README.md

---
dataset_info:
  features:
  - name: identifier
    dtype: string
  - name: dataset
    dtype: string
  - name: question
    dtype: string
  - name: rank
    dtype: int64
  - name: url
    dtype: string
  - name: read_more_link
    dtype: string
  - name: language
    dtype: string
  - name: title
    dtype: string
  - name: top_image
    dtype: string
  - name: meta_img
    dtype: string
  - name: images
    sequence: string
  - name: movies
    sequence: string
  - name: keywords
    sequence: 'null'
  - name: meta_keywords
    sequence: string
  - name: tags
    dtype: 'null'
  - name: authors
    sequence: string
  - name: publish_date
    dtype: string
  - name: summary
    dtype: string
  - name: meta_description
    dtype: string
  - name: meta_lang
    dtype: string
  - name: meta_favicon
    dtype: string
  - name: meta_site_name
    dtype: string
  - name: canonical_link
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 28143581642
    num_examples: 2812737
  download_size: 11334496137
  dataset_size: 28143581642
configs:
- config_name: default
  data_files:
  - split: train
    path:
    - data/part_*
language:
- en
pretty_name: FactCheck
tags:
- FactCheck
- knowledge-graph
- question-answering
- classification
- FactBench
- YAGO
- DBpedia
- LLM-factuality
- fact-checking
license: mit
task_categories:
- question-answering
size_categories:
- 1M<n<10M
---
# Dataset Card for FactCheck

## 📝 Dataset Summary  
**FactCheck** is an benchmark for evaluating LLMs on **knowledge graph fact verification**. It combines structured facts from YAGO, DBpedia, and FactBench with web-extracted evidence including questions, summaries, full text, and metadata. The dataset contains examples designed for sentence-level fact-checking and QA tasks.

## 📚 Supported Tasks  
- **Question Answering**: Answer fact-checking questions derived from KG triples.
- **Benchmarking LLMs**

## 🗣 Languages  
- English (`en`)
- Maybe the dataset contains Google Search Engine Results in other language too

## 🧱 Dataset Structure  
Each example includes metadata fields, such as:

| Field             | Type     | Description |
|------------------|----------|-------------|
| `identifier`      | string   | Unique ID per example |
| `dataset`         | string   | Source KG: YAGO, DBpedia, or FactBench |
| `question`        | string   | Question derived from the fact |
| `rank`            | int      | Relevance rank of question/page |
| `url`, `read_more_link` | string | Web source links |
| `title`, `summary`, `text` | string | Extracted HTML content |
| `images`, `movies` | [string] | Media assets |
| `keywords`, `meta_keywords`, `tags`, `authors`, `publish_date`, `meta_description`, `meta_site_name`, `top_image`, `meta_img`, `canonical_link` | string or [string] | Additional metadata |

## 🚦 Data Splits  
Only a **train** split is available, aggregated across 13 source files.

## 🛠 Dataset Creation

### Curation Rationale  
Constructed to benchmark LLM performance on structured KG verification, with and without external evidence.

### Source Data  
- **FactBench**: ~2,800 facts  
- **YAGO**: ~1,400 facts  
- **DBpedia**: ~9,300 facts
- Web-scraped evidence using Google SERP for contextual support.

### Processing Steps  
- Facts retrieved and paired with search queries.  
- Web pages were scraped, parsed, cleaned, and stored.  
- Metadata normalized across all sources.  
- Optional ranking and filtering applied to prioritize high-relevance evidence.

### Provenance  
Compiled by the FactCheck‑AI team, anchored in public sources (KGs + web content).

## ⚠️ Personal & Sensitive Information  
The FactCheck dataset does not contain personal or private data. All information is sourced from publicly accessible knowledge graphs (YAGO, DBpedia, FactBench) and web-extracted evidence. However, if you identify any content that you believe may be in conflict with privacy standards or requires further review, please contact us. We are committed to addressing such concerns promptly and making necessary adjustments.

## 🧑‍💻 Dataset Curators  
FactCheck‑AI Team:

- **Farzad Shami** - University of Padua - [[email protected]](mailto:[email protected])
- **Stefano Marchesin** - University of Padua - [[email protected]](mailto:[email protected])  
- **Gianmaria Silvello** - University of Padua - [[email protected]](mailto:[email protected])

## ✉️ Contact  
For issues or questions, please raise a GitHub issue on this repo.

---

### ✅ SQL Queries for Interactive Analysis

Here are useful queries users can run in the Hugging Face SQL Console to analyze this dataset:

```sql
-- 1. Count of rows per source KG
SELECT dataset, COUNT(*) AS count
FROM train
GROUP BY dataset
ORDER BY count DESC;
````

```sql
-- 2. Daily entry counts based on publish_date
SELECT publish_date, COUNT(*) AS count
FROM train
GROUP BY publish_date
ORDER BY publish_date;
```

```sql
-- 3. Count of missing titles or summaries
SELECT
  SUM(CASE WHEN title IS NULL OR title = '' THEN 1 ELSE 0 END) AS missing_title,
  SUM(CASE WHEN summary IS NULL OR summary = '' THEN 1 ELSE 0 END) AS missing_summary
FROM train;
```

```sql
-- 4. Top 5 most frequent host domains
SELECT
  SUBSTR(url, INSTR(url, '://')+3, INSTR(SUBSTR(url, INSTR(url,'://')+3),'/')-1) AS domain,
  COUNT(*) AS count
FROM train
GROUP BY domain
ORDER BY count DESC
LIMIT 5;
```

```sql
-- 5. Average number of keywords per example
SELECT AVG(array_length(keywords, 1)) AS avg_keywords
FROM train;
```

These queries offer insights into data coverage, quality, and structure.