Bibliographic Dataset Research Articles

Bibliographic Data Towards the Semantic Web: A Review of Key Issues and Recent Experiences

This article intends to review the underlying concepts and technologies of the Semantic Web and the potential they provide for metadata management covering bibliographic resources. To get closer to a semantic web data space, different libraries are adhering to the initiatives making their traditional Knowledge Organization Systems (KOS) operational on the web through SKOS techniques, as well as releasing bibliographic data under open licenses (open bibliographic data) and publishing it with Linked Data (LD) mechanisms. LD meaningful semantic connections create the Web of Data, a global database representing the first practical step to the Semantic Web. Here interoperable data can be processed independently of application, platform or domain, providing rich retrieval results produced by powerful query languages. From a library perspective, a problem statement is a global promotion within the Library community of understanding and of adoption of Linked Open Data (LOD), of LODe-BD recommendations, as well as releasing bibliographic data as Linked Library Data (LLD). In this way, different bibliographic datasets could become full members of the Semantic Web making interoperable different knowledge datasets of heterogeneous web communities.

An Efficient Blocking Technique for Reference Matching using MapReduce

Document Clustering has become an increasingly important task in the area of data mining and information retrieval. With growing data volumes, CPU—and memory-efficient techniques for clustering algorithms are receiving considerable attention in the research community. To deal with huge amounts of data (e.g., documents from Wikipedia or CiteSeerX which are several GB in size), distributed clustering techniques have been designed to provide scalable and flexible approaches. We study the problem of document clustering in the area of Entity Matching, where documents from various data sources are matched together. More specifically, we focus on a common optimization technique called blocking which reduces the enormous search space by clustering the data sources into smaller groups and processes comparisons only within a group. In this article, we describe our experiences and findings in applying the MapReduce framework to deal with huge bibliographic data sets and to provide a flexible, scalable and easy-to-use blocking technique to reduce the search space for Entity Matching.

Automatic metadata generation using associative networks

In spite of its tremendous value, metadata is generally sparse and incomplete, thereby hampering the effectiveness of digital information services. Many of the existing mechanisms for the automated creation of metadata rely primarily on content analysis which can be costly and inefficient. The automatic metadata generation system proposed in this article leverages resource relationships generated from existing metadata as a medium for propagation from metadata-rich to metadata-poor resources. Because of its independence from content analysis, it can be applied to a wide variety of resource media types and is shown to be computationally inexpensive. The proposed method operates through two distinct phases. Occurrence and cooccurrence algorithms first generate an associative network of repository resources leveraging existing repository metadata. Second, using the associative network as a substrate, metadata associated with metadata-rich resources is propagated to metadata-poor resources by means of a discrete-form spreading activation algorithm. This article discusses the general framework for building associative networks, an algorithm for disseminating metadata through such networks, and the results of an experiment and validation of the proposed method using a standard bibliographic dataset.