Human in the Loop Enrichment of Product Graphs with Probabilistic Soft Logic

Nikolaos Vasiloglou

Abstract:

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 focus on the
subtask of enriching product graphs with product attributes and we
employ statistical relational learning coupled with a novel human
in the loop enhanced inference workflow based on Probabilistic Soft
Logic (PSL), to reliably predict product-attribute relationships. Our
preliminary experiments demonstrate the viability, practicality and
effectiveness of our approach and its competitiveness comparing
with alternative methods. As a by-product, our method generates
probabilistic fact validity labels from an originally unlabeled database that can subsequently be leveraged by other graph completion
methods.