Papers Entity Resolution
“Entity Resolution” 태그가 달린 논문 214편 · 필터 해제
Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text
Process-aware information systems offer extensive advantages to companies, facilitating planning, operations, and optimization of day-to-day business activities. However, the time-consuming but required step of designing…
Entity ResolutionFeature Engineeringnamed-entity-recognitionNamed Entity Recognition+1Using ChatGPT for Entity Matching
Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models such as BERT or RoBERTa. Two m…
Data IntegrationEntity ResolutionIn-Context LearningUnicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
Data matching - which decides whether two data elements (e.g., string, tuple, column, or knowledge graph entity) are the "same" (a.k.a. a match) - is a key concept in data integration, such as entity matching and schema …
Data IntegrationEntity ResolutionMixture-of-ExpertsZero-Shot LearningPre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]
Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained…
BlockingDeep LearningEntity ResolutionA Framework for Combining Entity Resolution and Query Answering in Knowledge Bases
We propose a new framework for combining entity resolution and query answering in knowledge bases (KBs) with tuple-generating dependencies (tgds) and equality-generating dependencies (egds) as rules. We define the semant…
AttributeEntity ResolutionAnother Generic Setting for Entity Resolution: Basic Theory
Benjelloun et al. \cite{BGSWW} considered the Entity Resolution (ER) problem as the generic process of matching and merging entity records judged to represent the same real world object. They treated the functions for ma…
Entity ResolutionSC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines
The goal of entity resolution is to identify records in multiple datasets that represent the same real-world entity. However, comparing all records across datasets can be computationally intensive, leading to long runtim…
BlockingContrastive LearningEntity ResolutionWDC Products: A Multi-Dimensional Entity Matching Benchmark
The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the si…
Contrastive LearningData IntegrationEntity ResolutionMulti-class ClassificationKAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution
Entity resolution has been an essential and well-studied task in data cleaning research for decades. Existing work has discussed the feasibility of utilizing pre-trained language models to perform entity resolution and a…
Entity ResolutionGeneral KnowledgeLanguage ModelingLanguage ModellingPIZZA: A new benchmark for complex end-to-end task-oriented parsing
Much recent work in task-oriented parsing has focused on finding a middle ground between flat slots and intents, which are inexpressive but easy to annotate, and powerful representations such as the lambda calculus, whic…
Entity ResolutionDeduplication Over Heterogeneous Attribute Types (D-HAT)
Deduplication is the task of recognizing multiple representations of the same real-world object. The majority of existing solutions focuses on textual data, this means that data sets containing boolean and numerical attr…
AttributeClusteringEntity ResolutionFeature Engineering+1Introducing Semantics into Speech Encoders
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recognition (ASR) to large language model (LLM)…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Entity Resolutionintent-classification+10Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
This paper introduces a novel evaluation methodology for entity resolution algorithms. It is motivated by PatentsView.org, a U.S. Patents and Trademarks Office patent data exploration tool that disambiguates patent inven…
Entity ResolutionProbing the Robustness of Pre-trained Language Models for Entity Matching
The paradigm of fine-tuning Pre-trained Language Models (PLMs) has been successful in Entity Matching (EM). Despite their remarkable performance, PLMs exhibit tendency to learn spurious correlations from training data. I…
Data AugmentationDomain GeneralizationEntity ResolutionFlexER: Flexible Entity Resolution for Multiple Intents
Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat entity resolution as a universal task, ass…
Entity ResolutionGraph Neural NetworkMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEntity Resolution with Hierarchical Graph Attention Networks
Entity Resolution (ER) links entities that refer to the same real-world entity from different sources. Existing work usually takes pairs of entities as input and judges those pairs independently. However, there is often …
AttributeEntity ResolutionGraph AttentionWord EmbeddingsDomain Adaptation for Deep Entity Resolution: A Design Space Exploration
Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. …
Data IntegrationDomain AdaptationEntity ResolutionLow-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI
Entity Resolution (ER) in voice assistants is a prime component during run time that resolves entities in users request to real world entities. ER involves two major functionalities 1. Relevance generation and 2. Ranking…
Entity ResolutionCan Foundation Models Wrangle Your Data?
Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations conti…
Entity ResolutionImputationManagementBridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance i…
Entity Resolution