QUASAR: A Query-Oriented System for Multi-hop Knowledge Graph Data Summarisation

To quantify information loss when KG triples are verbalized, summarized, and reconstructed, and how much QA capability survives.

Compares 2 verbalization methods (rule-based vs. neural Flan-T5-XL) and 3 summarization levels (extractive TextRank, abstractive Flan-T5-XL, none).

KG storage, transfer, and augmentation via large language models, question answering, search engines, and recommendation systems.

Knowledge Graphs (KGs) store structured factual information as RDF triples, but their util- ity depends on preservation through NLP transformations. This thesis presents QUASAR (Query-oriented System for Multi-hop Knowledge Graph Data Summarisation), a systematic investigation into information loss when KG triples are verbalized into natural language text, optionally summarized, and then reconstructed back into a KG. We implement a complete six-stage round-trip evaluation pipeline on the Web QSP dataset over Freebase, testing 6 pipeline variants formed by combining 2 verbalization methods (rule-based templatesvs. neural Flan-T 5-XL) with 3 summarizationlevels(extractive Text Rank, abstractive Flan-T 5-XL, andnosummarization). Eachvariantproducesareconstructed KG viathe REBEL relation extraction model, which is then evaluated by executing the original 1,639 test SPARQL queries and comparing predicted answers against ground truth. Our key (cid:28) ndings are: (1) the reconstruction stage is the dominant information bottleneck, with REBEL recoveringonly∼12%oftriples(ℓ 5 ≈ 0.88);(2) rule-basedverbalizationconsistentlyout- performs neural across all downstream metrics (Answer F 1: 0.0295 vs. 0.0275 for best variants); (3) extractive summarization preserves 93% of recoverable information while abstractive loses 44%; (4) 2-hop queries involving CVT intermediate nodes are essentially unrecoverable across all approaches; and (5) even the identity baseline (original 810 K-triple KG) can only answer 28.8% of cross-split test queries, establishing a hard ceiling. The cumulative preservation through the best pipeline variant (nosumm_rb) is 7.77%, demon- strating that text remains a highly lossy compression format for structured knowledge. These (cid:28) ndingshaveimplicationsfor KG storage, transfer, andaugmentationvialargelanguagemodels.