diff --git a/Phase 2/task_11.ipynb b/Phase 2/task_11.ipynb
index f00272a..42bd050 100644
--- a/Phase 2/task_11.ipynb	
+++ b/Phase 2/task_11.ipynb	
@@ -39,53 +39,51 @@
     "    def create_similarity_graph(\n",
     "        self, n, feature_model, semantic_data=None, dim_reduction_method=None\n",
     "    ):\n",
+    "        import glob\n",
+    "\n",
     "        if semantic_data is None:\n",
-    "            # Similarity graph from feature models\n",
-    "            image_sim_matrix = find_image_image_similarity(fd_collection, feature_model)\n",
+    "            file_prefix = f\"image_sim-{feature_model}-*.json\"\n",
+    "            if len(glob.glob(file_prefix)) > 0:\n",
+    "                matrix_data = json.load(open(glob.glob(file_prefix)[0]))\n",
+    "                image_sim_matrix = matrix_data[\"sim-matrix\"]\n",
+    "                if self.verbose:\n",
+    "                    print(\"Using image-image similarity matrix from semantic data\")\n",
+    "            else:\n",
+    "                # Similarity graph from feature models\n",
+    "                image_sim_matrix = find_image_image_similarity(\n",
+    "                    fd_collection, feature_model\n",
+    "                )\n",
     "            if self.verbose:\n",
     "                print(\"Image-image similarity matrix constructed from\", feature_model)\n",
     "        else:\n",
     "            # Similarity graph from image-semantic latent space\n",
-    "            # LS3, LS4\n",
-    "            if \"sim-matrix\" in semantic_data:\n",
-    "                # for now, don't work with LS3\n",
-    "                # TODO: do similar to task 7 and 10\n",
-    "                image_sim_matrix = np.array(semantic_data[\"sim-matrix\"])\n",
-    "                if image_sim_matrix.shape[0] != NUM_IMAGES:\n",
-    "                    raise TypeError(\n",
-    "                        \"Functionality to construct similarity graph from LS3 not yet done\"\n",
-    "                    )\n",
-    "                if self.verbose:\n",
-    "                    print(\"Using image-image similarity matrix from semantic data\")\n",
-    "            # LS1, LS2\n",
-    "            else:\n",
-    "                image_semantic = semantic_data[\"image-semantic\"]\n",
-    "                # SVD, CP\n",
-    "                if \"semantics-core\" in semantic_data:\n",
-    "                    semantics_core = np.array(semantic_data[\"semantics-core\"])\n",
-    "                    if len(semantics_core.shape) == 1:\n",
-    "                        semantics_core = np.diag(semantics_core)\n",
-    "                    image_semantic = np.matmul(image_semantic, semantics_core)\n",
+    "            image_semantic = semantic_data[\"image-semantic\"]\n",
+    "            # SVD, CP\n",
+    "            if \"semantics-core\" in semantic_data:\n",
+    "                semantics_core = np.array(semantic_data[\"semantics-core\"])\n",
+    "                if len(semantics_core.shape) == 1:\n",
+    "                    semantics_core = np.diag(semantics_core)\n",
+    "                image_semantic = np.matmul(image_semantic, semantics_core)\n",
     "\n",
-    "                image_sim_matrix = np.zeros((NUM_IMAGES, NUM_IMAGES))\n",
-    "                # Calculate half and fill the other\n",
-    "                for i in range(NUM_IMAGES):\n",
-    "                    for j in range(i + 1, NUM_IMAGES):\n",
-    "                        # Note: lower the value, lower the distance => higher the similarity\n",
-    "                        distance_measure = (\n",
-    "                            kl_divergence_measure\n",
-    "                            if dim_reduction_method == \"lda\"\n",
-    "                            else euclidean_distance_measure\n",
-    "                        )\n",
-    "                        image_sim_matrix[j][i] = distance_measure(\n",
-    "                            np.array(image_semantic[i]),\n",
-    "                            np.array(image_semantic[j]),\n",
-    "                        )\n",
-    "                        image_sim_matrix[i][j] = image_sim_matrix[j][i]\n",
-    "                if self.verbose:\n",
-    "                    print(\n",
-    "                        \"Image-image similarity matrix constructed from given image-semantic\"\n",
+    "            image_sim_matrix = np.zeros((NUM_IMAGES, NUM_IMAGES))\n",
+    "            # Calculate half and fill the other\n",
+    "            for i in range(NUM_IMAGES):\n",
+    "                for j in range(i + 1, NUM_IMAGES):\n",
+    "                    # Note: lower the value, lower the distance => higher the similarity\n",
+    "                    distance_measure = (\n",
+    "                        kl_divergence_measure\n",
+    "                        if dim_reduction_method == \"lda\"\n",
+    "                        else euclidean_distance_measure\n",
     "                    )\n",
+    "                    image_sim_matrix[j][i] = distance_measure(\n",
+    "                        np.array(image_semantic[i]),\n",
+    "                        np.array(image_semantic[j]),\n",
+    "                    )\n",
+    "                    image_sim_matrix[i][j] = image_sim_matrix[j][i]\n",
+    "            if self.verbose:\n",
+    "                print(\n",
+    "                    \"Image-image similarity matrix constructed from given image-semantic\"\n",
+    "                )\n",
     "\n",
     "        # Create an unweighted directed similarity graph, with no self-loops\n",
     "        self.similarity_graph = []\n",
@@ -103,8 +101,6 @@
     "    def personalized_pagerank(\n",
     "        self, label, m, damping_factor=0.85, max_iter=1000, tol=1e-6\n",
     "    ):\n",
-    "        import time\n",
-    "\n",
     "        if self.similarity_graph is None:\n",
     "            raise ValueError(\n",
     "                \"Similarity graph not created. Call create_similarity_graph() first.\"\n",