INCEpTION provides a semantic annotation platform offering intelligent annotation assistance and knowledge management.
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Updated
Jun 2, 2024 - Java
Entity resolution (also known as data matching, data linkage, record linkage, and many other terms) is the task of finding entities in a dataset that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Entity resolution is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference.
INCEpTION provides a semantic annotation platform offering intelligent annotation assistance and knowledge management.
💫 Industrial-strength Natural Language Processing (NLP) in Python
Insightful Tutorials and Papers about Knowledge Graphs
Curated list of awesome software and resources for Senzing, The First Real-Time AI for Entity Resolution.
GERBIL - General Entity annotatoR Benchmark
Rosette API Client Library for Java
Named Entity Recognition, Entity Linking, and more
Biomedical Term Annotator
Simple SNOMED-CT assistant app demo by Streamlit.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Code of "A Read-and-Select Framework for Zero-shot Entity Linking" (EMNLP 2023 Findings).
Entity linking evaluation and analysis tool
Spark RDD with Lucene's query and entity linkage capabilities
Project that aims to sentenize all the open data of Riksdagen and other sources to create an easily linkable dataset of sentences that can be refered to from Wikidata lexemes and other resources
Fast dictionary-based approach for semantic annotation / entity linking
Corpus of Online Medical EnTities: the cometA corpus
Combining Linking Techniques (CLiT) is an entity linking combination and execution framework, allowing for the seamless integration of EL systems and result exploitation for the sake of system reusability, result reproducibility, analysis and continuous improvement. (We hate waste. Especially wasting time. So let's reuse instead!)
A Metric learning-based Method for Biomedical Entity Linking
Attention-based approach to NIL Entity Linking
Created by Halbert L. Dunn
Released 1946