# Scientific Semantic Scholar > Semantic Scholar 学術グラフスキル。Semantic Scholar Academic Graph API による論文検索・著者プロファイル・引用グラフ・ 推薦・TLDR 要約。ToolUniverse 連携: semantic_scholar。 - Skill: `nahisaho/scientific-semantic-scholar-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add nahisaho/scientific-semantic-scholar-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-semantic-scholar-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: nahisaho (https://skillmd.com/u/nahisaho) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/nahisaho/scientific-semantic-scholar-2 --- # Scientific Semantic Scholar Semantic Scholar Academic Graph API を活用した学術論文検索・ 引用ネットワーク解析・著者プロファイル・論文推薦パイプライン を提供する。 ## When to Use - 学術論文を高精度で検索するとき - 引用・被引用ネットワークを解析するとき - 著者の h-index・論文数・研究領域を調べるとき - 関連論文の推薦を受けるとき - TLDR (自動要約) を取得するとき - 特定分野の引用傾向を分析するとき - PubMed/OpenAlex 以外の学術検索エンジンを使うとき --- ## Quick Start ## 1. 論文検索 ```python import requests import pandas as pd S2_BASE = "https://api.semanticscholar.org/graph/v1" S2_HEADERS = {} # API key: {"x-api-key": "YOUR_KEY"} def semantic_scholar_search(query, limit=50, year_range=None, fields_of_study=None): """ Semantic Scholar — 論文検索。 Parameters: query: str — 検索クエリ limit: int — 最大結果数 year_range: str — 年範囲 (例: "2020-2024") fields_of_study: list[str] — 分野フィルタ """ url = f"{S2_BASE}/paper/search" params = { "query": query, "limit": min(limit, 100), "fields": ("paperId,title,year,citationCount," "influentialCitationCount,authors," "journal,tldr,openAccessPdf,fieldsOfStudy"), } if year_range: params["year"] = year_range if fields_of_study: params["fieldsOfStudy"] = ",".join(fields_of_study) resp = requests.get(url, params=params, headers=S2_HEADERS, timeout=30) resp.raise_for_status() data = resp.json() results = [] for p in data.get("data", []): authors = [a.get("name", "") for a in p.get("authors", [])] tldr_text = "" if p.get("tldr"): tldr_text = p["tldr"].get("text", "") results.append({ "paper_id": p.get("paperId", ""), "title": p.get("title", ""), "year": p.get("year"), "citation_count": p.get("citationCount", 0), "influential_citations": p.get( "influentialCitationCount", 0), "authors": "; ".join(authors[:5]), "journal": (p.get("journal") or {}).get("name", ""), "fields": ", ".join(p.get("fieldsOfStudy") or []), "tldr": tldr_text[:300], "pdf_url": (p.get("openAccessPdf") or {}).get("url", ""), }) df = pd.DataFrame(results) print(f"Semantic Scholar: {len(df)} papers " f"(query='{query}')") return df def semantic_scholar_get_paper(paper_id): """ Semantic Scholar — 論文詳細取得。 Parameters: paper_id: str — S2 Paper ID / DOI / ArXiv ID """ url = f"{S2_BASE}/paper/{paper_id}" params = { "fields": ("paperId,title,year,abstract,citationCount," "influentialCitationCount,authors,references," "citations,journal,tldr,openAccessPdf," "fieldsOfStudy,publicationDate,venue"), } resp = requests.get(url, params=params, headers=S2_HEADERS, timeout=30) resp.raise_for_status() return resp.json() ``` ## 2. 著者プロファイル・引用解析 ```python def semantic_scholar_author(author_id, paper_limit=100): """ Semantic Scholar — 著者プロファイル取得。 Parameters: author_id: str — S2 Author ID paper_limit: int — 取得論文数上限 """ url = f"{S2_BASE}/author/{author_id}" params = { "fields": ("authorId,name,affiliations,homepage," "paperCount,citationCount,hIndex"), } resp = requests.get(url, params=params, headers=S2_HEADERS, timeout=30) resp.raise_for_status() profile = resp.json() # 論文一覧 papers_url = f"{S2_BASE}/author/{author_id}/papers" p_params = { "fields": "paperId,title,year,citationCount,venue", "limit": min(paper_limit, 1000), } p_resp = requests.get(papers_url, params=p_params, headers=S2_HEADERS, timeout=30) p_resp.raise_for_status() papers = [] for p in p_resp.json().get("data", []): papers.append({ "paper_id": p.get("paperId", ""), "title": p.get("title", ""), "year": p.get("year"), "citations": p.get("citationCount", 0), "venue": p.get("venue", ""), }) papers_df = pd.DataFrame(papers) print(f"Author {profile.get('name', '')}: " f"h-index={profile.get('hIndex', 0)}, " f"{profile.get('paperCount', 0)} papers, " f"{profile.get('citationCount', 0)} citations") return profile, papers_df ``` ## 3. 引用ネットワーク・影響度分析 ```python def semantic_scholar_citation_graph(paper_id, direction="both", limit=100): """ Semantic Scholar — 引用グラフ取得。 Parameters: paper_id: str — S2 Paper ID direction: str — "citations", "references", "both" limit: int — 各方向の上限 """ graphs = {} fields = "paperId,title,year,citationCount,authors" if direction in ("citations", "both"): url = f"{S2_BASE}/paper/{paper_id}/citations" resp = requests.get(url, params={"fields": fields, "limit": limit}, headers=S2_HEADERS, timeout=30) resp.raise_for_status() cites = [] for c in resp.json().get("data", []): cp = c.get("citingPaper", {}) cites.append({ "paper_id": cp.get("paperId", ""), "title": cp.get("title", ""), "year": cp.get("year"), "citations": cp.get("citationCount", 0), }) graphs["citations"] = pd.DataFrame(cites) if direction in ("references", "both"): url = f"{S2_BASE}/paper/{paper_id}/references" resp = requests.get(url, params={"fields": fields, "limit": limit}, headers=S2_HEADERS, timeout=30) resp.raise_for_status() refs = [] for r in resp.json().get("data", []): rp = r.get("citedPaper", {}) refs.append({ "paper_id": rp.get("paperId", ""), "title": rp.get("title", ""), "year": rp.get("year"), "citations": rp.get("citationCount", 0), }) graphs["references"] = pd.DataFrame(refs) for k, v in graphs.items(): print(f" {k}: {len(v)} papers") return graphs ``` ## 4. 学術文献統合パイプライン ```python def semantic_scholar_pipeline(query, year_range=None, output_dir="results"): """ Semantic Scholar 統合パイプライン。 Parameters: query: str — 検索クエリ year_range: str — 年範囲 output_dir: str — 出力ディレクトリ """ from pathlib import Path output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) # 1) 論文検索 papers = semantic_scholar_search(query, year_range=year_range) papers.to_csv(output_dir / "papers.csv", index=False) # 2) トップ被引用論文の引用グラフ if not papers.empty: top = papers.sort_values("citation_count", ascending=False).iloc[0] pid = top["paper_id"] graphs = semantic_scholar_citation_graph(pid) for k, df in graphs.items(): df.to_csv(output_dir / f"{k}.csv", index=False) # 3) 年次引用傾向 if not papers.empty and "year" in papers.columns: yearly = papers.groupby("year").agg( papers_count=("paper_id", "count"), total_citations=("citation_count", "sum"), avg_citations=("citation_count", "mean"), ).reset_index() yearly.to_csv(output_dir / "yearly_trend.csv", index=False) print(f"Semantic Scholar pipeline: {output_dir}") return {"papers": papers} ``` --- ## ToolUniverse 連携 | TU Key | ツール名 | 連携内容 | |--------|---------|---------| | `semantic_scholar` | Semantic Scholar | 論文検索・引用解析・著者・TLDR | ## パイプライン統合 ``` literature-search → semantic-scholar → deep-research (PubMed/NCBI) (Academic Graph API) (knowledge synthesis) │ │ ↓ crossref-metadata ─────┘ citation-checker (DOI/metadata) │ (引用品質検証) ↓ gene-expression-transcriptomics (論文引用データからの解析) ``` ## パイプライン出力 | ファイル | 説明 | 次スキル | |---------|------|---------| | `results/papers.csv` | 論文検索結果 | → deep-research | | `results/citations.csv` | 被引用論文 | → citation-checker | | `results/references.csv` | 引用論文 | → meta-analysis | | `results/yearly_trend.csv` | 年次引用傾向 | → bibliometrics |