SELECT * FROM Articles WHERE topic = 'Renewable Energy' AND content LIKE '%economic benefits%';
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Construct a query to find an article about the economic benefits of renewable energy sources.

### Response:
SELECT * FROM Articles WHERE topic = 'Renewable Energy' AND content LIKE '%economic benefits%';
assistant

To find an article about the economic benefits of renewable energy sources, you can use the following query:

```sql
SELECT *
FROM articles
WHERE title LIKE '%economic benefits of renewable energy sources%' OR content LIKE '%renewable energy sources benefits%' OR content LIKE '%renewable energy benefits%'
ORDER BY created_at DESC;
```

Replace `articles` with the actual table name where the articles are stored. 

This query will return the article titles that contain the phrase "economic benefits of renewable energy sources" or "renewable energy sources benefits" along with the "renewable energy benefits" phrase. The results will be ordered by the article's creation date in descending order.

Note: This query is case-insensitive and uses OR operators to match multiple phrases. It's also assumed that the table columns are `title`, `content`, and `created_at`. You may need to adjust the query based on your specific database schema and table structure.

Alternatively, you can use a more advanced query using natural language processing techniques, such as keyword extraction and ranking. This approach can be more effective for finding relevant articles but requires more complex setup and maintenance.

Here's an example of a Python query using Natural Language Toolkit (NLTK) and scikit-learn libraries:

```python
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

nltk.download('wordnet')
nltk.download('stopwords')

def extract_keywords(article):
    lemmatizer = WordNetLemmatizer()
    stop_words = set(stopwords.words('english'))
    keywords = set()
    for word in nltk.word_tokenize(article):
        if word.lower() not in stop_words:
            lemmatized_word = lemmatizer.lemmatize(word, 'v')
            keywords.add(lemmatized_word)
    return keywords

def find_relevant_articles(query):
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform([article['content'] for article in articles])
    query_vector = vectorizer.transform([query])
    similarities = cosine_similarity(tfidf_matrix, query_vector)
    relevant_articles = []
    for article in articles:
        score = similarities[0, 1]
        if score > 0.5:
            relevant_articles.append(article)
    return relevant_articles

articles = [...]  # Replace with your actual article data
query = 'economic benefits of renewable energy sources'
relevant_articles = find_relevant_articles(query)
for article in relevant_articles:
    print(article['title'])
```

This query uses the TF-IDF vectorizer to transform the article content into a matrix of word frequencies, then calculates the cosine similarity between the query and each article's content. Articles with a similarity score above 0.5 are considered relevant.