FRBR-ML: A FRBR-based framework for semantic interoperability

Abstract

Metadata related to cultural items such as literature, music and movies is a valuable resource that is currently exploited in many applications and services based on semantic web technologies. A vast amount of such information has been created by memory institutions in the last decades using different standard or ad hoc schemas, and a main challenge is to make this legacy data accessible as reusable semantic data. On one hand, this is a syntactic problem that can be solved by transforming to formats that are compatible with the tools and services used for semantic aware services. On the other hand, this is a semantic problem. Simply transforming from one format to another does not automatically enable semantic interoperability and legacy data often needs to be reinterpreted as well as transformed. The conceptual model in the Functional Requirements for Bibliographic Records, initially developed as a conceptual framework for library standards and systems, is a major step towards a shared semantic model of the products of artistic and intellectual endeavor of mankind. The model is generally accepted as sufficiently generic to serve as a conceptual framework for a broad range of cultural heritage metadata. Unfortunately, the existing large body of legacy data makes a transition to this model difficult. For instance, most bibliographic data is still only available in various MARC-based formats which is hard to render into reusable and meaningful semantic data. Making legacy bibliographic data accessible as semantic data is a complex problem that includes interpreting and transforming the information. In this article, we present our work on transforming and enhancing legacy bibliographic information into a representation where the structure and semantics of the FRBR model is explicit.

A Survey of FRBRization Techniques

“…Several interesting challenges of FRBRization are faced by XC in terms of input model management and normalization of extracted data (see Section 4.3). [29] is an extended version of Aalberg's FRBRization tool [1]. In addition to the transformation aspects, it aims at promoting interoperability between different models such as MARC and FRBR.…”

“…Variations may create several levels of Works 6 while eXtensible Catalog can directly create a new Expression and its parent Work 7 . In FRBR-ML, the idea for disambiguating information contained in the MARC21 7xx fields is to analyse similar records that may not contain the ambiguity [29]. This search is performed either in the same collection, in external collections (z39.50 or SRU/SRW) or in knowledge bases such as the Linked Open Data cloud 8 (LOD).…”

“…For instance, using both information from structured sources (e.g., LOD) and non-structured sources (e.g., websites) involve effective and efficient matching tools at schema and instance levels. FRBR-ML performs matching to external knowledge bases (LOD cloud) for discovering the semantic type of an ambiguous field (e.g., person, location) and detecting missing relationships between FRBR entities [29]. As for eXtensible Catalog, it can perform a specific data fusion process to merge the attributes from equivalent entities [7].…”

“…As for eXtensible Catalog, it can perform a specific data fusion process to merge the attributes from equivalent entities [7]. Descriptive keys are used for comparing records and for detecting duplicates, thus improving quality [7,21,9,29]. The issue is to find the most relevant patterns for descriptive keys.…”

“…TelPlus proposes a metric for the evaluation of the aggregation level (i.e., the percentage of duplicate entities extracted). The FRBR-ML project [29] defines three metrics to measure the degree of completeness (i.e., the amount of information lost during FRBRization), the minimality rate (i.e., the amount of redundant information, at the property, record and collection levels) and the extension rate (i.e., the amount of enriched information). Finally, our classification enables the identification of relevant techniques accord-ing to the type of FRBRization and the model expressiveness.…”