HighlightCurated by Aramai EditorialarXiv

BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration

A paper proposing a method for bootstrapping e-commerce attribute taxonomies using human-AI collaboration.

The authors present BEATS, an approach to iteratively refine e-commerce attribute taxonomies through human-AI collaboration. This method aims to improve search performance by leveraging AI-driven suggestions and human feedback. The proposed framework is designed to be applicable in various e-commerce scenarios.

Based on: BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration · arXiv

HighlightCurated by Aramai EditorialDigital

Transforming SHACL Shape Graphs into HTML Applications for Populating Knowledge Graphs

A paper proposing a method to design user interfaces for populating knowledge graphs using SHACL constraint files.

The authors present an approach to create multi-form web applications from SHACL constraints, enabling user interface modeling and leveraging OWL reasoning for logical consistency. This method treats editing knowledge graphs as a business process, integrating ontology-based components. The application models are themselves knowledge graphs that can be verified using OWL reasoning.

Based on: Transforming SHACL Shape Graphs into HTML Applications for Populating Knowledge Graphs · Digital

HighlightCurated by Aramai Editorialopenalex.org

Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management

A paper on formalizing MIRAGE guidelines into a prototype ontology and DCAT3 extension vocabulary for glycomics data management.

The authors present a comprehensive semantic formalization of MIRAGE guidelines using an integrated RDF ontology framework. The framework models glycan structures, biological specimens, analytical instruments, and experimental processes with formal OWL semantics and SHACL validation constraints. It enables automated quality assessment, federated data querying, and enhanced reproducibility in glycomics research.

Based on: Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management

HighlightCurated by Aramai EditorialLecture notes in computer science

OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models

A paper on using retrieval-augmented generation to improve biomedical code mapping leveraging ontology knowledge graphs and large language models.

The authors propose OntologyRAG, a method for better and faster biomedical code mapping. They leverage ontology knowledge graphs and large language models to improve the accuracy and efficiency of code mapping. The approach uses retrieval-augmented generation to combine the strengths of both knowledge graphs and language models.

Based on: OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models · Lecture notes in computer science

HighlightCurated by Aramai EditorialElectronics

CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning

A framework that enhances Large Language Models by retrieving relevant knowledge.

The CRP-RAG framework addresses limitations in existing Retrieval-Augmented Generation methods. It employs reasoning graphs to model complex query reasoning processes and guides knowledge retrieval, aggregation, and evaluation through these graphs. This approach outperforms baseline models in open-domain QA, multi-hop reasoning, and factual verification.

Based on: CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning · Electronics

HighlightCurated by Aramai Editorialopenalex.org

Enhancing Vector based Retrieval Augmented Generation with Contextual Knowledge Graph Construction

A novel approach to enhancing vector-based RAG models using contextual knowledge graph construction.

The authors introduce Contextual Knowledge Graph Construction (CKGC), a method that dynamically builds a knowledge graph to enhance information retrieval and question answering tasks. CKGC leverages text chunking, large language models, and ontology mapping to construct a contextualized knowledge graph. Experiments demonstrate significant improvements in Mean Reciprocal Rank and Top-k Accuracy.

Based on: Enhancing Vector based Retrieval Augmented Generation with Contextual Knowledge Graph Construction

HighlightCurated by Aramai EditorialPeerJ Computer Science

Evaluating FAIR Digital Object and Linked Data as distributed object systems

A paper evaluating the FAIR Digital Object concept and its implementations.

The authors evaluate FAIR Digital Object (FDO) as a global distributed object system using five conceptual frameworks. They compare FDO with established Linked Data practices and Web architecture, providing recommendations for both communities. The paper discusses the history of the Semantic Web and its relevance to FDO adoption.

Based on: Evaluating FAIR Digital Object and Linked Data as distributed object systems · PeerJ Computer Science

HighlightCurated by Aramai EditorialInformation

Construction of Knowledge Graphs: Current State and Challenges

A research paper on the current state and challenges of constructing knowledge graphs.

The authors discuss the main graph models for knowledge graphs, introduce requirements for future construction pipelines, and evaluate the state-of-the-art. They identify areas in need of further research and improvement. The paper provides an overview of necessary steps to build high-quality knowledge graphs, including metadata management and quality assurance.

Based on: Construction of Knowledge Graphs: Current State and Challenges · Information

HighlightCurated by Aramai Editorialopenalex.org

Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

A paper introducing SMART-SLIC, a domain-specific LLM framework integrating RAG with KG and vector store.

The authors present SMART-SLIC, a framework that combines retrieval-augmented generation (RAG) with knowledge graphs (KG) and vector stores to improve question answering accuracy in specific domains. The framework is designed to be generalizable and adaptable to various specialized domains. It aims to mitigate hallucinations, reduce fine-tuning needs, and attribute information sources.

Based on: Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

HighlightCurated by Aramai EditorialarXiv (Cornell University)

From human experts to machines: An LLM supported approach to ontology and knowledge graph construction

Paper exploring the semi-automatic construction of Knowledge Graphs using Large Language Models.

The authors propose a pipeline for constructing Knowledge Graphs with minimal human involvement, leveraging open-source Large Language Models. They demonstrate their method on a deep learning methodology dataset. The paper evaluates the generated content and suggests that LLMs can reduce human effort in KG construction.

Based on: From human experts to machines: An LLM supported approach to ontology and knowledge graph construction · arXiv (Cornell University)

HighlightCurated by Aramai Editorialopenalex.org

TrumorGPT: Query Optimization and Semantic Reasoning over Networks for Automated Fact-Checking

A generative AI solution for automated fact-checking that merges machine learning with natural language processing techniques.

The paper introduces TrumorGPT, a novel framework for automated fact-checking. It leverages a large language model with few-shot learning and retrieval-augmented generation to access updated knowledge graphs. This approach aims to combat misinformation by providing accurate and reliable information promptly.

Based on: TrumorGPT: Query Optimization and Semantic Reasoning over Networks for Automated Fact-Checking

HighlightCurated by Aramai EditorialInstitution of Engineering and Technology eBooks

Knowledge representation and reasoning in personal knowledge graphs

A chapter discussing the semantic web stack and its application to personal knowledge graphs.

The authors describe the semantic web stack, a set of open standards for representing and reasoning with knowledge graphs.,They discuss projects using these standards to build personal knowledge graphs that interoperate with other knowledge graphs on the web.,Related standards for describing rules and policies are also discussed.

Based on: Knowledge representation and reasoning in personal knowledge graphs · Institution of Engineering and Technology eBooks