https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c/Head https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c/assertion https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c/provenance https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c/pubinfo https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAVrgcBn3vWKbPnxAb24pbaEkK9nm9MPRUdzWSkECHz6c/assertion https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/dc/terms/title LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#LLMAlign https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#AttrGNN https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#BERTINT https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#ChatEA https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#DERA https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#DERAR https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#GCNAlign https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#HMAN https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#LLMEA https://doi.org/10.48550/arXiv.2412.04690 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#TEA https://doi.org/10.48550/arXiv.2412.04690 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/ns/prov#Entity https://neverblink.eu/ontologies/llm-kg/methods#AttrGNN http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#AttrGNN http://www.w3.org/2000/01/rdf-schema#comment AttrGNN is a representative method for modeling the topological structure of attribute triples using Graph Neural Networks. It is included as a baseline to explore the effectiveness of attribute information modeling through GNNs compared to LLMs. https://neverblink.eu/ontologies/llm-kg/methods#AttrGNN http://www.w3.org/2000/01/rdf-schema#label AttrGNN https://neverblink.eu/ontologies/llm-kg/methods#BERTINT http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#BERTINT http://www.w3.org/2000/01/rdf-schema#comment BERT-INT is an Entity Alignment method that uses the BERT language model for cross-graph interactive modeling of semantic information. It is used as a baseline to evaluate the performance of LLM-Align. https://neverblink.eu/ontologies/llm-kg/methods#BERTINT http://www.w3.org/2000/01/rdf-schema#label BERT-INT https://neverblink.eu/ontologies/llm-kg/methods#ChatEA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#ChatEA http://www.w3.org/2000/01/rdf-schema#comment ChatEA is an Entity Alignment approach that utilizes an existing EA model to generate alignment candidates and then leverages the reasoning capabilities of LLMs to predict the final results. It is discussed as related work and used as a baseline for comparison. https://neverblink.eu/ontologies/llm-kg/methods#ChatEA http://www.w3.org/2000/01/rdf-schema#label ChatEA https://neverblink.eu/ontologies/llm-kg/methods#DERA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#DERA http://www.w3.org/2000/01/rdf-schema#comment DERA is an Entity Alignment method based on heterogeneous parsing with large language models, used as a state-of-the-art baseline for comparison against LLM-Align's performance. https://neverblink.eu/ontologies/llm-kg/methods#DERA http://www.w3.org/2000/01/rdf-schema#label DERA https://neverblink.eu/ontologies/llm-kg/methods#DERAR http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#DERAR http://www.w3.org/2000/01/rdf-schema#comment DERA-R is a simplified version of DERA, serving as a base model for candidate alignment selection within the LLM-Align framework and also as a strong baseline for performance comparison. https://neverblink.eu/ontologies/llm-kg/methods#DERAR http://www.w3.org/2000/01/rdf-schema#label DERA-R https://neverblink.eu/ontologies/llm-kg/methods#GCNAlign http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#GCNAlign http://www.w3.org/2000/01/rdf-schema#comment GCN-Align is an Entity Alignment model that uses Graph Convolutional Networks (GCNs) to encode attribute information into entity embeddings. It serves as a base model for candidate entity selection within the LLM-Align framework and also as a baseline for performance comparison. https://neverblink.eu/ontologies/llm-kg/methods#GCNAlign http://www.w3.org/2000/01/rdf-schema#label GCN-Align https://neverblink.eu/ontologies/llm-kg/methods#HMAN http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#HMAN http://www.w3.org/2000/01/rdf-schema#comment HMAN is an Entity Alignment method that employs fine-grained ranking techniques from information retrieval to re-rank candidate entities. It is used as a baseline for performance comparison in the experiments. https://neverblink.eu/ontologies/llm-kg/methods#HMAN http://www.w3.org/2000/01/rdf-schema#label HMAN https://neverblink.eu/ontologies/llm-kg/methods#LLMAlign http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGConstruction https://neverblink.eu/ontologies/llm-kg/methods#LLMAlign http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#LLMAlign http://www.w3.org/2000/01/rdf-schema#comment LLM-Align is a novel framework for Entity Alignment (EA) that leverages Large Language Models (LLMs) to improve EA performance. It combines candidate alignment selection using existing EA models with LLM-based reasoning, enhanced by heuristic attribute and relation selection for prompt construction and a multi-round voting mechanism to mitigate LLM hallucinations and positional bias. This method directly addresses a core task in Knowledge Graph construction by enabling LLMs to perform cross-KG entity resolution. https://neverblink.eu/ontologies/llm-kg/methods#LLMAlign http://www.w3.org/2000/01/rdf-schema#label LLM-Align https://neverblink.eu/ontologies/llm-kg/methods#LLMAlign https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG https://neverblink.eu/ontologies/llm-kg/methods#LLMEA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#LLMEA http://www.w3.org/2000/01/rdf-schema#comment LLMEA is an Entity Alignment method that integrates knowledge from both Knowledge Graphs and Large Language Models to predict entity alignments. It is discussed as related work and used as a baseline to compare the performance of LLM-Align. https://neverblink.eu/ontologies/llm-kg/methods#LLMEA http://www.w3.org/2000/01/rdf-schema#label LLMEA https://neverblink.eu/ontologies/llm-kg/methods#TEA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#TEA http://www.w3.org/2000/01/rdf-schema#comment TEA is a classic method for Entity Alignment based on language model modeling, organized around textual entailment. 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