VEM2L: an easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion
The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In this paper, we propose VEM 2 L, a joint learning framework that incorporates struc...
Saved in:
| Published in | Data mining and knowledge discovery Vol. 38; no. 2; pp. 343 - 371 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In this paper, we propose VEM
2
L, a joint learning framework that incorporates structure and relevant text information to supplement insufficient features for sparse KGs. We begin by training two pre-existing KGC models: one based on structure and the other based on text. Our ultimate goal is to fuse knowledge acquired by these models. To achieve this, we divide knowledge within the models into two non-overlapping parts:
expressive power
and
generalization ability
. We then propose two different joint learning methods that co-distill these two kinds of knowledge respectively. For expressive power, we allow each model to learn from and exchange knowledge mutually on training examples. For the generalization ability, we propose a novel co-distillation strategy using the Variational EM algorithm on unobserved queries. Our proposed joint learning framework is supported by both detailed theoretical evidence and qualitative experiments, demonstrating its effectiveness. |
|---|---|
| AbstractList | The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In this paper, we propose VEM
2
L, a joint learning framework that incorporates structure and relevant text information to supplement insufficient features for sparse KGs. We begin by training two pre-existing KGC models: one based on structure and the other based on text. Our ultimate goal is to fuse knowledge acquired by these models. To achieve this, we divide knowledge within the models into two non-overlapping parts:
expressive power
and
generalization ability
. We then propose two different joint learning methods that co-distill these two kinds of knowledge respectively. For expressive power, we allow each model to learn from and exchange knowledge mutually on training examples. For the generalization ability, we propose a novel co-distillation strategy using the Variational EM algorithm on unobserved queries. Our proposed joint learning framework is supported by both detailed theoretical evidence and qualitative experiments, demonstrating its effectiveness. The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In this paper, we propose VEM2L, a joint learning framework that incorporates structure and relevant text information to supplement insufficient features for sparse KGs. We begin by training two pre-existing KGC models: one based on structure and the other based on text. Our ultimate goal is to fuse knowledge acquired by these models. To achieve this, we divide knowledge within the models into two non-overlapping parts: expressive power and generalization ability. We then propose two different joint learning methods that co-distill these two kinds of knowledge respectively. For expressive power, we allow each model to learn from and exchange knowledge mutually on training examples. For the generalization ability, we propose a novel co-distillation strategy using the Variational EM algorithm on unobserved queries. Our proposed joint learning framework is supported by both detailed theoretical evidence and qualitative experiments, demonstrating its effectiveness. |
| Author | Zheng, Zihao Liu, Ming He, Tao Qu, Meng Qin, Bing Cao, Yixin |
| Author_xml | – sequence: 1 givenname: Tao orcidid: 0000-0002-6052-4573 surname: He fullname: He, Tao organization: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology – sequence: 2 givenname: Ming surname: Liu fullname: Liu, Ming email: mliu@ir.hit.edu.cn organization: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Peng Cheng Laboratory – sequence: 3 givenname: Yixin surname: Cao fullname: Cao, Yixin organization: SMU School of Computing and Information Systems 1, Singapore Management University – sequence: 4 givenname: Meng surname: Qu fullname: Qu, Meng organization: Mila - Quebec AI Institute – sequence: 5 givenname: Zihao surname: Zheng fullname: Zheng, Zihao organization: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology – sequence: 6 givenname: Bing surname: Qin fullname: Qin, Bing organization: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Peng Cheng Laboratory |
| BookMark | eNpNkE1LAzEQhoMo2Fb_gKeA5-gk6e6m3qTUD6h4UfG2JNtJ7VeyJllr_73RCsocZnh5mZn36ZND5x0ScsbhggNUl5FDyRUDIRlkgbPdAenxopKsKsrXwzxLNWSF4nBM-jEuAaAQEnokvkwexPSKakdRxx01XaJoLTZp8YHUBr3BrQ8ran2gtosLN6cJP1P2z2hMoWtSF5CunN-ucTZH6h2NrQ7xvzYPun2jjd-0a0wL707IkdXriKe_fUCebyZP4zs2fby9H19PWZs_T6wRCrQqjDRaNKrgZljYkamEMqUdKQMzVFU1BK5kWebIRmkhtbBDLgyXmGtAzvd72-DfO4ypXvouuHyyFiOhKskFF9kl967YhhwPw5-LQ_1Nt97TrTPd-oduvZNfYhJwJg |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| DBID | 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M2O MBDVC P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U |
| DOI | 10.1007/s10618-023-01001-y |
| DatabaseName | ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Research Library ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Korea Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database ProQuest Research Library Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central Basic |
| DatabaseTitle | ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Research Library Prep Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics Computer Science |
| EISSN | 1573-756X |
| EndPage | 371 |
| ExternalDocumentID | 10_1007_s10618_023_01001_y |
| GrantInformation_xml | – fundername: National Key Research and Development Project of China grantid: No.2022YFF0903301 – fundername: Shenzhen Foundational Research Funding grantid: JCYJ20200109113441941 – fundername: National Science Foundation of China grantid: U22B2059, 61976073, 62276083 – fundername: Major Key Project of PCL grantid: PCL2021A06 |
| GroupedDBID | -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 203 29F 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5VS 67Z 6NX 78A 7WY 8AO 8FE 8FG 8FL 8G5 8TC 8UJ 95- 95. 95~ 96X AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EDO EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X J-C J0Z J9A JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV LAK LLZTM M0C M0N M2O M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9- O93 O9J OAM OVD P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOS R89 R9I RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S27 S3B SAP SCO SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7W Z7X Z7Y Z7Z Z81 Z83 Z88 ZMTXR 7SC 7XB 8AL 8FD 8FK AAPKM ABBRH ABDBE ABFSG ABRTQ ACSTC ADKFA AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA JQ2 L.- L7M L~C L~D MBDVC PHGZM PHGZT PKEHL PQEST PQGLB PQUKI Q9U |
| ID | FETCH-LOGICAL-p157t-c280a85b3ba2c851b45f9b728b6f98b0de8774018366001b8a23a2f412b13e3e3 |
| IEDL.DBID | U2A |
| ISSN | 1384-5810 |
| IngestDate | Sat Aug 16 17:42:39 EDT 2025 Fri Feb 21 02:42:14 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Knowledge fusion Knowledge graph reasoning Variational inference Graph neural network |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-p157t-c280a85b3ba2c851b45f9b728b6f98b0de8774018366001b8a23a2f412b13e3e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-6052-4573 |
| PQID | 2928731212 |
| PQPubID | 43030 |
| PageCount | 29 |
| ParticipantIDs | proquest_journals_2928731212 springer_journals_10_1007_s10618_023_01001_y |
| PublicationCentury | 2000 |
| PublicationDate | 20240300 20240301 |
| PublicationDateYYYYMMDD | 2024-03-01 |
| PublicationDate_xml | – month: 3 year: 2024 text: 20240300 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Data mining and knowledge discovery |
| PublicationTitleAbbrev | Data Min Knowl Disc |
| PublicationYear | 2024 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | ZhuZZhangZXhonneuxL-PTangJNeural bellman-ford networks: a general graph neural network framework for link predictionAdv Neural Inf Process Syst2021342947629490 Yang B, Yih SW-T, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the international conference on learning representations (ICLR) 2015 Lv X, Lin Y, Cao Y, Hou L, Li J, Liu Z, Li P, Zhou J (2022) Do pre-trained models benefit knowledge graph completion? A reliable evaluation and a reasonable approach. In: Findings of the association for computational linguistics: ACL 2022, pp 3570–3581 Kenton JDM-WC, Toutanova LK (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp 4171–4186 Qu M, Bengio Y, Tang J (2019) Gmnn: graph markov neural networks. In: International conference on machine learning, PMLR, pp 5241–5250 Zhu Y, Zhang W, Chen M, Chen H, Cheng X, Zhang W, Chen H (2022) Dualde: dually distilling knowledge graph embedding for faster and cheaper reasoning. In: Proceedings of the 15th ACM international conference on web search and data mining, pp 1516–1524 Shang C, Tang Y, Huang J, Bi J, He X, Zhou B (2019) End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 3060–3067 Nickel M, Tresp V, Kriegel H-P (2011) A three-way model for collective learning on multi-relational data. In: ICML Zhang Y, Yao Q (2022) Knowledge graph reasoning with relational digraph. In: Proceedings of the ACM web conference 2022, pp 912–924 Fu C, Chen T, Qu M, Jin W, Ren X (2019) Collaborative policy learning for open knowledge graph reasoning. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 2672–2681 Xiao C, He X, Cao Y (2023) Knowledge graph embedding by normalizing flows. In: AAAI conference on artificial intelligence Balazevic I, Allen C, Hospedales T (2019) Tucker: tensor factorization for knowledge graph completion. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference, Springer, pp 593–607 Lv X, Han X, Hou L, Li J, Liu Z, Zhang W, Zhang Y, Kong H, Wu S (2020) Dynamic anticipation and completion for multi-hop reasoning over sparse knowledge graph. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 5694–5703 Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 Liu Y, Sun Z, Li G, Hu W (2022) I know what you do not know: knowledge graph embedding via co-distillation learning. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 1329–1338 Wang K, Liu Y, Ma Q, Sheng QZ (2021) Mulde: multi-teacher knowledge distillation for low-dimensional knowledge graph embeddings. In: Proceedings of the web conference 2021, pp 1716–1726 Malaviya C, Bhagavatula C, Bosselut A, Choi Y (2020) Commonsense knowledge base completion with structural and semantic context. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 2925–2933 Xu J, Qiu X, Chen K, Huang X (2017) Knowledge graph representation with jointly structural and textual encoding. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 1318–1324 BesagJStatistical analysis of non-lattice dataJ R Stat Soc Ser D (The Statistician)1975243179195 Zhang D, Yuan Z, Liu H, Xiong H et al (2022) Learning to walk with dual agents for knowledge graph reasoning. In: Proceedings of the AAAI Conference on artificial intelligence, vol 36, pp 5932–5941 Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations NealRMHintonGEA view of the EM algorithm that justifies incremental, sparse, and other variantsLearning in graphical models1998ChamSpringer35536810.1007/978-94-011-5014-9_12 Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, vol 26 Chen W, Xiong W, Yan X, Wang WY (2018) Variational knowledge graph reasoning. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1. Long Papers, pp 1823–1832 Hinton G, Vinyals O, Dean J et al (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 Vashishth S, Sanyal S, Nitin V, Talukdar P (2019) Composition-based multi-relational graph convolutional networks. In: International conference on learning representations Sun Z, Deng Z-H, Nie J-Y, Tang J (2018) Rotate: knowledge graph embedding by relational rotation in complex space. In: International conference on learning representations Markowitz E, Balasubramanian K, Mirtaheri M, Annavaram M, Galstyan A, Ver Steeg G (2022) Statik: structure and text for inductive knowledge graph completion. In: Findings of the association for computational linguistics: NAACL 2022, pp 604–615 Sun Z, Vashishth S, Sanyal S, Talukdar P, Yang Y (2020) A re-evaluation of knowledge graph completion methods. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 5516–5522 BishopCMPattern recognition and machine learning2006ChamSpringer Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. In: 32nd AAAI conference on artificial intelligence Wang H, Zhang F, Xie X, Guo M (2018) Dkn: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference, pp 1835–1844 Nathani D, Chauhan J, Sharma C, Kaul M (2019) Learning attention-based embeddings for relation prediction in knowledge graphs. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 4710–4723 Huang X, Zhang J, Li D, Li P (2019) Knowledge graph embedding based question answering. In: Proceedings of the 12th ACM international conference on web search and data mining, pp 105–113 Toutanova K, Chen D, Pantel P, Poon H, Choudhury P, Gamon M (2015) Representing text for joint embedding of text and knowledge bases. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1499–1509 Xie R, Liu Z, Jia J, Luan H, Sun M (2016) Representation learning of knowledge graphs with entity descriptions. In: Proceedings of the AAAI conference on artificial intelligence, vol 30 Lin XV, Socher R, Xiong C (2018) Multi-hop knowledge graph reasoning with reward shaping. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3243–3253 Wang B, Shen T, Long G, Zhou T, Wang Y, Chang Y (2021) Structure-augmented text representation learning for efficient knowledge graph completion. In: Proceedings of the web conference 2021, pp 1737–1748 Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4320–4328 Pavlović A, Sallinger E (2022) Expressive: a spatio-functional embedding for knowledge graph completion. In: The 11th international conference on learning representations Chen W, Cao Y, Feng F, He X, Zhang Y (2022) Explainable sparse knowledge graph completion via high-order graph reasoning network. arXiv preprint arXiv:2207.07503 Das R, Dhuliawala S, Zaheer M, Vilnis L, Durugkar I, Krishnamurthy A, Smola A, McCallum A (2018) Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning. In: International conference on learning representations Li R, Cao Y, Zhu Q, Bi G, Fang F, Liu Y, Li Q (2022) How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 5781–5791 Xiong C, Power R, Callan J (2017) Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th international conference on world wide web, pp 1271–1279 Vashishth S, Sanyal S, Nitin V, Agrawal N, Talukdar P (2020) Interacte: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 3009–3016 Wang L, Zhao W, Wei Z, Liu J (2022) Simkgc: simple contrastive knowledge graph completion with pre-trained language models. arXiv preprint arXiv:2203.02167 QiuJChaiYTianZDuXGuizaniMAutomatic concept extraction based on semantic graphs from big data in smart cityIEEE Trans Comput Soc Syst20197122523310.1109/TCSS.2019.2946181 Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, PMLR, pp 2071–2080 Yao L, Mao C, Luo Y (2019) Kg-bert: bert for knowledge graph completion. arXiv preprint arXiv:1909.03193 OhBSeoSHwangJLeeDLeeK-HOpen-world knowledge graph completion for unseen entities and relations via attentive feature aggregationInf Sci202258646848410.1016/j.ins.2021.11.085 ZhouLLiJGuZQiuJGuptaBBTianZPanner: pos-aware nested named entity recognition through heterogeneous graph neural networkIEEE Trans Comput Soc Syst2022451910.1109/TCSS.2022.3159366 RossiABarbosaDFirmaniDMatinataAMerialdoPKnowledge graph embedding for link prediction: A comparative analysisACM Trans Knowl Discov Data (TKDD)202115214910.11 |
| References_xml | – reference: Zhang Y, Yao Q (2022) Knowledge graph reasoning with relational digraph. In: Proceedings of the ACM web conference 2022, pp 912–924 – reference: RossiABarbosaDFirmaniDMatinataAMerialdoPKnowledge graph embedding for link prediction: A comparative analysisACM Trans Knowl Discov Data (TKDD)202115214910.1145/3424672 – reference: Wang H, Zhang F, Xie X, Guo M (2018) Dkn: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference, pp 1835–1844 – reference: Fu C, Chen T, Qu M, Jin W, Ren X (2019) Collaborative policy learning for open knowledge graph reasoning. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 2672–2681 – reference: Xiong C, Power R, Callan J (2017) Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th international conference on world wide web, pp 1271–1279 – reference: Toutanova K, Chen D, Pantel P, Poon H, Choudhury P, Gamon M (2015) Representing text for joint embedding of text and knowledge bases. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1499–1509 – reference: Vashishth S, Sanyal S, Nitin V, Agrawal N, Talukdar P (2020) Interacte: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 3009–3016 – reference: QiuJChaiYTianZDuXGuizaniMAutomatic concept extraction based on semantic graphs from big data in smart cityIEEE Trans Comput Soc Syst20197122523310.1109/TCSS.2019.2946181 – reference: Shang C, Tang Y, Huang J, Bi J, He X, Zhou B (2019) End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 3060–3067 – reference: Zhu Y, Zhang W, Chen M, Chen H, Cheng X, Zhang W, Chen H (2022) Dualde: dually distilling knowledge graph embedding for faster and cheaper reasoning. In: Proceedings of the 15th ACM international conference on web search and data mining, pp 1516–1524 – reference: Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, PMLR, pp 2071–2080 – reference: BesagJStatistical analysis of non-lattice dataJ R Stat Soc Ser D (The Statistician)1975243179195 – reference: Malaviya C, Bhagavatula C, Bosselut A, Choi Y (2020) Commonsense knowledge base completion with structural and semantic context. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 2925–2933 – reference: Sun Z, Deng Z-H, Nie J-Y, Tang J (2018) Rotate: knowledge graph embedding by relational rotation in complex space. In: International conference on learning representations – reference: NealRMHintonGEA view of the EM algorithm that justifies incremental, sparse, and other variantsLearning in graphical models1998ChamSpringer35536810.1007/978-94-011-5014-9_12 – reference: Sun Z, Vashishth S, Sanyal S, Talukdar P, Yang Y (2020) A re-evaluation of knowledge graph completion methods. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 5516–5522 – reference: Xu J, Qiu X, Chen K, Huang X (2017) Knowledge graph representation with jointly structural and textual encoding. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 1318–1324 – reference: Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, vol 26 – reference: Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference, Springer, pp 593–607 – reference: Qu M, Bengio Y, Tang J (2019) Gmnn: graph markov neural networks. In: International conference on machine learning, PMLR, pp 5241–5250 – reference: Zhang D, Yuan Z, Liu H, Xiong H et al (2022) Learning to walk with dual agents for knowledge graph reasoning. In: Proceedings of the AAAI Conference on artificial intelligence, vol 36, pp 5932–5941 – reference: Wang B, Shen T, Long G, Zhou T, Wang Y, Chang Y (2021) Structure-augmented text representation learning for efficient knowledge graph completion. In: Proceedings of the web conference 2021, pp 1737–1748 – reference: Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations – reference: Huang X, Zhang J, Li D, Li P (2019) Knowledge graph embedding based question answering. In: Proceedings of the 12th ACM international conference on web search and data mining, pp 105–113 – reference: Chen W, Xiong W, Yan X, Wang WY (2018) Variational knowledge graph reasoning. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1. Long Papers, pp 1823–1832 – reference: Vashishth S, Sanyal S, Nitin V, Talukdar P (2019) Composition-based multi-relational graph convolutional networks. In: International conference on learning representations – reference: Nickel M, Tresp V, Kriegel H-P (2011) A three-way model for collective learning on multi-relational data. In: ICML – reference: Yao L, Mao C, Luo Y (2019) Kg-bert: bert for knowledge graph completion. arXiv preprint arXiv:1909.03193 – reference: OhBSeoSHwangJLeeDLeeK-HOpen-world knowledge graph completion for unseen entities and relations via attentive feature aggregationInf Sci202258646848410.1016/j.ins.2021.11.085 – reference: Lv X, Han X, Hou L, Li J, Liu Z, Zhang W, Zhang Y, Kong H, Wu S (2020) Dynamic anticipation and completion for multi-hop reasoning over sparse knowledge graph. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 5694–5703 – reference: Lin XV, Socher R, Xiong C (2018) Multi-hop knowledge graph reasoning with reward shaping. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3243–3253 – reference: Lv X, Lin Y, Cao Y, Hou L, Li J, Liu Z, Li P, Zhou J (2022) Do pre-trained models benefit knowledge graph completion? A reliable evaluation and a reasonable approach. In: Findings of the association for computational linguistics: ACL 2022, pp 3570–3581 – reference: Pavlović A, Sallinger E (2022) Expressive: a spatio-functional embedding for knowledge graph completion. In: The 11th international conference on learning representations – reference: Kenton JDM-WC, Toutanova LK (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp 4171–4186 – reference: Yang B, Yih SW-T, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the international conference on learning representations (ICLR) 2015 – reference: Liu Y, Sun Z, Li G, Hu W (2022) I know what you do not know: knowledge graph embedding via co-distillation learning. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 1329–1338 – reference: Xiao C, He X, Cao Y (2023) Knowledge graph embedding by normalizing flows. In: AAAI conference on artificial intelligence – reference: ZhuZZhangZXhonneuxL-PTangJNeural bellman-ford networks: a general graph neural network framework for link predictionAdv Neural Inf Process Syst2021342947629490 – reference: Nathani D, Chauhan J, Sharma C, Kaul M (2019) Learning attention-based embeddings for relation prediction in knowledge graphs. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 4710–4723 – reference: Das R, Dhuliawala S, Zaheer M, Vilnis L, Durugkar I, Krishnamurthy A, Smola A, McCallum A (2018) Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning. In: International conference on learning representations – reference: Chen W, Cao Y, Feng F, He X, Zhang Y (2022) Explainable sparse knowledge graph completion via high-order graph reasoning network. arXiv preprint arXiv:2207.07503 – reference: Markowitz E, Balasubramanian K, Mirtaheri M, Annavaram M, Galstyan A, Ver Steeg G (2022) Statik: structure and text for inductive knowledge graph completion. In: Findings of the association for computational linguistics: NAACL 2022, pp 604–615 – reference: Xie R, Liu Z, Jia J, Luan H, Sun M (2016) Representation learning of knowledge graphs with entity descriptions. In: Proceedings of the AAAI conference on artificial intelligence, vol 30 – reference: Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4320–4328 – reference: Balazevic I, Allen C, Hospedales T (2019) Tucker: tensor factorization for knowledge graph completion. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics – reference: Wang K, Liu Y, Ma Q, Sheng QZ (2021) Mulde: multi-teacher knowledge distillation for low-dimensional knowledge graph embeddings. In: Proceedings of the web conference 2021, pp 1716–1726 – reference: Hinton G, Vinyals O, Dean J et al (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 – reference: Wang L, Zhao W, Wei Z, Liu J (2022) Simkgc: simple contrastive knowledge graph completion with pre-trained language models. arXiv preprint arXiv:2203.02167 – reference: BishopCMPattern recognition and machine learning2006ChamSpringer – reference: ZhouLLiJGuZQiuJGuptaBBTianZPanner: pos-aware nested named entity recognition through heterogeneous graph neural networkIEEE Trans Comput Soc Syst2022451910.1109/TCSS.2022.3159366 – reference: Li R, Cao Y, Zhu Q, Bi G, Fang F, Liu Y, Li Q (2022) How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 5781–5791 – reference: Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 – reference: Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. In: 32nd AAAI conference on artificial intelligence |
| SSID | ssj0005230 |
| Score | 2.4085658 |
| Snippet | The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing... |
| SourceID | proquest springer |
| SourceType | Aggregation Database Publisher |
| StartPage | 343 |
| SubjectTerms | Algorithms Artificial Intelligence Chemistry and Earth Sciences Computer Science Data Mining and Knowledge Discovery Distillation Information Storage and Retrieval Knowledge Knowledge acquisition Knowledge representation Physics Statistics for Engineering Training |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV09T8MwELWgXVj4RhQKuoERi8R2EocFAWpVIVohRFG3yE4ctvQj7dB_zzlxaGFgjaMMd_Hzu7t3PkJugpR5SFQZ9ZVJqTAipSoKDfXCHE97hgBYVUyHo3AwFi-TYOISbqWTVTaYWAF1Nk1tjvyOxcjtuY9I-zCbUzs1ylZX3QiNXdLGBYnBV_upN3p73xJ58LpPWAoaSN9zbTOueS70JcUzC8NpKyxa_yKZf-qi1XHTPyT7jifCY-3YI7JjimNy0MxgALclT0j52Ruy13tQBRhVrkGvllBrNBDGIG-kV4DcFHIrcv8Cq_XA9zOo745dLQz8ZNZgWgBizKLcflZdag2V-NxYP56Scb_38TygbpACnflBtKQpk56SgeZasRQplhZBHuuISR3msdReZmRkJ_NJHlr-o6ViXLFc-EzbJKnhZ6RVTAtzTkBw7hljMCzEUCbOmU4RBIKMM25bbGPeId3GhonbDWWy8V2H3DZ23Sxvbk62HknQI0nlkWR98f_XLskeQ45RS8K6pIVWM1fIEZb62v0I30nSuUo priority: 102 providerName: ProQuest |
| Title | VEM2L: an easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion |
| URI | https://link.springer.com/article/10.1007/s10618-023-01001-y https://www.proquest.com/docview/2928731212 |
| Volume | 38 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLZgExIXHgPEYEw5cCRSm_SRchuo2wRsQoihcaqSLuXWoXU77N_jpC0biAunSm3Ug-3Yn-PPDsC1nzIHgSqjrtQp9bSXUhkGmjpBhtGeoQO0FdPROBhOvIepP62awoqa7V6XJK2n3mp2C1xBMcZg-muIQOtdaPomd0crnrDeFrGDl73BwqO-cJ2qVebvf_wAlr9qoTbE9I_goMKGpFcq8xh2dN6Cw_reBVJtwxbsWdpmWpxA8RaP2NMtkTnRslgTtVqSkqGBToxkNfGKIDIlmaG4fxDD9MD1M1JOjl0tNPk-VyPznKCHWRTb7-xIa2Kp59po8RQm_fj1fkiraxTop-uHS5oy4UjhK64kSxFgKc_PIhUyoYIsEsqZaRGae_kEDwz6UUIyLlnmuUyZI1LNz6CRz3N9DsTj3NFaY1KIiUyUMZWiC_BnnHHTYBvxNnRqaSbVXigSFmFWxl2MkW24qSW8-byZm2x0k6BuEqubZH3xv-WXsI_W4JUEsQ40UIr6ChHDUnVhV_QHXWj2Bu-PMT7v4vHzS9eazRd91rxQ |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07T8MwED5BGWDhjSjPG2DDIrHzcJAQQtBSoGVqUbcQJw5bCqQV6p_iN3LOgwIDG2scebg7f_7O_s4HcOTG3CKiypkd6Zg52olZ5HuaWV5Kuz0nACxuTHsPXmfg3A3d4Rx81LUwRlZZY2IB1MkoNmfkpzwgbi9sQtqLl1dmukaZ29W6hUYZFvd6-k4pW35-e03-Pea83epfdVjVVYC92K4_ZjGXViRdJVTEY-IbynHTQPlcKi8NpLISLX3Tpk4Kz5ABJSMuIp46NlfmxFALmnceFhwhArOiZPvmm6RElFXJ0mGutK2qSKcq1fNsyWiHpOTdyJimPyjtr1vYYnNrr8JyxUrxsgyjNZjT2Tqs1B0fsAKADcgfWz3ePcMoQx3lU1STMZaKEAJNTGuhFxITxtRI6p_RKEvo_wTLl2onbxq_zvFwlCEh2lv-_VvxhDYWUndtomYTBv9i4C1oZKNMbwOSkS2tNSWhlDgFKVcxQY6bCC5MQW8gmrBX2zCs1l4eziKlCSe1XWfDs3eajUdC8khYeCSc7vw92yEsdvq9bti9fbjfhSVO7KYUo-1Bgyyo94mdjNVBERIIT_8dg5-UyfNo |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3JTsMwEB1BkRAXdkShwBzghtXEzuIgIcTSiqWtKgSIW4hTh1sKpBXqr_F1jLNQ4MCNa2L5MDN-fmO_8QDsuzG3iKhyZkc6Zo52Yhb5nmaWl9BuzwkA8xvTbs-7vHeuH93HGfioamGMrLLCxByoB8PYnJE3eUDcXtiEtM2klEX0L9onL6_MdJAyN61VO40iRG705J3St-z46oJ8fcB5u3V3fsnKDgPsxXb9EYu5tCLpKqEiHhP3UI6bBMrnUnlJIJU10NI3Leuk8AwxUDLiIuKJY3NlTg-1oHlnYY6G-FYN5s5avf7tN4GJKGqUpcNcaVtlyU5ZuOfZktF-Sam8ETVNfhDcX3ey-VbXXobFkqPiaRFUKzCj01VYqvo_YAkHa5A9tLq8c4RRijrKJqjGIyz0IQShmFSyLyRejIkR2D-jMSiNH2Dxbu34TePXqR4OUyR8e8u-f8sf1MZc-K5NDK3D_b-YeANq6TDVm4COEJbWmlJSSqOChKuYAMgdCC5MeW8g6tCobBiWKzELp3FTh8PKrtPf01ebjUdC8kiYeyScbP092x7MU_yFnavezTYscKI6hTKtATUyoN4hqjJSu2VMIDz9dxh-Avtf-Po |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=VEM2L%3A+an+easy+but+effective+framework+for+fusing+text+and+structure+knowledge+on+sparse+knowledge+graph+completion&rft.jtitle=Data+mining+and+knowledge+discovery&rft.au=He%2C+Tao&rft.au=Liu%2C+Ming&rft.au=Cao%2C+Yixin&rft.au=Qu%2C+Meng&rft.date=2024-03-01&rft.pub=Springer+US&rft.issn=1384-5810&rft.eissn=1573-756X&rft.volume=38&rft.issue=2&rft.spage=343&rft.epage=371&rft_id=info:doi/10.1007%2Fs10618-023-01001-y&rft.externalDocID=10_1007_s10618_023_01001_y |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1384-5810&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1384-5810&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1384-5810&client=summon |