Class Schema Discovery from Semi-Structured Data
DOI:
https://doi.org/10.5753/jidm.2023.3117Keywords:
Schema Discovery, Entity Classes, Semi-structured Data, Class AttributesAbstract
A wide range of applications has used semi-structured data. A characteristic of this type of data is its flexible structure, i.e., it does not rely on schema-based constraints to define its entities. Usually entities of a same kind (i.e, class) do not present the same attribute set. However, some data processing and management applications rely on a data schema to perform their tasks. In this context, the lack of structure is a challenge for these applications to use this data. In this paper, we propose CoFFee, an approach to class schema discovery. Given a set of heterogeneous entity schemata, found within a class, CoFFee provides a summarized set with core attributes. To this end, CoFFee applies a strategy combining attributes co-occurrence and frequency. It models a set of entity schemata as a graph and uses centrality metrics to capture the co-occurrence between attributes. We evaluated CoFFee using data from 12 classes extracted from DBpedia and e-Commerce datasets. We benchmarked it against two other state-of-the-art approaches. The results show that: i) CoFFee effectively provides a summarized schema, minimizing non-relevant attributes without compromising the data retrieval rate; and ii) CoFFee produces a summarized schema of good quality, outperforming the baselines by an average of 19% of F1 score.
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