{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-integration-of-semantic-and-structural","title":"The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing","arxiv_id":"2404.08313","date":"2024-04-12","proceeding":null,"authors":["Muzhi Li","Minda Hu","Irwin King","Ho-fung Leung"],"abstract":"The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the \\textit{\\textbf{structural knowledge}} in the local neighborhood of entities, disregarding \\textit{\\textbf{semantic knowledge}} in the textual representations of entities, relations, and types that are also crucial for type inference. Additionally, we observe that the interaction between semantic and structural knowledge can be utilized to address the false-negative problem. In this paper, we propose a novel \\textbf{\\underline{S}}emantic and \\textbf{\\underline{S}}tructure-aware KG \\textbf{\\underline{E}}ntity \\textbf{\\underline{T}}yping~{(SSET)} framework, which is composed of three modules. First, the \\textit{Semantic Knowledge Encoding} module encodes factual knowledge in the KG with a Masked Entity Typing task. Then, the \\textit{Structural Knowledge Aggregation} module aggregates knowledge from the multi-hop neighborhood of entities to infer missing types. Finally, the \\textit{Unsupervised Type Re-ranking} module utilizes the inference results from the two models above to generate type predictions that are robust to false-negative samples. Extensive experiments show that SSET significantly outperforms existing state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2404.08313v1","url_pdf":"https://arxiv.org/pdf/2404.08313v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-integration-of-semantic-and-structural","repo_url":"https://github.com/raynorlee/kg-entitytyping","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"entity-typing","task_name":"Entity Typing"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"re-ranking","task_name":"Re-Ranking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2404.08313","atlas_url":"https://app.syntology.ai/?focus=2404.08313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.08313"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/RaynorLEE/KG-EntityTyping","reach":null}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"e0b311ed3e69d9fd","entry":"CSRA","repo":"RaynorLEE/KG-EntityTyping","repo_kind":"official","path":"SEM.py","file_url":"https://github.com/RaynorLEE/KG-EntityTyping/blob/HEAD/SEM.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e0b311ed3e69d9fd"}},{"code_sha256_prefix":"505b64b8e1ea2975","entry":"MHA","repo":"RaynorLEE/KG-EntityTyping","repo_kind":"official","path":"SEM.py","file_url":"https://github.com/RaynorLEE/KG-EntityTyping/blob/HEAD/SEM.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"505b64b8e1ea2975"}},{"code_sha256_prefix":"2e1c92d4d12b58cd","entry":"SEM","repo":"RaynorLEE/KG-EntityTyping","repo_kind":"official","path":"SEM.py","file_url":"https://github.com/RaynorLEE/KG-EntityTyping/blob/HEAD/SEM.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2e1c92d4d12b58cd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}