Knowledge Tree
triplets
Also known as: triples, triplets
Facts (36)
Sources
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 19 facts
claimA knowledge base is a data set that represents real-world facts and semantic relations in the form of triplets.
claimConvKB, introduced by Nguyen et al. in 2017, utilizes a convolutional neural network (CNN) to conduct knowledge graph embeddings by representing each triplet (h, r, t) as a three-row matrix A, which is input to a convolution layer to obtain feature maps.
claimThe types of additional information that can be incorporated into the features of triplets are currently severely limited.
claimSingle-hop prediction predicts one element of a triplet for given two elements, whereas multi-hop reasoning predicts one or more elements in a multi-hop logical query.
procedureKnowledge graph completion models train machine learning models on existing graphs to assess the plausibility of new candidate triplets and add those with high plausibility to the graph.
claimMany established methods for generating knowledge graph embeddings suffer from limitations because they only consider surface facts (triplets) and ignore additional information such as entity types and relation paths, which could otherwise improve embedding accuracy.
claimNeural network-based methods for knowledge graph embeddings employ deep learning to represent triplets, with representative works including SME, ConvKB, and R-GCN (Dai et al. 2020a).
claimClosed-world knowledge graph completion methods cannot predict new entities, such as predicting the triplet (Tom, friendOf, Jerry) for the incomplete triplet (Tom, friendOf, ?), unless the entity Jerry is already present in the knowledge graph.
referenceNeural network-based knowledge reasoning utilizes neural networks to infer new triplets given the body of knowledge in the graph (Xian et al. 2019).
procedureKnowledge graph completion typically utilizes link prediction techniques to generate triplets and subsequently assigns plausibility scores to those triplets (Ji et al. 2021).
claimMost current knowledge graph completion methods are limited to closed-world data sources, meaning they require entities or relations to already exist in the knowledge graph to generate new triplets.
claimThe predicate constraint-based question-answering system (PCQA) presented in 2019 utilizes knowledge graph predicate constraints—triplets consisting of a subject, predicate, and object—to capture connections between questions and answers, thereby simplifying processing and improving results.
formulaIn a knowledge graph, two nodes (e1 and e2) connected by a relation (r1) form a triplet (e1, r1, e2), where e1 is the head entity and e2 is the tail entity.
claimA knowledge graph is a representation of triplets as a graph where edges represent relations and nodes represent entities.
claimThe SME (Semantic Matching Energy) model, as described in 2014, utilizes neural networks to design an energy function that measures the confidence of each triplet (h, r, t) in knowledge graphs.
claimKnowledge graphs are frequently incomplete, often missing relevant triplets and entities, as noted by Zhang et al. (2020a).
claimGeneral additional information often fails to represent the semantic meaning of triplets because entity types are not inherently related to the semantic information of triplets.
referenceHuang et al. (2019) proposed a knowledge graph embedding-based question-answering system (KEQA) that translates questions and answers into single triplets, such as (Leonardo, act, Inception), and represents the head entity, relation, and tail entity as a vector matrix in an embedding space.
claimIn knowledge graph-based question-answering systems, simple questions are answered by referring to a single triplet, while multi-hop questions require combining multiple entities and relations.
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 3 facts
claimIn RDF-Star, relations (triples) are identified based on their associated elements (s1, p1, o1, p2, o2), making it impossible to attach different sets of information to equally named relations without causing incorrect connections or relying on support constructs like singleton properties.
claimRelation extraction can convert text snippets into triples, such as transforming the text 'album Syro' into the triple 'dbr:Syro rdf:type dbo:Album'.
referenceKnowledge graphs represented using the Resource Description Framework consist of sets of <subject, predicate, object> triples, where predicates represent named relations between subjects and either attribute values (literals) or other entities (objects).
A Knowledge-Graph Based LLM Hallucination Evaluation Framework themoonlight.io 2 facts
procedureThe GraphCorrect strategy rectifies hallucinations by identifying inconsistent triples, sending the problematic triple and context back to an LLM to generate a corrected version, and substituting the new triple into the original output to ensure localized correction without altering unaffected sections.
procedureThe GraphEval framework constructs a Knowledge Graph from LLM output through a four-step pipeline: (1) processing input text, (2) detecting unique entities, (3) performing coreference resolution to retain only specific references, and (4) extracting relations to form triples of (entity1, relation, entity2).
KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org Mar 18, 2025 2 facts
procedureThe KG-IRAG iterative RAG process proceeds as follows: (1) identify the starting time and location, (2) perform KG exploration to retrieve relevant triplets, (3) evaluate the retrieved data using LLM2 to determine if the problem is solved, (4) if unresolved, adjust search criteria by moving to a different time or location, and (5) continue retrieving new triplets until the answer is generated.
claimGraph Retrieval-Augmented Generation (GraphRAG) offers an advantage over traditional RAG systems by retrieving knowledge from graph databases and utilizing triplets as the primary data source.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 2 facts
referenceK-BERT (Liu et al., 2020) is a knowledge-based model that injects triples into sentences as domain knowledge and utilizes soft-position and visible matrix mechanisms to mitigate the impact of knowledge noise.
procedureGeneration-retrieval frameworks for knowledge graph question answering, such as ChatKBQA (Luo H. et al., 2023) and GoG (Xu et al., 2024), use a two-stage approach that generates logical forms or new triples before retrieving relevant knowledge graph elements.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 1 fact
referenceThe World Wide Web Consortium (W3C) proposed the Resource Description Framework (RDF) in the 1990s to standardize data interchange on the web using triples (subject, predicate, object) for data integration and interoperability.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org May 20, 2024 1 fact
procedureThe Storage stage of KG-RAG involves transforming unstructured text data into a structured knowledge graph by extracting triples formatted as (entity)[relationship](entity).
A Knowledge-Graph Based LLM Hallucination Evaluation Framework arxiv.org Jul 15, 2024 1 fact
claimGraphEval identifies specific triples within a Knowledge Graph that are prone to hallucinations, providing insight into the location of hallucinations within an LLM response.
Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org Aug 7, 2025 1 fact
claimThe EntityRelationNormalizer tool cleans noisy natural language text and technical documents to ensure compatibility with graph storage systems, such as removing colons from entity labels or unescaped null characters from RDF triples.
Empowering GraphRAG with Knowledge Filtering and Integration arxiv.org Mar 18, 2025 1 fact
formulaThe GraphRAG-Filtering method utilizes a two-stage filtering process to select retrieved paths or triplets for LLM prompts, where the set of retrieved paths is denoted as P and each path is assigned an attention score α.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 1 fact
claimLanguage models can extract triples from unstructured texts to enrich knowledge graphs with new knowledge that can be added to the graph structure.
Empowering RAG Using Knowledge Graphs: KG+RAG = G-RAG neurons-lab.com 1 fact
referenceA Knowledge Graph represents knowledge as a set of triplets, where each triplet consists of a Head (Subject), a Relation (Predicate), and a Tail (Object). An example is 'Neurons Lab (Head) is located in (Relation) Europe (Tail).'
Applying Large Language Models in Knowledge Graph-based ... arxiv.org Jan 7, 2025 1 fact
claimKnowledge graphs are typically organized in triples consisting of a subject, predicate, and object, and they show relationships between entities to uncover complex interrelations.