Triple based Background Knowledge Ranking for Document Enrichment

Encoding Distributional Semantics into Triple-Based Knowledge Ranking for Document Enrichment

Enriching Documents with Compact, Representative, Relevant Knowledge Graphs

A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and direct relations. We compute an entity …

Predicting Informativeness of Semantic Triples

Many automatic semantic relation extraction tools extract subject-predicate-object triples from unstructured text. However, a large quantity of these triples merely represent background knowledge. We explore using full t…

GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources. Exi…

Neural Relation Extraction for Knowledge Base Enrichment

We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an …