Ontology-Guided Knowledge Graph Retrieval for Multi-Hop and Cross-Granularity Store Fulfillment Queries
Year: 2025
Type: conference-paper
Abstract: Answering complex queries in store fulfillment, such as ''What percentage of employee-assigned actions remain unresolved?'' or ''How many worklists for a specific product type were completed within a timeframe at each location?'' requires precise, multi-hop reasoning across datasets with varying granularities. This paper introduces an ontology-based knowledge graph (KG) approach integrated with a structured text-to-Cypher generation pipeline, enabling accurate retrieval for such queries. Benchmarking against a robust hybrid search baseline combining BM25 and semantic search, our method demonstrates superior performance in addressing multi-hop and cross-granularity questions. Leveraging a KG schema designed to capture intricate relationships (e.g. (OrderLineItem)-[:INVOLVES_ACTION]->(Action)-[:INVOLVES]->(BatchProcess)-[:IS_COMPLETED_AT]->(Location)), we reveal universal patterns for constructing and querying highly relational data. This work highlights the transformative potential of ontology-driven KGs to improve reasoning, data aggregation, and decision-making, with broader implications for any domain requiring structured, multi-relational data analysis.
Authors: Mengyue Zhao, Matthew Nokleby, Bo Shen, Wenbo Dong, Deepti Pachauri, Andrew Yang
Institution: Target (United States)
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412377964