Papers Relation Linking
“Relation Linking” 태그가 달린 논문 17편 · 필터 해제
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text
Knowledge graphs can represent information about the real-world using entities and their relations in a structured and semantically rich manner and they enable a variety of downstream applications such as question-answer…
graph constructionIn-Context LearningKnowledge GraphsLanguage Modelling+4Implicit Relation Linking for Question Answering over Knowledge Graph
Relation linking (RL) is a vital module in knowledge-based question answering (KBQA) systems. It aims to link the relations expressed in natural language (NL) to the corresponding ones in knowledge graph (KG). Existing m…
Question AnsweringRelationRelation LinkingTargeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases
Knowledge Base Question Answering (KBQA) systems have the goal of answering complex natural language questions by reasoning over relevant facts retrieved from Knowledge Bases (KB). One of the major challenges faced by th…
Knowledge Base Question AnsweringOpen-Domain Question AnsweringQuestion AnsweringRelation LinkingComparison of biomedical relationship extraction methods and models for knowledge graph creation
Biomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the li…
Key Information ExtractionKnowledge GraphsNamed Entity Recognition (NER)Relation Extraction+1A Neural Approach to KGQA via SPARQL Silhouette Generation
Semantic parsing is a predominant approach to solve the Knowledge Graph Question Answering (KGQA) task where, natural language question is translated into a logic form such as SPARQL. Semantic parsing based …
Graph Question AnsweringMachine TranslationNMTQuestion Answering+3Generative Relation Linking for Question Answering over Knowledge Bases
Relation linking is essential to enable question answering over knowledge bases. Although there are various efforts to improve relation linking performance, the current state-of-the-art methods do not achieve optimal res…
Question AnsweringRelationRelation LinkingA Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. How…
Knowledge Base Question AnsweringQuestion AnsweringRelationRelation Linking+1Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules. However, the task of extracting and linking relations from text to knowledgebases faces two primary challenges; t…
Abstract Meaning RepresentationQuestion AnsweringRelationRelation Extraction+2End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings
Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on measuring the string similarity between…
Entity DisambiguationEntity LinkingKnowledge Graph EmbeddingsQuestion Answering+3Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Con…
Knowledge Base Question AnsweringKnowledge GraphsLanguage ModellingRelation+1Towards Combinational Relation Linking over Knowledge Graphs
Given a natural language phrase, relation linking aims to find a relation (predicate or property) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language …
Knowledge GraphsQuestion AnsweringRelationRelation Linking+1Distantly Supervised Question Parsing
The emergence of structured databases for Question Answering (QA) systems has led to developing methods, in which the problem of learning the correct answer efficiently is based on a linking task between the constituents…
Knowledge GraphsQuestion AnsweringReinforcement LearningRelation Linking+1Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering
Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking…
Question AnsweringRelationRelation LinkingOld is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text
Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long t…
Entity LinkingImplicit Relationsnamed-entity-recognitionNamed Entity Recognition+3EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and rel…
Entity LinkingKnowledge GraphsQuestion AnsweringRelation+2Tapping the sensorimotor trajectory
In this paper, we propose the concept of sensorimotor tappings, a new graphical technique that explicitly represents relations between the time steps of an agent's sensorimotor loop and a single training step of an adapt…
Relation LinkingDiverse Neural Network Learns True Target Functions
Neural networks are a powerful class of functions that can be trained with simple gradient descent to achieve state-of-the-art performance on a variety of applications. Despite their practical success, there is a paucity…
DiversityRelation Linking