{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a344005c",
   "metadata": {},
   "source": [
    "# Subsumption Recapitulation: Do LLM Embeddings Encode the Ontology Hierarchy?\n",
    "\n",
    "LLM embeddings of ontology terms are built only from text (labels, and possibly\n",
    "definitions). Ontologies encode **subsumption**: *finger syndactyly* is a kind of\n",
    "*syndactyly*. This notebook asks how much of that hierarchy can be recovered from\n",
    "embedding space.\n",
    "\n",
    "We compare the four embedding models hosted by OLS with the ontology itself, written as\n",
    "`closure` vectors (each term is a multi-hot vector of its ancestors). All comparisons use\n",
    "the same `EmbeddingProviderInterface` code, so the only thing that changes between rows\n",
    "is the source of the vectors.\n",
    "\n",
    "**Design.** From the HPO phenotypic abnormality branch we sample:\n",
    "\n",
    "- **parent pairs**: a term and one of its direct `is_a` parents\n",
    "- **sibling pairs**: the same term and another child of that parent (not subsuming or\n",
    "  subsumed); these are the hard negatives, because they are as close in the graph as a\n",
    "  parent is\n",
    "- **random pairs**: two terms drawn at random from the branch\n",
    "\n",
    "We then ask two questions:\n",
    "\n",
    "1. Does embedding similarity track ontology similarity (closure Jaccard)?\n",
    "2. For each term, is its **parent** closer than a **sibling**? Cosine is symmetric, so\n",
    "   this is the most that a single similarity score can say about subsumption; it cannot\n",
    "   tell which term is the parent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d724890d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:44.382926Z",
     "iopub.status.busy": "2026-10-05T01:15:44.382650Z",
     "iopub.status.idle": "2026-10-05T01:15:46.220856Z",
     "shell.execute_reply": "2026-10-05T01:15:46.218588Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"eutils\")\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# reference palette (categorical slots in fixed order; sequential blue ramp)\n",
    "BLUE, ORANGE, AQUA = \"#2a78d6\", \"#eb6834\", \"#1baf7a\"\n",
    "INK, INK2, GRID, SURFACE = \"#0b0b0b\", \"#52514e\", \"#e4e3df\", \"#fcfcfb\"\n",
    "plt.rcParams.update({\n",
    "    \"figure.facecolor\": SURFACE, \"axes.facecolor\": SURFACE, \"savefig.facecolor\": SURFACE,\n",
    "    \"axes.edgecolor\": GRID, \"axes.labelcolor\": INK2, \"axes.titlecolor\": INK,\n",
    "    \"axes.grid\": True, \"grid.color\": GRID, \"grid.linewidth\": 0.6,\n",
    "    \"axes.spines.top\": False, \"axes.spines.right\": False,\n",
    "    \"xtick.color\": INK2, \"ytick.color\": INK2, \"text.color\": INK,\n",
    "    \"font.size\": 10, \"axes.titlesize\": 11, \"figure.dpi\": 110,\n",
    "})\n",
    "SEQUENTIAL = sns.blend_palette([\"#f0efec\", \"#86b6ef\", \"#2a78d6\", \"#0d366b\"], as_cmap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd9e9dd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:46.224575Z",
     "iopub.status.busy": "2026-10-05T01:15:46.223988Z",
     "iopub.status.idle": "2026-10-05T01:15:49.227578Z",
     "shell.execute_reply": "2026-10-05T01:15:49.226217Z"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "from oaklib import get_adapter\n",
    "from oaklib.datamodels.vocabulary import IS_A\n",
    "\n",
    "hp = get_adapter(\"sqlite:obo:hp\")\n",
    "ols = get_adapter(\"ols:hp\")\n",
    "MODELS = ols.embedding_models()\n",
    "PHENOTYPIC_ABNORMALITY = \"HP:0000118\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a098a5d",
   "metadata": {},
   "source": [
    "## Sampling term pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a5a7d83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:49.232138Z",
     "iopub.status.busy": "2026-10-05T01:15:49.231572Z",
     "iopub.status.idle": "2026-10-05T01:15:52.037199Z",
     "shell.execute_reply": "2026-10-05T01:15:52.035747Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19177 terms in branch; 150 (term, parent, sibling) triples; 729 distinct terms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "kind\n",
       "parent     150\n",
       "random     150\n",
       "sibling    150\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.seed(42)\n",
    "terms = sorted(t for t in hp.descendants(PHENOTYPIC_ABNORMALITY, predicates=[IS_A], reflexive=False) if t.startswith(\"HP:\"))\n",
    "\n",
    "ancestors = {}\n",
    "def anc(t):\n",
    "    if t not in ancestors:\n",
    "        ancestors[t] = set(hp.ancestors(t, predicates=[IS_A], reflexive=True)) - {\"owl:Thing\"}\n",
    "    return ancestors[t]\n",
    "\n",
    "parents, children = {}, defaultdict(list)\n",
    "for s, _, o in hp.relationships(predicates=[IS_A]):\n",
    "    if s.startswith(\"HP:\") and o.startswith(\"HP:\"):\n",
    "        parents.setdefault(s, []).append(o)\n",
    "        children[o].append(s)\n",
    "\n",
    "candidates = [t for t in terms if t in parents]\n",
    "random.shuffle(candidates)\n",
    "triples = []\n",
    "for c in candidates:\n",
    "    p = random.choice(parents[c])\n",
    "    if p in (PHENOTYPIC_ABNORMALITY, \"HP:0000001\"):\n",
    "        continue\n",
    "    siblings = [s for s in children[p] if s != c and s not in anc(c) and c not in anc(s)]\n",
    "    if not siblings:\n",
    "        continue\n",
    "    triples.append((c, p, random.choice(siblings)))\n",
    "    if len(triples) >= 150:\n",
    "        break\n",
    "random_pairs = [(random.choice(terms), random.choice(terms)) for _ in range(150)]\n",
    "\n",
    "pairs = pd.DataFrame(\n",
    "    [(c, p, \"parent\") for c, p, _ in triples]\n",
    "    + [(c, s, \"sibling\") for c, _, s in triples]\n",
    "    + [(a, b, \"random\") for a, b in random_pairs],\n",
    "    columns=[\"subject\", \"object\", \"kind\"],\n",
    ")\n",
    "all_terms = sorted(set(pairs.subject) | set(pairs.object))\n",
    "print(f\"{len(terms)} terms in branch; {len(triples)} (term, parent, sibling) triples; {len(all_terms)} distinct terms\")\n",
    "pairs.groupby(\"kind\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdbb4672",
   "metadata": {},
   "source": [
    "## Vectors for every model\n",
    "\n",
    "One matrix per model. OLS vectors are cached locally after the first run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e6366d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:52.039983Z",
     "iopub.status.busy": "2026-10-05T01:15:52.039731Z",
     "iopub.status.idle": "2026-10-05T01:15:53.215199Z",
     "shell.execute_reply": "2026-10-05T01:15:53.213775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>object</th>\n",
       "      <th>kind</th>\n",
       "      <th>closure_jaccard</th>\n",
       "      <th>closure</th>\n",
       "      <th>harrier-oss-v1-27b_pca512</th>\n",
       "      <th>llama-embed-nemotron-8b_pca512</th>\n",
       "      <th>text-embedding-3-large_pca512</th>\n",
       "      <th>text-embedding-3-small_pca512</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HP:0012712</td>\n",
       "      <td>HP:0000365</td>\n",
       "      <td>parent</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.925820</td>\n",
       "      <td>0.826311</td>\n",
       "      <td>0.739575</td>\n",
       "      <td>0.838928</td>\n",
       "      <td>0.819596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HP:0100623</td>\n",
       "      <td>HP:0000036</td>\n",
       "      <td>parent</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.948683</td>\n",
       "      <td>0.864599</td>\n",
       "      <td>0.876593</td>\n",
       "      <td>0.779589</td>\n",
       "      <td>0.792845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP:0002343</td>\n",
       "      <td>HP:0000238</td>\n",
       "      <td>parent</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.948683</td>\n",
       "      <td>0.831912</td>\n",
       "      <td>0.721223</td>\n",
       "      <td>0.837716</td>\n",
       "      <td>0.790062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HP:6000651</td>\n",
       "      <td>HP:0010660</td>\n",
       "      <td>parent</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.827170</td>\n",
       "      <td>0.930526</td>\n",
       "      <td>0.855038</td>\n",
       "      <td>0.900561</td>\n",
       "      <td>0.873528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HP:0033265</td>\n",
       "      <td>HP:0031265</td>\n",
       "      <td>parent</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.960769</td>\n",
       "      <td>0.717179</td>\n",
       "      <td>0.471363</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.522678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject      object    kind  closure_jaccard   closure  \\\n",
       "0  HP:0012712  HP:0000365  parent         0.857143  0.925820   \n",
       "1  HP:0100623  HP:0000036  parent         0.900000  0.948683   \n",
       "2  HP:0002343  HP:0000238  parent         0.900000  0.948683   \n",
       "3  HP:6000651  HP:0010660  parent         0.684211  0.827170   \n",
       "4  HP:0033265  HP:0031265  parent         0.923077  0.960769   \n",
       "\n",
       "   harrier-oss-v1-27b_pca512  llama-embed-nemotron-8b_pca512  \\\n",
       "0                   0.826311                        0.739575   \n",
       "1                   0.864599                        0.876593   \n",
       "2                   0.831912                        0.721223   \n",
       "3                   0.930526                        0.855038   \n",
       "4                   0.717179                        0.471363   \n",
       "\n",
       "   text-embedding-3-large_pca512  text-embedding-3-small_pca512  \n",
       "0                       0.838928                       0.819596  \n",
       "1                       0.779589                       0.792845  \n",
       "2                       0.837716                       0.790062  \n",
       "3                       0.900561                       0.873528  \n",
       "4                       0.636514                       0.522678  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pair_cosines(adapter, model, metric=\"cosine\"):\n",
    "    ids, m = adapter.entity_embeddings(all_terms, model=model)\n",
    "    ix = {c: i for i, c in enumerate(ids)}\n",
    "    if metric == \"cosine\":\n",
    "        m = m / np.linalg.norm(m, axis=1, keepdims=True)\n",
    "    out = []\n",
    "    for s, o in zip(pairs.subject, pairs.object):\n",
    "        if s not in ix or o not in ix:\n",
    "            out.append(np.nan)\n",
    "            continue\n",
    "        a, b = m[ix[s]], m[ix[o]]\n",
    "        if metric == \"cosine\":\n",
    "            out.append(float(a @ b))\n",
    "        else:\n",
    "            a, b = a > 0, b > 0\n",
    "            out.append((a & b).sum() / (a | b).sum())\n",
    "    return out\n",
    "\n",
    "pairs[\"closure_jaccard\"] = pair_cosines(hp, \"closure\", metric=\"jaccard\")\n",
    "pairs[\"closure\"] = pair_cosines(hp, \"closure\")\n",
    "for model in MODELS:\n",
    "    pairs[model] = pair_cosines(ols, model)\n",
    "SOURCES = [\"closure\"] + MODELS\n",
    "pairs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d988ae",
   "metadata": {},
   "source": [
    "## 1. Does embedding similarity track ontology similarity?\n",
    "\n",
    "Each point is a pair of terms. The x-axis is the Jaccard similarity of the two terms'\n",
    "ancestor sets (classic semantic similarity), and the y-axis is cosine similarity in each\n",
    "model's embedding space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9bb309c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:53.218042Z",
     "iopub.status.busy": "2026-10-05T01:15:53.217778Z",
     "iopub.status.idle": "2026-10-05T01:15:54.065974Z",
     "shell.execute_reply": "2026-10-05T01:15:54.063561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1760x462 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KIND_COLORS = {\"parent\": BLUE, \"sibling\": ORANGE, \"random\": AQUA}\n",
    "fig, axes = plt.subplots(1, len(MODELS), figsize=(16, 4.2), sharex=True)\n",
    "for ax, model in zip(axes, MODELS):\n",
    "    for kind, color in KIND_COLORS.items():\n",
    "        d = pairs[pairs.kind == kind]\n",
    "        ax.scatter(d.closure_jaccard, d[model], s=14, color=color, alpha=0.75,\n",
    "                   edgecolors=SURFACE, linewidths=0.5, label=kind)\n",
    "    rho = spearmanr(pairs.closure_jaccard, pairs[model], nan_policy=\"omit\").statistic\n",
    "    ax.set_title(f\"{model.replace('_pca512', '')}\\nSpearman ρ = {rho:.2f}\")\n",
    "    ax.set_xlabel(\"closure Jaccard (ontology)\")\n",
    "axes[0].set_ylabel(\"cosine similarity (embedding)\")\n",
    "axes[0].legend(title=\"pair\", frameon=False, loc=\"lower right\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bf2ffc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:54.071327Z",
     "iopub.status.busy": "2026-10-05T01:15:54.070964Z",
     "iopub.status.idle": "2026-10-05T01:15:54.100940Z",
     "shell.execute_reply": "2026-10-05T01:15:54.099543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spearman ρ, all pairs</th>\n",
       "      <th>Spearman ρ, parent + sibling only</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>closure</th>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>harrier-oss-v1-27b_pca512</th>\n",
       "      <td>0.775</td>\n",
       "      <td>0.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llama-embed-nemotron-8b_pca512</th>\n",
       "      <td>0.687</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-embedding-3-large_pca512</th>\n",
       "      <td>0.768</td>\n",
       "      <td>0.297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-embedding-3-small_pca512</th>\n",
       "      <td>0.739</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Spearman ρ, all pairs  \\\n",
       "closure                                         0.999   \n",
       "harrier-oss-v1-27b_pca512                       0.775   \n",
       "llama-embed-nemotron-8b_pca512                  0.687   \n",
       "text-embedding-3-large_pca512                   0.768   \n",
       "text-embedding-3-small_pca512                   0.739   \n",
       "\n",
       "                                Spearman ρ, parent + sibling only  \n",
       "closure                                                     0.999  \n",
       "harrier-oss-v1-27b_pca512                                   0.313  \n",
       "llama-embed-nemotron-8b_pca512                              0.108  \n",
       "text-embedding-3-large_pca512                               0.297  \n",
       "text-embedding-3-small_pca512                               0.230  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = pd.DataFrame({\n",
    "    \"Spearman ρ, all pairs\": [spearmanr(pairs.closure_jaccard, pairs[m], nan_policy=\"omit\").statistic for m in SOURCES],\n",
    "    \"Spearman ρ, parent + sibling only\": [\n",
    "        spearmanr(*pairs.loc[pairs.kind != \"random\", [\"closure_jaccard\", m]].T.values, nan_policy=\"omit\").statistic\n",
    "        for m in SOURCES\n",
    "    ],\n",
    "}, index=SOURCES)\n",
    "corr.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8127c180",
   "metadata": {},
   "source": [
    "All four LLM models correlate well with ontology similarity when random pairs are\n",
    "included (ρ ≈ 0.7 to 0.8), because unrelated terms are easy to separate. Among closely\n",
    "related terms (parents and siblings only) the correlation falls to ρ ≈ 0.1 to 0.3: in text\n",
    "space, the fine structure of the hierarchy is largely invisible. In the scatter plots,\n",
    "parent pairs (blue) and sibling pairs (orange) overlap almost completely on the y-axis,\n",
    "even though the ontology separates them on the x-axis. (The `closure` row is a sanity\n",
    "check: cosine and Jaccard over the same binary vectors are nearly monotonic.)\n",
    "\n",
    "## 2. Is a term's parent closer than its sibling?\n",
    "\n",
    "For each sampled term, we check whether cosine(term, parent) > cosine(term, sibling).\n",
    "Chance is 50%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cd76a5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:54.104121Z",
     "iopub.status.busy": "2026-10-05T01:15:54.103814Z",
     "iopub.status.idle": "2026-10-05T01:15:54.317287Z",
     "shell.execute_reply": "2026-10-05T01:15:54.315823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parent closer than sibling</th>\n",
       "      <th>95% CI low</th>\n",
       "      <th>95% CI high</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>closure</th>\n",
       "      <td>0.987</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>harrier-oss-v1-27b_pca512</th>\n",
       "      <td>0.620</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llama-embed-nemotron-8b_pca512</th>\n",
       "      <td>0.447</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-embedding-3-large_pca512</th>\n",
       "      <td>0.587</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-embedding-3-small_pca512</th>\n",
       "      <td>0.493</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                parent closer than sibling  95% CI low  \\\n",
       "source                                                                   \n",
       "closure                                              0.987       0.953   \n",
       "harrier-oss-v1-27b_pca512                            0.620       0.540   \n",
       "llama-embed-nemotron-8b_pca512                       0.447       0.369   \n",
       "text-embedding-3-large_pca512                        0.587       0.507   \n",
       "text-embedding-3-small_pca512                        0.493       0.414   \n",
       "\n",
       "                                95% CI high  \n",
       "source                                       \n",
       "closure                               0.996  \n",
       "harrier-oss-v1-27b_pca512             0.694  \n",
       "llama-embed-nemotron-8b_pca512        0.527  \n",
       "text-embedding-3-large_pca512         0.662  \n",
       "text-embedding-3-small_pca512         0.573  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 825x374 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par = pairs[pairs.kind == \"parent\"].reset_index(drop=True)\n",
    "sib = pairs[pairs.kind == \"sibling\"].reset_index(drop=True)\n",
    "\n",
    "def wilson(k, n, z=1.96):\n",
    "    # 95% Wilson score interval for a proportion\n",
    "    p = k / n\n",
    "    centre = (p + z**2 / (2 * n)) / (1 + z**2 / n)\n",
    "    half = z * np.sqrt(p * (1 - p) / n + z**2 / (4 * n**2)) / (1 + z**2 / n)\n",
    "    return centre - half, centre + half\n",
    "\n",
    "rows = []\n",
    "for m in SOURCES:\n",
    "    k, n = int((par[m] > sib[m]).sum()), len(par)\n",
    "    lo, hi = wilson(k, n)\n",
    "    rows.append({\"source\": m, \"parent closer than sibling\": k / n, \"95% CI low\": lo, \"95% CI high\": hi})\n",
    "wins = pd.DataFrame(rows).set_index(\"source\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.5, 3.4))\n",
    "names = [m.replace(\"_pca512\", \"\") for m in SOURCES]\n",
    "v = wins[\"parent closer than sibling\"].values\n",
    "err = [v - wins[\"95% CI low\"].values, wins[\"95% CI high\"].values - v]\n",
    "ax.set_axisbelow(True)\n",
    "ax.barh(names, v, color=BLUE, height=0.55, xerr=err, error_kw={\"ecolor\": INK2, \"elinewidth\": 1, \"capsize\": 3})\n",
    "ax.axvline(0.5, color=INK2, linewidth=1, linestyle=\"--\")\n",
    "ax.annotate(\"chance\", (0.5, 1.0), xycoords=(\"data\", \"axes fraction\"), xytext=(3, 2),\n",
    "            textcoords=\"offset points\", color=INK2, fontsize=9, va=\"bottom\")\n",
    "for y, (val, hi) in enumerate(zip(v, wins[\"95% CI high\"].values)):\n",
    "    ax.text(hi + 0.015, y, f\"{val:.0%}\", va=\"center\", color=INK, fontsize=9)\n",
    "ax.set_xlim(0, 1.1)\n",
    "ax.set_xlabel(\"fraction of terms whose parent is closer than a sibling (95% CI)\")\n",
    "ax.invert_yaxis()\n",
    "ax.grid(axis=\"y\", visible=False)\n",
    "fig.tight_layout()\n",
    "wins.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6612ee26",
   "metadata": {},
   "source": [
    "## Where the models get it wrong\n",
    "\n",
    "The cases where a model places a sibling closer than the parent show the kinds of\n",
    "structure that text similarity misses: siblings that share most of their wording, and\n",
    "parents with generic labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9ce43c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T01:15:54.320175Z",
     "iopub.status.busy": "2026-10-05T01:15:54.319859Z",
     "iopub.status.idle": "2026-10-05T01:15:54.350401Z",
     "shell.execute_reply": "2026-10-05T01:15:54.348970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>parent</th>\n",
       "      <th>sibling</th>\n",
       "      <th>cos(term, parent)</th>\n",
       "      <th>cos(term, sibling)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Podocyte myelin figures</td>\n",
       "      <td>Abnormal glomerular visceral epithelial cell m...</td>\n",
       "      <td>Podocyte foot process effacement</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jumping</td>\n",
       "      <td>Stereotypic whole-body movements</td>\n",
       "      <td>Stereotypical body rocking</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Excessive purine production</td>\n",
       "      <td>Abnormal homeostasis</td>\n",
       "      <td>Abnormal glucose homeostasis</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Upper eyelid erythema</td>\n",
       "      <td>Eyelid erythema</td>\n",
       "      <td>Lower eyelid erythema</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Increased circulating interleukin 8 concentration</td>\n",
       "      <td>Abnormal circulating interleukin concentration</td>\n",
       "      <td>Increased circulating interleukin 13 concentra...</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Decreased circulating alkaline phosphatase act...</td>\n",
       "      <td>Abnormality of alkaline phosphatase level</td>\n",
       "      <td>Elevated circulating alkaline phosphatase conc...</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Best corrected visual acuity 0.9 LogMAR</td>\n",
       "      <td>Abnormal best corrected visual acuity test</td>\n",
       "      <td>Best corrected visual acuity 3.0 LogMAR</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Intrathoracic hemangioma</td>\n",
       "      <td>Hemangioma</td>\n",
       "      <td>Visceral hemangioma</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Philtrum with midline raphe</td>\n",
       "      <td>Abnormality of the philtrum</td>\n",
       "      <td>Short philtrum</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Triceps areflexia</td>\n",
       "      <td>Areflexia of upper limbs</td>\n",
       "      <td>Biceps areflexia</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term  \\\n",
       "4                             Podocyte myelin figures   \n",
       "6                                             Jumping   \n",
       "8                         Excessive purine production   \n",
       "10                              Upper eyelid erythema   \n",
       "14  Increased circulating interleukin 8 concentration   \n",
       "17  Decreased circulating alkaline phosphatase act...   \n",
       "18            Best corrected visual acuity 0.9 LogMAR   \n",
       "20                           Intrathoracic hemangioma   \n",
       "23                        Philtrum with midline raphe   \n",
       "24                                  Triceps areflexia   \n",
       "\n",
       "                                               parent  \\\n",
       "4   Abnormal glomerular visceral epithelial cell m...   \n",
       "6                    Stereotypic whole-body movements   \n",
       "8                                Abnormal homeostasis   \n",
       "10                                    Eyelid erythema   \n",
       "14     Abnormal circulating interleukin concentration   \n",
       "17          Abnormality of alkaline phosphatase level   \n",
       "18         Abnormal best corrected visual acuity test   \n",
       "20                                         Hemangioma   \n",
       "23                        Abnormality of the philtrum   \n",
       "24                           Areflexia of upper limbs   \n",
       "\n",
       "                                              sibling  cos(term, parent)  \\\n",
       "4                    Podocyte foot process effacement              0.637   \n",
       "6                          Stereotypical body rocking              0.455   \n",
       "8                        Abnormal glucose homeostasis              0.421   \n",
       "10                              Lower eyelid erythema              0.916   \n",
       "14  Increased circulating interleukin 13 concentra...              0.713   \n",
       "17  Elevated circulating alkaline phosphatase conc...              0.862   \n",
       "18            Best corrected visual acuity 3.0 LogMAR              0.753   \n",
       "20                                Visceral hemangioma              0.753   \n",
       "23                                     Short philtrum              0.616   \n",
       "24                                   Biceps areflexia              0.803   \n",
       "\n",
       "    cos(term, sibling)  \n",
       "4                0.687  \n",
       "6                0.490  \n",
       "8                0.443  \n",
       "10               0.933  \n",
       "14               0.725  \n",
       "17               0.866  \n",
       "18               0.932  \n",
       "20               0.797  \n",
       "23               0.725  \n",
       "24               0.908  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = \"text-embedding-3-large_pca512\"\n",
    "miss = par[par[model] <= sib[model]].copy()\n",
    "miss[\"sibling\"] = sib.loc[miss.index, \"object\"]\n",
    "miss[\"cos(term, parent)\"] = miss[model]\n",
    "miss[\"cos(term, sibling)\"] = sib.loc[miss.index, model]\n",
    "lbl = dict(hp.labels(set(miss.subject) | set(miss.object) | set(miss.sibling)))\n",
    "miss = miss.assign(term=miss.subject.map(lbl), parent=miss.object.map(lbl), sibling=miss.sibling.map(lbl))\n",
    "miss[[\"term\", \"parent\", \"sibling\", \"cos(term, parent)\", \"cos(term, sibling)\"]].head(10).round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0fb1ab",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- **Coarse structure: yes.** All four OLS-hosted LLM embedding models separate related\n",
    "  from unrelated HPO terms well.\n",
    "- **Fine structure: barely.** Given a term, its direct parent is not reliably closer than a\n",
    "  sibling. The best models (harrier, text-embedding-3-large) are only modestly above\n",
    "  chance, and nemotron and text-embedding-3-small are at chance. The ontology closure gets\n",
    "  this right 99% of the time (the misses involve terms with several parents).\n",
    "- **Direction: no.** Cosine similarity is symmetric, so embeddings alone cannot say\n",
    "  which of two terms subsumes the other. That needs the ontology, or an asymmetric scoring\n",
    "  method such as a trained entailment classifier.\n",
    "\n",
    "In practice, LLM embeddings are useful for finding the right neighbourhood (search,\n",
    "grounding, candidate mappings), but they should not stand in for the hierarchy when exact\n",
    "subsumption matters.\n",
    "\n",
    "**Caveats.** 150 triples per condition gives 95% intervals of about ±8 percentage points,\n",
    "so differences between the LLM models are indicative only. OLS embeds a fixed text per\n",
    "class (see [#918](https://github.com/INCATools/ontology-access-kit/issues/918)), and\n",
    "embedding labels plus definitions may behave differently; the `llm:` adapter can test that.\n",
    "Increase the sample size in the sampling cell to tighten the estimates. Vectors are cached,\n",
    "so only new terms are fetched from OLS."
   ]
  }
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