Google Scholar Guide

A skill for leveraging Google Scholar's full capabilities for academic literature search. Covers advanced search operators, citation tracking, alert configuration, and strategies for systematic and comprehensive retrieval.

Advanced Search Operators

Core Operators

Operator	Syntax	Example	Effect
Exact phrase	"..."	"machine learning"	Matches exact phrase
OR	OR	"deep learning" OR "neural network"	Matches either term
Exclude	-	transformer -electrical	Excludes term
Author	author:	author:"Y LeCun"	Filter by author
Source	source:	source:"Nature"	Filter by journal
Title only	intitle:	intitle:"attention mechanism"	Search in title only
Date range	Custom range	Via Advanced Search UI	Limit publication years
File type	filetype:	filetype:pdf	Specific file formats

Constructing Effective Queries

def build_scholar_query(concepts: list[list[str]], exclude: list[str] = None,
                         title_only: bool = False, author: str = None,
                         source: str = None) -> str:
    """
    Build a structured Google Scholar query from concept groups.

    Args:
        concepts: List of concept groups, each a list of synonyms
                  Groups are ANDed together, synonyms are ORed
        exclude: Terms to exclude
        title_only: Search in title only
        author: Author name filter
        source: Journal/source filter
    Returns:
        Formatted Google Scholar query string
    """
    # Build concept groups with OR
    groups = []
    for concept_group in concepts:
        if len(concept_group) == 1:
            groups.append(f'"{concept_group[0]}"')
        else:
            terms = ' OR '.join(f'"{term}"' for term in concept_group)
            groups.append(f'({terms})')

    # AND the concept groups together
    query = ' '.join(groups)

    # Apply title restriction
    if title_only:
        query = f'intitle:{query}'

    # Add exclusions
    if exclude:
        for term in exclude:
            query += f' -{term}'

    # Add author filter
    if author:
        query += f' author:"{author}"'

    # Add source filter
    if source:
        query += f' source:"{source}"'

    return query

# Example: find papers on transfer learning for medical imaging
query = build_scholar_query(
    concepts=[
        ["transfer learning", "domain adaptation", "fine-tuning"],
        ["medical imaging", "radiology", "pathology images"],
        ["deep learning", "convolutional neural network"]
    ],
    exclude=["survey", "review"],
    title_only=False
)
print(query)
# Output: ("transfer learning" OR "domain adaptation" OR "fine-tuning")
#         ("medical imaging" OR "radiology" OR "pathology images")
#         ("deep learning" OR "convolutional neural network") -survey -review