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https://github.com/seukgcode/MELBench
Multimodal entity linking (MEL) aims to utilize multimodal information to map mentions to corresponding entities defined in knowledge bases. We release three MEL datasets: Weibo-MEL, Wikidata-MEL and Richpedia-MEL, containing 25,602, 18,880 and 17,806 samples from social media, encyclopedia and multimodal knowledge graphs respectively. A MEL dataset construction approach is proposed, including five stages: multimodal information extraction, mention extraction, entity extraction, triple construction and dataset construction. Experiment results demonstrate the usability of the datasets and the distinguishability between baseline models.
https://github.com/seukgcode/MELBench
entity-linking knowledge-graph multimodal
Last synced: about 1 month ago
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Multimodal entity linking (MEL) aims to utilize multimodal information to map mentions to corresponding entities defined in knowledge bases. We release three MEL datasets: Weibo-MEL, Wikidata-MEL and Richpedia-MEL, containing 25,602, 18,880 and 17,806 samples from social media, encyclopedia and multimodal knowledge graphs respectively. A MEL dataset construction approach is proposed, including five stages: multimodal information extraction, mention extraction, entity extraction, triple construction and dataset construction. Experiment results demonstrate the usability of the datasets and the distinguishability between baseline models.
- Host: GitHub
- URL: https://github.com/seukgcode/MELBench
- Owner: seukgcode
- License: mit
- Created: 2021-07-03T06:23:27.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-07-31T14:46:23.000Z (over 3 years ago)
- Last Synced: 2024-08-03T09:07:10.304Z (5 months ago)
- Topics: entity-linking, knowledge-graph, multimodal
- Homepage:
- Size: 8.79 MB
- Stars: 77
- Watchers: 4
- Forks: 4
- Open Issues: 4
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - seukgcode/MELBench. - MEL、Wikidata-MEL 和 Richpedia-MEL，分别包含来自社交媒体、百科全书和多模态知识图谱的 25,602、18,880 和 17,806 个样本。 (知识图谱 / 其他_文本生成、文本对话)
README
# Multimodal Entity Linking Datasets Benchmark
## 1. Abstract
Multimodal entity linking (MEL) aims to utilize multimodal information to map mentions to corresponding entities defined in knowledge bases. We release three MEL datasets: Weibo-MEL, Wikidata-MEL and Richpedia-MEL, containing 25,602, 18,880 and 17,806 samples from social media, encyclopedia and multimodal knowledge graphs respectively. A MEL dataset construction approach is proposed, including five stages: multimodal information extraction, mention extraction, entity extraction, triple construction and dataset construction. Experiment results demonstrate the usability of the datasets and the distinguishability between baseline models.
## 2. Introduction
Multimodal entity linking (MEL) aims to utilize multimodal information to map mentions to corresponding entities defined in knowledge bases. We release three MEL datasets: Weibo-MEL, Wikidata-MEL and Richpedia-MEL, containing 25,602, 18,880 and 17,806 samples from social media, encyclopedia and multimodal knowledge graphs respectively. A MEL dataset construction approach is proposed, including five stages: multimodal information extraction, mention extraction, entity extraction, triple construction and dataset construction. Experiment results demonstrate the usability of the datasets and the distinguishability between baseline models.
To construct large-scale MEL datasets, we propose a MEL dataset construction approach, including five stages. In **Multimodal Information Extraction**, we select multimodal data sources and extract textual and visual information. In **Mention Extraction**, we extract mentions from textual information and keep the mentions which corresponding entities may exist. In **Entity Extraction**, we query the knowledge bases with the filtered mentions, gather the entity lists, and save the correct entities. In **Triple Construction**, we merge the corresponding mentions and entities into mention-entity (M-E) pairs, and combine them into triples with textual and visual information. Then, we keep the correct triples as the samples of the MEL dataset. Finally, in **Dataset Construction** stage, we partition the dataset into training set (70%), validation set (10%) and testing set (20%). The overview of the approach is illustrated in figure below.
## 3. Visual Information and Knowledge Graph Resources
Due to the large size of the visual information and knowledge graph resources, we deposited these resources in the Baidu Cloud Disk.
Visual information download addresses:
* [Weibo-MEL Visual Information](https://pan.baidu.com/s/1VTzzKXpORziookJiHKwWKw)
* [Wikidata-MEL Visual Information](https://pan.baidu.com/s/1FbhgMZ-w2DdAPLgCBDvKtQ)
* [Richpedia-MEL Visual Information](https://pan.baidu.com/s/1lt-SmWUX5GAmLRNWggDkXQ?from=init#list/path=%2Fsharelink653312845-459959024382112%2Fimage&parentPath=%2Fsharelink653312845-459959024382112)
Knowledge graph download addresses:
* [Wikidata Knowledge Graph for Weibo-MEL and Wikidata-MEL](https://dumps.wikimedia.org/wikidatawiki/entities/latest-all.json.bz2)
* [Richpedia Knowledge Graph for Richpedia-MEL](https://pan.baidu.com/s/1FTEwSV6CystQHT_wgdjYEA)
The extraction codes are 2021.
## 4. Samples of the MEL datasets
### 4.1 Weibo-MEL dataset
**Visual information:**
* 
**Textual information:**
* 【感谢30年的坚守！#辽宁舰首位一级军士长退役# 】近日，辽宁舰为首位一级军士长刘德波举行了隆重的退役仪式。刘德波，1990年12月入伍，服役期间，荣获全军士官优秀人才奖二等奖1次，荣立二等功1次，三等功4次。入伍30年，他坚守在不见阳光的深舱，始终与高温和热浪相伴，穿梭于管路和设备之间，把最美好的青春年华都献给了心爱的战舰。致敬！（北海舰队）#我为中国军人点赞# [组图共9张] 原图
**Mention-entity pairs:**
* "刘德波": "刘德波"
* "中国": "中国（世界四大文明古国之一）"
* "辽宁舰": "中国人民解放军海军辽宁舰"
### 4.2 Wikidata-MEL dataset
**Visual information:**
* 
**Textual information:**
* Seattle Mayor Charles L. Smith (left) with Will Rogers, circa 1935.
**Mention-entity pairs:**
* "Charles L. Smith": "Charles L. Smith (Seattle politician)"
* "Will Rogers": "Will Rogers"
### 4.3 Richpedia-MEL dataset
**Visual information:**
* 
**Textual information:**
* Washington resigned his commission after the Treaty of Paris in 1783.
**Mention-entity pairs:**
* "Washington": "George Washington"