. One way of tackling this co-evolution issue is to ensure the traceability of DW constructs for the whole design cycle. Our approach is tested using: the LUBM (Lehigh University BenchMark), different LOD datasets (DBepedia, YAGO, etc.), and Oracle 12c database management system (DBMS) used for the DW deployment.

SAFE: SPARQL Federation over RDF Data Cubes with Access Control

BackgroundSeveral query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through access control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data.ResultsWe present SAFE: a query federation engine that enables policy-aware access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where access control is coupled with source selection, user profiles and their access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter access control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change.ConclusionsWe validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

Exploring LOD through metadata extraction and data-driven visualizations

Purpose – The purpose of this paper is to present a new approach toward automatically visualizing Linked Open Data (LOD) through metadata analysis. Design/methodology/approach – By focussing on the data within a LOD dataset, the authors can infer its structure in a much better way than current approaches, generating more intuitive models to progress toward visual representations. Findings – With no technical knowledge required, focussing on metadata properties from a semantically annotated dataset could lead to automatically generated charts that allow to understand the dataset in an exploratory manner. Through interactive visualizations, users can navigate LOD sources using a natural approach, in order to save time and resources when dealing with an unknown resource for the first time. Research limitations/implications – This approach is suitable for available SPARQL endpoints and could be extended for resource description framework dumps loaded locally. Originality/value – Most works dealing with LOD visualization are customized for a specific domain or dataset. This paper proposes a generic approach based on traditional data visualization and exploratory data analysis literature.

Applying Linked Data Technologies in the Social Sciences

In recent years, Linked Open Data (LOD) has matured and gained acceptance across various communities and domains. Large potential of Linked Data technologies is seen for an application in scientific disciplines. In this article, we present use cases and applications for an application of Linked Data in the social sciences. They focus on (a) interlinking domain-specific information, and (b) linking social science data to external LOD sources (e.g. authority data) from other domains. However, several technical and research challenges arise, when applying Linked Data technologies to a scientific domain with its specific data, information needs and use cases. We discuss these challenges and show how they can be addressed.

Semantic sensitive tensor factorization

The ability to predict the activities of users is an important one for recommender systems and analyses of social media. User activities can be represented in terms of relationships involving three or more things (e.g. when a user tags items on a webpage or tweets about a location he or she visited). Such relationships can be represented as a tensor, and tensor factorization is becoming an increasingly important means for predicting users' possible activities. However, the prediction accuracy of factorization is poor for ambiguous and/or sparsely observed objects. Our solution, Semantic Sensitive Tensor Factorization (SSTF), incorporates the semantics expressed by an object vocabulary or taxonomy into the tensor factorization. SSTF first links objects to classes in the vocabulary (taxonomy) and resolves the ambiguities of objects that may have several meanings. Next, it lifts sparsely observed objects to their classes to create augmented tensors. Then, it factorizes the original tensor and augmented tensors simultaneously. Since it shares semantic knowledge during the factorization, it can resolve the sparsity problem. Furthermore, as a result of the natural use of semantic information in tensor factorization, SSTF can combine heterogeneous and unbalanced datasets from different Linked Open Data sources. We implemented SSTF in the Bayesian probabilistic tensor factorization framework. Experiments on publicly available large-scale datasets using vocabularies from linked open data and a taxonomy from WordNet show that SSTF has up to 12% higher accuracy in comparison with state-of-the-art tensor factorization methods.

The KnowledgeStore

Although the quantity of structured information on the Web and within organizations is increasing, the majority of information remains available only in unstructured form. While different in form, both unstructured and structured information sources provide information about entities in the world and their properties and relations; still, frameworks for their seamless integration have not been deeply investigated. In this paper the authors describe the KnowledgeStore, a scalable, fault-tolerant, and Semantic Web grounded open-source storage system for interlinking structured and unstructured data. They present the concept, design, function and implementation of the system, and report on its concrete usage in three application scenarios within the NewsReader EU project, where it stores and supports the querying of millions of news articles interlinked with millions of RDF triples extracted from text and imported from Linked Open Data sources. The authors report on data population and data retrieval performances of the system measured through a number of experiments, and they also discuss the practical issues and lessons learned from these experiences.

MONTAGE: CREATING SELF-POPULATING DOMAIN ONTOLOGIES FROM LINKED OPEN DATA

Domain-specific ontologies have become integral components of numerous semantic- and knowledge-based applications. However, creating such ontologies and populating them with correct individuals is a difficult and time-consuming process. Recently, a vast amount of knowledge has become available as part of the Linked Open Data (LOD) project, which includes data sets in multiple areas. In this paper, we present mOntage, a novel ontology design and population framework, which allows a domain expert to easily define a domain ontology schema and specify the ontology's classes and properties in terms of the subsets of the existing LOD data sources. The classes and properties of the ontology being created can be defined either directly, in terms of existing LOD-available classes and properties, or can be newly constructed by the domain expert. The definitions, called maps, are encoded as part of the ontology itself, effectively converting it into a self-populating ontology. Finally, a dedicated software system automatically populates the ontology with instances obtained from the selected LOD sources by executing suitable SPARQL queries. We illustrate our framework by creating Cancer Treatment ontology in the area of biomedicine.

Interactive faceted search and exploration of open social media data on a touchscreen mobile phone

A central challenge of semantic ambient media applications is designing smart user interfaces that are able to dynamically deal with an a-priori unknown number of data categories and data instances received live from different Linked Open Data sources while at the same time being intuitive and easy to use. In the mobile world, this challenge is even more difficult as the mobile devices have limited interaction possibilities and smaller display size. In this paper, we tackle this challenge and present the user-centered, iterative design of a mobile application for faceted search and exploration of a large, multi-dimensional data set of open social media on a touchscreen mobile phone. The application is called Mobile Facets and provides live retrieval and interactive search and exploration of resources like places, persons, organizations, and events originating from different, integrated social media sources like DBpedia, Eventful, Upcoming, Flickr, and GeoNames. In contrast to existing work, we do not know in advance the number and type of data categories and data instances that will be received as the data is queried live from the sources. While developing Mobile Facets, we have applied a participatory design with a small group of five users. For the final prototype we have conducted a task-based, formative evaluation with 12 additional subjects to investigate the applicability and usability of our Mobile Facets application.