Update README.md

README.md

@@ -50,16 +50,142 @@ dataset_info:
   - name: text
     dtype: string
   splits:
-    - name: train
-      num_bytes: 28143581642
-      num_examples: 2812737
-
+  - 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_*"
+- 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
+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.