本文中咱们将解释如何构建KG、剖析它以及创立嵌入模型。
构建常识图谱
加载咱们的数据。在本文中咱们将从头创立一个简略的KG。
import pandas as pd # Define the heads, relations, and tails head = ['drugA', 'drugB', 'drugC', 'drugD', 'drugA', 'drugC', 'drugD', 'drugE', 'gene1', 'gene2','gene3', 'gene4', 'gene50', 'gene2', 'gene3', 'gene4'] relation = ['treats', 'treats', 'treats', 'treats', 'inhibits', 'inhibits', 'inhibits', 'inhibits', 'associated', 'associated', 'associated', 'associated', 'associated', 'interacts', 'interacts', 'interacts'] tail = ['fever', 'hepatitis', 'bleeding', 'pain', 'gene1', 'gene2', 'gene4', 'gene20', 'obesity', 'heart_attack', 'hepatitis', 'bleeding', 'cancer', 'gene1', 'gene20', 'gene50'] # Create a dataframe df = pd.DataFrame({'head': head, 'relation': relation, 'tail': tail}) df接下来，创立一个NetworkX图(G)来示意KG。DataFrame (df)中的每一行都对应于KG中的三元组(头、关系、尾)。add_edge函数在头部和尾部实体之间增加边，关系作为标签。
import networkx as nx import matplotlib.pyplot as plt # Create a knowledge graph G = nx.Graph() for _, row in df.iterrows(): G.add_edge(row['head'], row['tail'], label=row['relation'])而后，绘制节点(实体)和边(关系)以及它们的标签。
# Visualize the knowledge graph pos = nx.spring_layout(G, seed=42, k=0.9) labels = nx.get_edge_attributes(G, 'label') plt.figure(figsize=(12, 10)) nx.draw(G, pos, with_labels=True, font_size=10, node_size=700, node_color='lightblue', edge_color='gray', alpha=0.6) nx.draw_networkx_edge_labels(G, pos, edge_labels=labels, font_size=8, label_pos=0.3, verticalalignment='baseline') plt.title('Knowledge Graph') plt.show()当初咱们能够进行一些剖析。
剖析
对于KG，咱们能够做的第一件事是查看它有多少个节点和边，并剖析它们之间的关系。
num_nodes = G.number_of_nodes() num_edges = G.number_of_edges() print(f'Number of nodes: {num_nodes}') print(f'Number of edges: {num_edges}') print(f'Ratio edges to nodes: {round(num_edges / num_nodes, 2)}')1、节点核心性剖析
节点核心性度量图中节点的重要性或影响。它有助于辨认图构造的核心节点。一些最常见的核心性度量是:
Degree centrality 计算节点上关联的边的数量。核心性越高的节点连贯越严密。
degree_centrality = nx.degree_centrality(G) for node, centrality in degree_centrality.items(): print(f'{node}: Degree Centrality = {centrality:.2f}')Betweenness centrality 掂量一个节点位于其余节点之间最短门路上的频率，或者说掂量一个节点对其余节点之间信息流的影响。具备高中间性的节点能够作为图的不同局部之间的桥梁。
betweenness_centrality = nx.betweenness_centrality(G) for node, centrality in betweenness_centrality.items(): print(f'Betweenness Centrality of {node}: {centrality:.2f}')Closeness centrality 量化一个节点达到图中所有其余节点的速度。具备较高靠近核心性的节点被认为更具核心性，因为它们能够更无效地与其余节点进行通信。
closeness_centrality = nx.closeness_centrality(G) for node, centrality in closeness_centrality.items(): print(f'Closeness Centrality of {node}: {centrality:.2f}')可视化
# Calculate centrality measures degree_centrality = nx.degree_centrality(G) betweenness_centrality = nx.betweenness_centrality(G) closeness_centrality = nx.closeness_centrality(G) # Visualize centrality measures plt.figure(figsize=(15, 10)) # Degree centrality plt.subplot(131) nx.draw(G, pos, with_labels=True, font_size=10, node_size=[v * 3000 for v in degree_centrality.values()], node_color=list(degree_centrality.values()), cmap=plt.cm.Blues, edge_color='gray', alpha=0.6) plt.title('Degree Centrality') # Betweenness centrality plt.subplot(132) nx.draw(G, pos, with_labels=True, font_size=10, node_size=[v * 3000 for v in betweenness_centrality.values()], node_color=list(betweenness_centrality.values()), cmap=plt.cm.Oranges, edge_color='gray', alpha=0.6) plt.title('Betweenness Centrality') # Closeness centrality plt.subplot(133) nx.draw(G, pos, with_labels=True, font_size=10, node_size=[v * 3000 for v in closeness_centrality.values()], node_color=list(closeness_centrality.values()), cmap=plt.cm.Greens, edge_color='gray', alpha=0.6) plt.title('Closeness Centrality') plt.tight_layout() plt.show()2、最短路径分析
最短路径分析的重点是寻找图中两个节点之间的最短门路。这能够帮忙了解不同实体之间的连通性，以及连贯它们所需的最小关系数量。例如，假如你想找到节点“gene2”和“cancer”之间的最短门路:
source_node = 'gene2' target_node = 'cancer' # Find the shortest path shortest_path = nx.shortest_path(G, source=source_node, target=target_node) # Visualize the shortest path plt.figure(figsize=(10, 8)) path_edges = [(shortest_path[i], shortest_path[i + 1]) for i in range(len(shortest_path) — 1)] nx.draw(G, pos, with_labels=True, font_size=10, node_size=700, node_color='lightblue', edge_color='gray', alpha=0.6) nx.draw_networkx_edges(G, pos, edgelist=path_edges, edge_color='red', width=2) plt.title(f'Shortest Path from {source_node} to {target_node}') plt.show() print('Shortest Path:', shortest_path)源节点“gene2”和指标节点“cancer”之间的最短门路用红色突出显示，整个图的节点和边缘也被显示进去。这能够帮忙了解两个实体之间最间接的门路以及该门路上的关系。
图嵌入
图嵌入是间断向量空间中图中节点或边的数学示意。这些嵌入捕捉图的构造和关系信息，容许咱们执行各种剖析，例如节点相似性计算和在低维空间中的可视化。
咱们将应用node2vec算法，该算法通过在图上执行随机游走并优化以保留节点的部分邻域构造来学习嵌入。
from node2vec import Node2Vec # Generate node embeddings using node2vec node2vec = Node2Vec(G, dimensions=64, walk_length=30, num_walks=200, workers=4) # You can adjust these parameters model = node2vec.fit(window=10, min_count=1, batch_words=4) # Training the model # Visualize node embeddings using t-SNE from sklearn.manifold import TSNE import numpy as np # Get embeddings for all nodes embeddings = np.array([model.wv[node] for node in G.nodes()]) # Reduce dimensionality using t-SNE tsne = TSNE(n_components=2, perplexity=10, n_iter=400) embeddings_2d = tsne.fit_transform(embeddings) # Visualize embeddings in 2D space with node labels plt.figure(figsize=(12, 10)) plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c='blue', alpha=0.7) # Add node labels for i, node in enumerate(G.nodes()): plt.text(embeddings_2d[i, 0], embeddings_2d[i, 1], node, fontsize=8) plt.title('Node Embeddings Visualization') plt.show()node2vec算法用于学习KG中节点的64维嵌入。而后应用t-SNE将嵌入缩小到2维。并将后果以散点图形式进行可视化。不相连的子图是能够在矢量化空间中独自示意的
聚类
聚类是一种寻找具备类似特色的察看组的技术。因为是无监督算法，所以不用特地通知算法如何对这些察看进行分组，算法会依据数据自行判断一组中的观测值(或数据点)比另一组中的其余观测值更类似。
1、K-means
K-means应用迭代细化办法依据用户定义的聚类数量(由变量K示意)和数据集生成最终聚类。
咱们能够对嵌入空间进行K-means聚类。这样能够分明地理解算法是如何基于嵌入对节点进行聚类的:
# Perform K-Means clustering on node embeddings num_clusters = 3 # Adjust the number of clusters kmeans = KMeans(n_clusters=num_clusters, random_state=42) cluster_labels = kmeans.fit_predict(embeddings) # Visualize K-Means clustering in the embedding space with node labels plt.figure(figsize=(12, 10)) plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=cluster_labels, cmap=plt.cm.Set1, alpha=0.7) # Add node labels for i, node in enumerate(G.nodes()): plt.text(embeddings_2d[i, 0], embeddings_2d[i, 1], node, fontsize=8) plt.title('K-Means Clustering in Embedding Space with Node Labels') plt.colorbar(label=”Cluster Label”) plt.show()每种色彩代表一个不同的簇。当初咱们回到原始图，在原始空间中解释这些信息:
from sklearn.cluster import KMeans # Perform K-Means clustering on node embeddings num_clusters = 3 # Adjust the number of clusters kmeans = KMeans(n_clusters=num_clusters, random_state=42) cluster_labels = kmeans.fit_predict(embeddings) # Visualize clusters plt.figure(figsize=(12, 10)) nx.draw(G, pos, with_labels=True, font_size=10, node_size=700, node_color=cluster_labels, cmap=plt.cm.Set1, edge_color=’gray’, alpha=0.6) plt.title('Graph Clustering using K-Means') plt.show()2、DBSCAN
DBSCAN是基于密度的聚类算法，并且不须要预设数量的聚类。它还能够将异样值辨认为噪声。上面是如何应用DBSCAN算法进行图聚类的示例，重点是基于从node2vec算法取得的嵌入对节点进行聚类。
from sklearn.cluster import DBSCAN # Perform DBSCAN clustering on node embeddings dbscan = DBSCAN(eps=1.0, min_samples=2) # Adjust eps and min_samples cluster_labels = dbscan.fit_predict(embeddings) # Visualize clusters plt.figure(figsize=(12, 10)) nx.draw(G, pos, with_labels=True, font_size=10, node_size=700, node_color=cluster_labels, cmap=plt.cm.Set1, edge_color='gray', alpha=0.6) plt.title('Graph Clustering using DBSCAN') plt.show()下面的eps参数定义了两个样本之间的最大间隔，，min_samples参数确定了一个被认为是外围点的邻域内的最小样本数。能够看到DBSCAN将节点调配到簇，并辨认不属于任何簇的噪声点。
总结
剖析KGs能够为实体之间的简单关系和交互提供贵重的见解。通过联合数据预处理、剖析技术、嵌入和聚类分析，能够发现暗藏的模式，并更深刻地理解底层数据结构。
本文中的办法能够无效地可视化和摸索KGs，是常识图谱学习中的必要的入门常识。
https://avoid.overfit.cn/post/7ec9eb11e66c4b44bd2270b8ad66d80d
作者：Diego Lopez Yse