Knowledge bases are in wide-spread use for aiding tasks such as information extraction and information retrieval, where Web search is a prominent example. However, knowledge bases are inherently incomplete, particularly with respect to tail entities and properties. On the other hand, embedded entity markup based on Microdata, RDFa, and Microformats have become prevalent on the Web and constitute an unprecedented source of data with significant potential to aid the task of knowledge base

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... tion (KBA). However, RDF statements extracted from markup are fundamentally different from traditional knowledge graphs: entity descriptions are flat, facts are highly redundant and of varied quality, and, explicit links are missing despite a vast amount of coreferences. Therefore, data fusion is required in order to facilitate the use of markup data for KBA. We present a novel data fusion approach which addresses these issues through a combination of entity matching and fusion techniques geared towards the specific challenges associated with Web markup. To ensure precise and diverse results, we follow a supervised learning approach based on a novel set of features considering aspects such as quality and relevance of entities, facts and their sources. We perform a thorough evaluation on a subset of the Web Data Commons dataset and show significant potential for augmenting existing KBs. A comparison with existing data fusion baselines demonstrates superior performance of our approach when applied to Web markup data.