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Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

A model for converting user questions into GQL statements for graph database retrieval.

The paper proposes a pipeline solution for the Text2GQL task, using an improved language model with pre-trained adapter plug-in and pointer network. The method enables efficient direct communication between humans and machines in medical Human-Robot Interactions (HRI).

Based on: Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot · Information Systems Frontiers

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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Comprehensive review of knowledge graph research topics and recent breakthroughs.

The paper provides a survey of knowledge graphs, covering representation learning, acquisition, and applications.,It reviews various aspects of knowledge graph embedding, including representation space, scoring function, encoding models, and auxiliary information.,The authors also explore emerging topics such as metarelational learning, commonsense reasoning, and temporal knowledge graphs.

Based on: A Survey on Knowledge Graphs: Representation, Acquisition, and Applications · IEEE Transactions on Neural Networks and Learning Systems

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Knowledge Graphs

Comprehensive introduction to knowledge graphs.

The article provides an overview of knowledge graphs, their applications, and challenges.,It motivates and contrasts various graph-based data models and query languages.,The authors explain how knowledge can be represented and extracted using deductive and inductive techniques.

Based on: Knowledge Graphs · ACM Computing Surveys

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Improving and assessing data quality of knowledge graphs

A dissertation on improving and assessing data quality in knowledge graphs.

This dissertation focuses on improving data quality and assessing semantic quality of knowledge graphs. It investigates two challenges: including data transformations to clean the data, and evaluating the quality of knowledge graphs on both data values and semantic relationships.

Based on: Improving and assessing data quality of knowledge graphs · Ghent University Academic Bibliography (Ghent University)

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Machine learning with biomedical ontologies

A paper on using biomedical ontologies in machine learning methods.

The authors provide an overview of methods that combine ontologies and machine learning. They outline how semantic similarity measures and ontology embeddings can exploit background knowledge in biomedical ontologies, and how ontologies can improve machine learning models by providing constraints. The methods and experiments are available as executable notebooks.

Based on: Machine learning with biomedical ontologies · bioRxiv (Cold Spring Harbor Laboratory)

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RDF graph validation using rule-based reasoning

Paper proposing an alternative validation approach for RDF graphs using rule-based reasoning.

The paper presents a new validation method that uses rule-based reasoning to validate RDF graphs. It compares to existing approaches and provides a formal ground and practical implementation called Validatrr, which is based on N3Logic and the EYE reasoner. The proposed approach better explains the root cause of violations and returns an accurate number of violations.

Based on: RDF graph validation using rule-based reasoning · Semantic Web

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The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows

Describes a new release workflow for DBpedia that increases agility and efficiency in knowledge extraction workflows.

DBpedia's new release cycle aims to improve productivity and agility through a re-engineered workflow.,The new workflow focuses on quality control, debugging, and maintainability while publishing regular releases with over 21 billion triples.,An experimental evaluation demonstrates the effectiveness of the implemented measures.

Based on: The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows · Lecture notes in computer science

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Semantic Systems. In the Era of Knowledge Graphs

Proceedings of the 16th International Conference on Semantic Systems (SEMANTiCS 2020)

This volume contains proceedings from SEMANTiCS 2020, a conference on semantic systems and related topics. The conference brings together researchers and industry experts to share latest results in areas like data science, machine learning, and the Semantic Web. Attendees learn about emerging trends and topics in semantic computing.

Based on: Semantic Systems. In the Era of Knowledge Graphs · Lecture notes in computer science

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

A paper on explainable artificial intelligence concepts, taxonomies, opportunities, and challenges.

The authors present a comprehensive review of explainable artificial intelligence (XAI) concepts, including taxonomies and challenges. They discuss the importance of XAI in ensuring transparency and accountability in AI systems. The paper also highlights opportunities for responsible AI development and deployment.

Based on: Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI · Information Fusion

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Ontology-Based Data Access: A Survey

A survey on the framework of ontology-based data access for providing user-friendly access to relational data sources.

The paper discusses the main ingredients, key results, techniques, applications, and future challenges of ontology-based data access. It focuses on relational data sources and provides a comprehensive overview of the field. The authors present the theoretical foundations, practical techniques, and potential applications of this semantic paradigm.

Based on: Ontology-Based Data Access: A Survey

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Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases

Study on evaluating the semantic relations represented by word embeddings using external knowledge bases.

This paper proposes a novel approach to evaluate the semantic relations in word embeddings using Wikipedia, WordNet, and UMLS. The authors trained multiple word embeddings using health-related articles and evaluated their performance in analogy and semantic relation term retrieval tasks. The study found that domain-specific corpora improve the performance of word embeddings for specific text mining tasks.

Based on: Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases · BMC Medical Informatics and Decision Making

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Completeness and consistency analysis for evolving knowledge bases

This paper presents an analysis on completeness and consistency in evolving knowledge bases.

The authors propose a framework to analyze the completeness and consistency of evolving knowledge bases. They introduce metrics to measure these aspects and apply them to several case studies. The results demonstrate the effectiveness of their approach in identifying issues with knowledge base evolution.

Based on: Completeness and consistency analysis for evolving knowledge bases · Journal of Web Semantics