Papers Ontology Matching
“Ontology Matching” 태그가 달린 논문 51편 · 필터 해제
OAEI-LLM-T: A TBox Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching
Hallucinations are often inevitable in downstream tasks using large language models (LLMs). To tackle the substantial challenge of addressing hallucinations for LLM-based ontology matching (OM) systems, we introduce a ne…
Language ModelingLanguage ModellingLarge Language ModelOntology MatchingComplex Ontology Matching with Large Language Model Embeddings
Ontology, and more broadly, Knowledge Graph Matching is a challenging task in which expressiveness has not been fully addressed. Despite the increasing use of embeddings and language models for this task, approaches for …
Graph MatchingLanguage ModelingLanguage ModellingLarge Language Model+5Ontology Matching with Large Language Models and Prioritized Depth-First Search
Ontology matching (OM) plays a key role in enabling data interoperability and knowledge sharing, but it remains challenging due to the need for large training datasets and limited vocabulary processing in machine learnin…
Large Language ModelOntology MatchingHow Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?
The classic text preprocessing pipeline, comprising Tokenisation, Normalisation, Stop Words Removal, and Stemming/Lemmatisation, has been implemented in many systems for syntactic ontology matching (OM). However, the lac…
DiversityOntology MatchingPart-Of-Speech TaggingPOS+1OM4OV: Leveraging Ontology Matching for Ontology Versioning
Due to the dynamic nature of the semantic web, ontology version control is required to capture time-varying information, most importantly for widely-used ontologies. Despite the long-standing recognition of ontology vers…
ManagementOntology MatchingOAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching
Hallucinations of large language models (LLMs) commonly occur in domain-specific downstream tasks, with no exception in ontology matching (OM). The prevalence of using LLMs for OM raises the need for benchmarks to better…
Language ModelingLanguage ModellingLarge Language ModelOntology MatchingOntological Relations from Word Embeddings
It has been reliably shown that the similarity of word embeddings obtained from popular neural models such as BERT approximates effectively a form of semantic similarity of the meaning of those words. It is therefore nat…
Ontology MatchingSemantic SimilaritySemantic Textual SimilarityWord EmbeddingsGraphMatcher: A Graph Representation Learning Approach for Ontology Matching
Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain ontologies, semantically similar entiti…
Graph AttentionGraph Representation LearningOntology MatchingRepresentation LearningLLMs4OM: Matching Ontologies with Large Language Models
Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge …
Ontology MatchingRetrievalInteractive Ontology Matching with Cost-Efficient Learning
The creation of high-quality ontologies is crucial for data integration and knowledge-based reasoning, specifically in the context of the rising data economy. However, automatic ontology matchers are often bound to the h…
Active LearningData IntegrationOntology MatchingMatching Weak Informative Ontologies
Most existing ontology matching methods utilize the literal information to discover alignments. However, some literal information in ontologies may be opaque and some ontologies may not have sufficient literal informatio…
Ontology MatchingAgent-OM: Leveraging LLM Agents for Ontology Matching
Ontology matching (OM) enables semantic interoperability between different ontologies and resolves their conceptual heterogeneity by aligning related entities. OM systems currently have two prevailing design paradigms: c…
Ontology MatchingRetrievalOLaLa: Ontology Matching with Large Language Models
Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible …
Graph MatchingLanguage ModelingLanguage ModellingLarge Language Model+1Uncertainty in Automated Ontology Matching: Lessons Learned from an Empirical Experimentation
Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such a process by providing well-consolidated support to link and s…
Data IntegrationOntology MatchingContextualized Structural Self-supervised Learning for Ontology Matching
Ontology matching (OM) entails the identification of semantic relationships between concepts within two or more knowledge graphs (KGs) and serves as a critical step in integrating KGs from various sources. Recent advance…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsOntology Matching+1Conversational Ontology Alignment with ChatGPT
This study evaluates the applicability and efficiency of ChatGPT for ontology alignment using a naive approach. ChatGPT's output is compared to the results of the Ontology Alignment Evaluation Initiative 2022 campaign us…
Language ModelingLanguage ModellingLarge Language ModelOntology MatchingTruveta Mapper: A Zero-shot Ontology Alignment Framework
In this paper, a new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task. Ontologies are represented as graphs, and the translation is performe…
Ontology MatchingTransfer LearningTranslationResults of SemTab 2022
SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Match…
Cell Entity AnnotationColumn Type AnnotationGraph MatchingOntology MatchingExploring Wasserstein Distance across Concept Embeddings for Ontology Matching
Measuring the distance between ontological elements is fundamental for ontology matching. String-based distance metrics are notorious for shallow syntactic matching. In this exploratory study, we investigate Wasserstein …
Ontology MatchingWord EmbeddingsMachine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) tech…
Ontology Matching