RelationalAI
01 January 2020
less than a minute read
Product graphs have emerged as a powerful tool for online retailers to enhance product semantic search, catalog navigation, and recommendations. Our work complements existing efforts on product graph creation, by enabling field experts to directly control the graph completion process.
Authors: Marouene Sfar Gandoura, Zografoula Vagena, Nikolaos Vasiloglou. 2020.
In KDD 2020 Workshop on Knowledge Graphs and E-commerce (KGE, KDD ‘20).
Product graphs have emerged as a powerful tool for online retailers to enhance product semantic search, catalog navigation, and recommendations. Their versatility stems from the fact that they can uniformly store and represent different relationships between products, their attributes, concepts or abstractions etc, in an actionable form. Such information may come from many, heterogeneous, disparate, and mostly unstructured data sources, rendering the product graph creation task a major undertaking. Our work complements existing efforts on product graph creation, by enabling field experts to directly control the graph completion process.
We defined named entity recognition (NER) in the legal domain and presented our approach towards generating ground truth data. In what follows, we go over the state-of-the-art in the NER domain and elaborate on the experiments we ran and the lessons we learned.
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