TL;DR
An AI-assisted form for KG metadata curation that enables a more efficient curation workflow, leads to more complete metadata, and is preferred by participants over both baselines is presented.
This work compares the respective capabilities of domain experts and of an Large Language Model (LLM) agent to generate useful domain-specific annotations for classifying, indexing, and searching scientific resources, and shows the complementarity between domain experts and LLMs.
An AI-guided framework is developed that aligns AI-assisted metadata extraction with Dublin Core Terms and the FAIR principles for digital libraries, archives, and cultural-heritage repositories and is evaluated as a design-science artefact in which retrieval is not a side feature but a feedback loop.
CHAD-ASK is introduced, a novel plugin for Morph-KGC, a Python-based RML data conversion engine that converts raw tabular survey data into fully compliant RDF triples, allowing researchers to perform complex metadata conversion without direct interaction with code or mapping languages.
This work targets a KG for Sophocles’ Antigone that supports two coupled uses: structured retrieval, through integrity and competency questions expressed in SPARQL over dramatic structure and interpretive annotations; and interactive exploration, through a lightweight read client that navigates lines across languages,...
This work forms the task of Information-Needs-Guided VKG Enrichment (IN-VKGE), and proposes an iterative framework that leverages large language models to assess whether information needs can be supported using SPARQL execution feedback and generate ontology and mapping enrichment proposals.
We present EXYGEN (EXplore Your Graphs ENgine), a framework for knowledge graph (KG) understanding that enables conversational access to KGs at scale. We address two questions in sequence. First, how effectively can LLMs perform text-to-SPARQL generation given only automatically derived structured metadata and small gr...
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