Knowledge Tree
knowledge base
Also known as: knowledge bases
Facts (52)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 7 facts
referenceThe paper 'Yago 4: A reason-able knowledge base' by T. Pellissier Tanon, G. Weikum, and F. Suchanek, published in the European Semantic Web Conference in 2020, describes the YAGO 4 knowledge base.
claimThe dstlr tool offers support for validating extracted facts against an external knowledge base.
referenceThe paper 'Yago: a core of semantic knowledge' by F.M. Suchanek, G. Kasneci, and G. Weikum, published in The Web Conference in 2007, introduces the YAGO knowledge base.
claimMachine learning methods are used for Named Entity Recognition (NER) to identify 'emerging entities' that are unknown to a knowledge base.
claimKnowledge graph quality can be improved by enriching domain knowledge through loading specific entity information from external, open-accessible knowledge bases, rather than integrating entire external data collections.
claimOntology development is the incremental process of creating or extending an ontological knowledge base, which is required for both the initial construction of a knowledge graph and its subsequent updates to incorporate new information.
claimEntity Linking (EL) or Named Entity Disambiguation (NED) is the process of linking recognized named entities in text to a knowledge base or Knowledge Graph (KG) by selecting the correct entity from a set of candidates.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 5 facts
referenceThe paper 'Language models as knowledge bases?' by Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A. H. et al. investigates whether language models can function as knowledge bases.
claimEntity Linking (EL) is the process of matching text mentions to specific entities in a knowledge base to enhance text understanding and information retrieval.
claimLarge Language Models (LLMs) often struggle with tasks requiring deep knowledge and complex reasoning due to limitations in their internal knowledge bases, a gap that can be bridged by integrating structured knowledge from Knowledge Graphs (KGs).
referenceBioLAMA (Sung et al., 2021) introduces a biomedical knowledge probing benchmark, assessing whether Language Models can serve as domain-specific Knowledge Bases using structured fact triples.
claimREALM and RAG pioneered the integration of neural retrievers with generative transformers by retrieving relevant documents or knowledge passages from large corpora or knowledge bases to support downstream predictions.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 5 facts
claimA knowledge base is a data set that represents real-world facts and semantic relations in the form of triplets.
claimKnowledge graphs and knowledge bases are generally regarded as the same concept and are used interchangeably.
referenceMinervini P, Bošnjak M, Rocktäschel T et al. published 'Differentiable reasoning on large knowledge bases and natural language' in the proceedings of the AAAI Conference on Artificial Intelligence in 2020.
referenceYago is a knowledge base containing a large number of entities and relationships extracted from sources including Wikipedia and WordNet.
claimOpen-world techniques for knowledge graph completion are emerging to extract potential objects from outside of existing knowledge bases.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 5 facts
claimT-REx is a benchmark for evaluating the ability of models to understand and generate text based on structured data from knowledge bases, specifically testing the generation of text from knowledge base triples.
referenceVrandečić and Krötzsch (2014) published 'Wikidata: a free collaborative knowledge base' in Communications of the ACM.
referenceFader A, Zettlemoyer L, and Etzioni O published 'Open question answering over curated and extracted knowledge bases' in the Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining in 2014.
claimNELL-995 is a benchmark for evaluating knowledge extraction and completion from large-scale knowledge bases by testing a model's ability to extract and infer new knowledge from existing knowledge base entries.
referencePetroni F, Rocktäschel T, Lewis P, Bakhtin A, Wu Y, Miller AH, and Riedel S authored 'Language models as knowledge bases?', published as an arXiv preprint in 2019 (arXiv:1909.01066).
Evaluating RAG applications with Amazon Bedrock knowledge base ... aws.amazon.com Mar 14, 2025 3 facts
procedureTo establish a baseline for RAG system performance, users should begin by configuring default settings in their knowledge base, such as chunking strategies, embedding models, and prompt templates, before creating a diverse evaluation dataset of queries and knowledge sources.
procedureA systematic approach to ongoing evaluation for RAG applications involves scheduling regular offline evaluation cycles aligned with knowledge base updates, tracking metric trends over time, and using insights to guide knowledge base refinements and generator model customization.
claimIn Amazon Bedrock RAG evaluations, the 'referenceResponses' field must contain the expected ground truth answer that an end-to-end RAG system should generate for a given prompt, rather than the expected passages or chunks retrieved from the Knowledge Base.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 3 facts
referenceRajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay-Yoon Lee, Lizhen Tan, Lazaros Polymenakos, and Andrew McCallum authored 'Case-based reasoning for natural language queries over knowledge bases', published as an arXiv preprint (arXiv:2104.08762) in 2021.
referenceHenrique Lemos, Pedro Avelar, Marcelo Prates, Artur Garcez, and Luís Lamb developed a neural-symbolic relational reasoning approach on graph models for effective link inference and computation from knowledge bases, published in the International Conference on Artificial Neural Networks in 2020.
claimIn neuro-symbolic reasoning tasks, the symbolic system (including the knowledge base and logic rules) orchestrates the overall reasoning process, while the neural network acts as a subcomponent that processes raw data and interprets symbolic rules in the context of a query.
Reducing hallucinations in large language models with custom ... aws.amazon.com Nov 26, 2024 2 facts
procedureThe cleanup process for the Amazon Bedrock Agents hallucination detection infrastructure follows this specific order: disable the action group, delete the action group, delete the alias, delete the agent, delete the Lambda function, empty the S3 bucket, delete the S3 bucket, delete AWS Identity and Access Management (IAM) roles and policies, delete the vector database collection policies, and delete the knowledge bases.
claimUsing Amazon Bedrock Agents can increase overall latency compared to using Amazon Bedrock Guardrails and Amazon Bedrock Prompt Flows because Amazon Bedrock Agents generate workflow orchestration in real time using available knowledge bases, tools, and APIs, whereas prompt flows and guardrails require offline design and orchestration.
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 2 facts
referencePellissier Tanon, T., Weikum, G. & Suchanek, F. published 'Yago 4: A reason-able knowledge base' in the proceedings of the 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31–June 4, 2020.
claimThe proposed knowledge graph construction framework integrates diverse data sources to produce a reliable knowledge base suitable for critical decision-support applications.
Combining large language models with enterprise knowledge graphs frontiersin.org Aug 26, 2024 2 facts
claimDistant supervision (DS) is an automated data labeling technique that aligns knowledge bases with raw corpora to produce annotated data, used to address the lack of large annotated corpora for relation extraction and named entity recognition.
claimDistant supervision (DS) methods for Named Entity Recognition (NER) involve tagging text corpora using external knowledge sources such as dictionaries, knowledge bases, or knowledge graphs.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 2 facts
procedureThe LNN-based inductive logic programming method proposed by Sen et al. (2022) operates through the following procedure: (1) Input a knowledge base containing facts, relations, and rules describing the target structure. (2) Build an LNN network based on the template to simulate logical connectives, where each node represents an expression or logical rule. (3) Use facts in the knowledge base as training data to adjust logical operations via optimization algorithms like back propagation and gradient descent. (4) Convert the trained LNN into a set of logical rules that reflect the relationships in the input data.
referenceLemos et al. (2020) developed a neuro-symbolic relational reasoning method for graph models that enables effective link inference and computation from knowledge bases.
Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org 2 facts
The Year of Neuro-Symbolic AI: How 2026 Makes Machines Actually ... cogentinfo.com Dec 30, 2025 1 fact
procedureOrganizations can create a reusable foundation for AI systems by structuring policies, workflows, taxonomies, and domain expertise into ontologies and knowledge bases.
10 RAG examples and use cases from real companies - Evidently AI evidentlyai.com Feb 13, 2025 1 fact
procedureThe DoorDash RAG-based delivery support chatbot processes queries by condensing the conversation to identify the core issue, searching the knowledge base for relevant articles and past resolved cases, and feeding the retrieved information into an LLM to craft a response.
KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org Mar 18, 2025 1 fact
referencePeiyun Wu, Xiaowang Zhang, and Zhiyong Feng authored the paper 'A survey of question answering over knowledge base', published in the proceedings of the 4th China Conference, CCKS 2019, in Hangzhou, China, August 24–27, 2019.
Neuro-symbolic AI - Wikipedia en.wikipedia.org 1 fact
claimGary Marcus identifies four cognitive prerequisites for building robust artificial intelligence: (1) hybrid architectures that combine large-scale learning with the representational and computational powers of symbol manipulation, (2) large-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledge, (3) reasoning mechanisms capable of leveraging those knowledge bases in tractable ways, and (4) rich cognitive models that work together with those mechanisms and knowledge bases.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 1 fact
referenceClassic symbolic AI represents knowledge through abstractions and symbols, utilizing explicit modeling like rules and relationships within structured knowledge bases to perform reasoning based on pre-defined rules.
Applying Large Language Models in Knowledge Graph-based ... arxiv.org Jan 7, 2025 1 fact
claimFormal knowledge bases containing domain knowledge are utilized for the generation and annotation of business process models.
Knowledge Enhanced Industrial Question-Answering Using Large ... sciencedirect.com Aug 12, 2025 1 fact
procedureA knowledge-enhanced LLM retrieves relevant information by processing both user queries and knowledge base content.
Real-Time Evaluation Models for RAG: Who Detects Hallucinations ... cleanlab.ai Apr 7, 2025 1 fact
procedureIn a Retrieval-Augmented Generation (RAG) system, the process involves retrieving relevant context from a knowledge base for a user query, then feeding that context and the query into a Large Language Model (LLM) to generate a response.
Empowering GraphRAG with Knowledge Filtering and Integration arxiv.org Mar 18, 2025 1 fact
referenceTalmor and Berant (2018) authored 'The web as a knowledge-base for answering complex questions', published as an arXiv preprint (arXiv:1803.06643).
Detecting hallucinations with LLM-as-a-judge: Prompt ... - Datadog datadoghq.com Aug 25, 2025 1 fact
procedureDetermining faithfulness in RAG systems requires three components: a user-posed question, context retrieved from a knowledge base, and an answer generated by the LLM.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 1 fact
claimNeural Theorem Provers utilize embeddings and attention mechanisms to learn logical inference in knowledge bases, quantifying uncertainty through scores or attention weights for each possible proof.
Knowledge Graphs Enhance LLMs for Contextual Intelligence linkedin.com Mar 10, 2026 1 fact
claimRetrieval-Augmented Generation (RAG) is best suited for dynamic queries, enterprise search, research assistants, and constantly updating knowledge bases because it provides fresh and dynamic responses, though it has higher latency and is network dependent.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org Sep 22, 2025 1 fact
claimCGPE (Tao et al., 2024) optimizes knowledge retrieval using clue-guided path exploration and information matching from knowledge bases to enhance the capabilities of Large Language Models for unfamiliar questions and reduce operational costs.
Does the combination of sustainable business model patterns lead ... link.springer.com Feb 20, 2023 1 fact
referenceAn, Huang, Liu, and Wu (2022) published 'The match between business model design and knowledge base in firm growth: from a knowledge-based view' in Technology Analysis & Strategic Management, volume 34, issue 1, pages 99–11.