{
  "annotation_id": "5884a055-7fdc-4501-849b-878737b64335",
  "date_created": "2026-02-17T05:53:23.418000Z",
  "date_modified": "2026-02-17T05:53:23.418000Z",
  "file_hash": "b7f30f5ad386d8461daa20ddf5c2a1474ce95b71502f28d807573deeafb616b7",
  "private": false,
  "record": {
    "abstract": "Domain-specific knowledge graphs (DKGs) often lack coverage compared to general knowledge graphs (GKGs). To address this, we introduce Domain-specific Knowledge Graph Fusion (DKGF), a novel task that enriches DKGs by integrating relevant facts from GKGs. DKGF faces two key challenges: high ambiguity in domain relevance and misalignment in knowledge granularity across graphs. We propose ExeFuse, a simple yet effective Fact-as-Program paradigm. It treats each GKG fact as a latent semantic program, maps abstract relations to granularity-aware operators, and verifies domain relevance via program executability on the target DKG. This unified probabilistic framework jointly resolves relevance and granularity issues. We construct two benchmarks, DKGF(W-I) and DKGF(Y-I), with 21 evaluation configurations. Extensive experiments validate the task\u0027s importance and our model\u0027s effectiveness, providing the first standardized testbed for DKGF.",
    "arxiv_id": "2601.10485",
    "authors": [
      "Runhao Zhao",
      "Weixin Zeng",
      "Wentao Zhang",
      "Chong Chen",
      "Zhengpin Li",
      "Xiang Zhao",
      "Lei Chen"
    ],
    "categories": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge",
    "url": "https://arxiv.org/abs/2601.10485",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "399103ea-98e9-4603-a774-78e026fec1ab",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}