Linked Open Data Repositories Research Articles

Towards a semantic approach in GLAM Labs: The case of the Data Foundry at the National Library of Scotland

GLAM organisations have been exploring the benefits of publishing their digital collections in a wide variety of forms since the 2000s. Many institutions, and in particular libraries, have applied the Semantic Web and Linked Data to their main catalogues. Recent advances in technology and innovative approaches concerning the reuse of the digital collections by means of computational access have paved the way for the creation of laboratories within GLAM organisations. In this work, we present a framework that works in several steps to transform into linked open data (LOD), assess and enrich with external repositories the data sets made available by GLAM organisations under open licences. The framework has been applied to three metadata data sets made available by the Data Foundry at the National Library of Scotland obtaining as a result a collection of LOD repositories, a data quality assessment and examples of exploration. The results of this work are publicly available and can be applied to other domains such as digital humanities and data science.

Change-Aware Scheduling for Effectively Updating Linked Open Data Caches

The linked open data (LOD) cloud is a global information space with a wealth of structured facts, which are useful for a wide range of usage scenarios. The LOD cloud handles a large number of requests from applications consuming the data. However, the performance of retrieving data from LOD repositories is one of the major challenge. Overcome with this challenge, we argue that it is advantageous to maintain a local cache for efficient querying and processing. Due to the continuous evolution of the LOD cloud, local copies become outdated. In order to utilize the best resources, improvised scheduling is required to maintain the freshness of the local data cache. In this paper, we have proposed an approach to efficiently capture the changes and update the cache. Our proposed approach, called application-aware change prioritization (AACP), consists of a change metric that quantifies the changes in LOD, and a weight function that assigns importance to recent changes. We have also proposed a mechanism to update policies, called preference-aware source update (PASU), which incorporates the previous estimation of changes and establishes when the local data cache needs to be updated. In the experimental evaluation, several state-of-the-art strategies are compared against the proposed approach. The performance of each policy is measured by computing the precision and recall between the local data cache update using the policy under consideration and the data source, which is the ground truth. Both cases of a single update and iterative update are evaluated in this study. The proposed approach is reported to outperform all the other policies by achieving an F1-score of 88% and effectivity of 93.5%.

Retrieval by recommendation: using LOD technologies to improve digital library search

This paper investigates how Linked Open Data (LOD) can be used for recommendations and information retrieval within digital libraries. While numerous studies on both research paper recommender systems and Linked Data-enabled recommender systems have been conducted, no previous attempt has been undertaken to explore opportunities of LOD in the context of search and discovery interfaces. We identify central advantages of Linked Open Data with regard to scientific search and propose two novel recommendation strategies, namely flexible similarity detection and constraint-based recommendations. These strategies take advantage of key characteristics of data that adheres to LOD principles. The viability of Linked Data recommendations was extensively evaluated within the scope of a web-based user experiment in the domain of economics. Findings indicate that the proposed methods are well suited to enhance established search functionalities and are thus offering novel ways of resource access. In addition to that, RDF triples from LOD repositories can complement local bibliographic records that are sparse or of poor quality.

Linking science: approaches for linking scientific publications across different LOD repositories

Enriching the content of a digital library (DL) with additional information from other DLs and domains would facilitate the scholarly communication, scientific findings, and knowledge distribution. The implementation of semantic technologies by interlinking resources results in a new vision for interoperability among different DLs. Therefore, this research explores bibliographic Linked Open Data (LOD) repositories by investigating alignments among them. The application of global unigrams frequency is applied for determining the importance of terms on the set of metadata. The semantic relatedness of the retrieved publications is measured by comparing two main approaches with one another: Vector Space Model through TF-IDF and Cosine Similarity, versus a Deep Learning approach through Word2Vec implementation of Word Embeddings. In summary, they are performing with 40.5% difference, concerning the outcome of relevant retrieved publications. In addition to the given metadata, word embeddings achieve a better performance for short texts, such as publications titles.