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Named entity disambiguation in short texts over knowledge graphs

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Abstract

The ever-growing usage of knowledge graphs (KGs) positions named entity disambiguation (NED) at the heart of designing accurate KG-driven systems such as query answering systems (QAS). According to the current research, most studies dealing with NED on KGs involve long texts, which is not the case of short text fragments, identified by their limited contexts. The accuracy of QASs strongly depends on the management of such short text. This limitation motivates this paper, which studies the NED problem on KGs, involving only short texts. First, we propose a NED approach including the following steps: (i) context expansion using WordNet to measure its similarity to the resource context. (ii) Exploiting coherence between entities in queries that contain more than one entity, such as “Is Michelle Obama the wife of Barack Obama?”. (iii) Taking into account the relations between words to calculate their similarity with the properties of a resource. (iv) the use of syntactic features. The NED solution approach is compared to state-of-the-art approaches using five datasets. The experimental results show that our approach outperforms these systems by 27% in the F-measure. A system called Welink, implementing our proposal, is available on GitHub, and it is also accessible via a REST API.

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Notes

  1. https://www.slideshare.net/LuMa921/enterprise-knowledge-graph.

  2. PREFIX dbr: <http://dbpedia.org/resource/>.

  3. http://193.194.84.136:8000/.

  4. https://github.com/wissembrdj/welink.

  5. This triple is a part of DBpedia’s Knowledge Graph.

  6. http://193.194.84.136:8000/.

  7. https://github.com/wissembrdj/welink.

  8. https://dbpedia.org/sparql.

  9. http://qald.aksw.org.

  10. https://trec.nist.gov/data/microblog2014.html.

References

  1. Al-Moslmi T, Ocaña MG, Opdahl AL, Veres C (2020) Named entity extraction for knowledge graphs: a literature overview. IEEE Access 8:32862–32881. https://doi.org/10.1109/ACCESS.2020.2973928

    Article  Google Scholar 

  2. Alokaili A, Menai MEB (2020) SVM ensembles for named entity disambiguation. Computing 102(4):1051–1076. https://doi.org/10.1007/s00607-019-00748-x

    Article  MathSciNet  Google Scholar 

  3. Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) Dbpedia: a nucleus for a web of open data. In: The semantic web. Springer, pp 722–735. https://doi.org/10.1007/978-3-540-76298-0_52

  4. Azad HK, Deepak A (2019) A new approach for query expansion using Wikipedia and wordnet. Inf Sci 492:147–163. https://doi.org/10.1016/j.ins.2019.04.019

    Article  Google Scholar 

  5. Bellomarini L, Gottlob G, Pieris A, Sallinger E (2018) Swift logic for big data and knowledge graphs. In: International conference on current trends in theory and practice of informatics. Springer, pp 3–16. https://doi.org/10.1007/978-3-319-73117-9_1

  6. Bennett PN, Gabrilovich E, Kamps J, Karlgren J (2014) Report on the sixth workshop on exploiting semantic annotations in information retrieval (esair’13). SIGIR Forum 48(1):13–20. https://doi.org/10.1145/2641383.2641387

  7. Bentounsi I, Boufaïda Z (2015) Disambiguation of semantic types in complex noun phrases for extracting candidate terms. Int J Metadata, Semant Ontol 10(2):112–122. https://doi.org/10.1504/IJMSO.2015.070830

    Article  Google Scholar 

  8. Berry DM (2007) Ambiguity in natural language requirements documents. In: Monterey workshop. Springer, pp 1–7. https://doi.org/10.1007/978-3-540-89778-1_1

  9. Bollacker KD, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: ACM SIGMOD, pp 1247–1250. https://doi.org/10.1145/1376616.1376746

  10. Bouarroudj W, Boufaida Z (2018) A candidate generation algorithm for named entities disambiguation using dbpedia. In: World conference on information systems and technologies. Springer, pp 712–721. https://doi.org/10.1007/978-3-319-77703-0_71

  11. Bouarroudj W, Boufaida Z, Bellatreche L (2019) Welink: a named entity disambiguation approach for a qas over knowledge bases. In: International conference on flexible query answering systems. Springer, pp 85–97. https://doi.org/10.1007/978-3-030-27629-4_11

  12. Burel G, Saif H, Alani H (2017) Semantic wide and deep learning for detecting crisis-information categories on social media. In: International semantic web conference. Springer, pp 138–155. https://doi.org/10.1007/978-3-319-68288-4_9

  13. Dai W, Vyatkin V, Christensen JH, Dubinin VN (2015) Bridging service-oriented architecture and IEC 61499 for flexibility and interoperability. IEEE Trans Ind Inf 11(3):771–781. https://doi.org/10.1109/TII.2015.2423495

    Article  Google Scholar 

  14. Diefenbach D, Giménez-García J, Both A, Singh K, Maret P (2020) Qanswer kg: Designing a portable question answering system over RDF data. In: European semantic web conference. Springer, pp 429–445. https://doi.org/10.1007/978-3-030-49461-2_25

  15. Diefenbach D, Lopez V, Singh K, Maret P (2018) Core techniques of question answering systems over knowledge bases: a survey. Knowl Inf Syst 55(3):529–569. https://doi.org/10.1007/s10115-017-1100-y

    Article  Google Scholar 

  16. Dredze M, McNamee P, Rao D, Gerber A, Finin T (2010) Entity disambiguation for knowledge base population. In: Proceedings of the 23rd international conference on computational linguistics. Association for Computational Linguistics, pp 277–285

  17. Dubey M, Banerjee D, Chaudhuri D, Lehmann J (2018) Earl: joint entity and relation linking for question answering over knowledge graphs. In: International semantic web conference. Springer, pp 108–126. https://doi.org/10.1007/978-3-030-00671-6_7

  18. Dubey M, Dasgupta S, Sharma A, Höffner K, Lehmann J (2016) Asknow: a framework for natural language query formalization in Sparql. In: European semantic web conference. Springer, pp 300–316. https://doi.org/10.1007/978-3-319-34129-3_19

  19. Fang Z, Cao Y, Li Q, Zhang D, Zhang Z, Liu Y (2019) Joint entity linking with deep reinforcement learning. In: The world wide web conference, pp 438–447. https://doi.org/10.1145/3308558.3313517

  20. Ferragina P, Scaiella U (2011) Fast and accurate annotation of short texts with Wikipedia pages. IEEE Softw 29(1):70–75. https://doi.org/10.1109/MS.2011.122

    Article  Google Scholar 

  21. Green Jr BF, Wolf AK, Chomsky C, Laughery K (1961) Baseball: an automatic question-answerer. In: Papers presented at the May 9–11, 1961, western joint IRE-AIEE-ACM computer conference, pp 219–224. https://doi.org/10.1145/1460690.1460714

  22. Guo S, Chang MW, Kiciman E (2013) To link or not to link? A study on end-to-end tweet entity linking. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1020–1030

  23. Gutierrez C, Sequeda JF (2021) Knowledge graphs. Commun ACM 64(3):96–104. https://doi.org/10.1145/3418294

    Article  Google Scholar 

  24. Hachey B, Radford W, Nothman J, Honnibal M, Curran JR (2013) Evaluating entity linking with Wikipedia. Artif Intell 194:130–150. https://doi.org/10.1016/j.artint.2012.04.005

    Article  MathSciNet  MATH  Google Scholar 

  25. Harris S, Seaborne A (2013) SPARQL 1.1 overview. https://www.w3.org/TR/sparql11-overview/

  26. Hasibi F, Balog K, Bratsberg SE (2017) Entity linking in queries: efficiency vs. effectiveness. In: European conference on information retrieval. Springer, pp 40–53. https://doi.org/10.1007/978-3-319-56608-5_4

  27. He Q (2021) People of ACM. https://www.acm.org/articles/people-of-acm/2021/qi-he?fbclid=IwAR0wMvkvDL5k_iuShKqGluAG5z9_t5dsw-bnnwTMVf2bE03KN4sBHuwXBwc

  28. He Q, Yang J, Shi B (2020) Constructing knowledge graph for social networks in A deep and holistic way. In: Companion of the web conference, pp 307–308. https://doi.org/10.1145/3366424.3383112

  29. Hoffart J, Yosef MA, Bordino I, Fürstenau H, Pinkal M, Spaniol M, Taneva B, Thater S, Weikum G (2011) Robust disambiguation of named entities in text. In: Proceedings of the 2011 conference on empirical methods in natural language processing, pp 782–792

  30. Höffner K, Walter S, Marx E, Usbeck R, Lehmann J, Ngonga Ngomo AC (2017) Survey on challenges of question answering in the semantic web. Semant Web 8(6):895–920. https://doi.org/10.3233/SW-160247

    Article  Google Scholar 

  31. Hogan A, Blomqvist E, Cochez M, d’Amato C, de Melo G, Gutierrez C, Gayo JEL, Kirrane S, Neumaier S, Polleres A, Navigli R, Ngomo ACN, Rashid SM, Rula A, Schmelzeisen L, Sequeda J, Staab S, Zimmermann A (2021) Knowledge graphs. Synth Lect Data Semantics Knowledge 12(2):1–257

  32. Hu X, Duan J, Dang D (2021) Natural language question answering over knowledge graph: the marriage of sparql query and keyword search. Knowl Inf Syst. https://doi.org/10.1007/s10115-020-01534-4

    Article  Google Scholar 

  33. 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. https://doi.org/10.1145/3289600.3290956

  34. Klie JC, de Castilho RE, Gurevych I (2020) From zero to hero: human-in-the-loop entity linking in low resource domains. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 6982–6993. https://doi.org/10.18653/v1/2020.acl-main.624

  35. Kolitsas N, Ganea OE, Hofmann T (2018) End-to-end neural entity linking. ArXiv preprint arXiv:1808.07699. https://doi.org/10.18653/v1/K18-1050

  36. Ling X, Singh S, Weld DS (2015) Design challenges for entity linking. Trans Assoc Comput Linguist 3:315–328. https://doi.org/10.1162/tacl_a_00141

    Article  Google Scholar 

  37. Logeswaran L, Chang MW, Lee K, Toutanova K, Devlin J, Lee H (2019) Zero-shot entity linking by reading entity descriptions. ArXiv preprint arXiv:1906.07348

  38. Manning CD, Raghavan P, Schütze H (2008) Scoring, term weighting and the vector space model. Introd Inf Retr 100:2–4. https://doi.org/10.1017/CBO9780511809071.007

    Article  Google Scholar 

  39. Marginean A (2017) Question answering over biomedical linked data with grammatical framework. Semant Web 8(4):565–580. https://doi.org/10.3233/SW-160223

    Article  Google Scholar 

  40. Mendes PN, Jakob M, García-Silva A, Bizer C (2011) Dbpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th international conference on semantic systems. ACM, pp 1–8. https://doi.org/10.1145/2063518.2063519

  41. Mezni H, Benslimane D, Bellatreche L (2021) Context-aware service recommendation based on knowledge graph embedding. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2021.3059506

  42. Michel F, Gandon F, Ah-Kane V, Bobasheva A, Cabrio E, Corby O, Gazzotti R, Giboin A, Marro S, Mayer T, et al. (2020) Covid-on-the-web: knowledge graph and services to advance covid-19 research. In: International semantic web conference. Springer, pp 294–310. https://doi.org/10.1007/978-3-030-62466-8_19

  43. Miller GA (1995) Wordnet: a lexical database for English. Commun ACM 38(11):39–41. https://doi.org/10.1145/219717.219748

    Article  Google Scholar 

  44. Mishra A, Jain SK (2016) A survey on question answering systems with classification. J King Saud Univer-Comput Inf Sci 28(3):345–361. https://doi.org/10.1016/j.jksuci.2014.10.007

    Article  Google Scholar 

  45. Moro A, Raganato A, Navigli R (2014) Entity linking meets word sense disambiguation: a unified approach. Trans Assoc Comput Linguist 2:231–244. https://doi.org/10.1162/tacl_a_00179

    Article  Google Scholar 

  46. Oliveira IL, Fileto R, Speck R, Garcia LP, Moussallem D, Lehmann J (2021) Towards holistic entity linking: survey and directions. Inf Syst 95:101624. https://doi.org/10.1016/j.is.2020.101624

    Article  Google Scholar 

  47. Parravicini A, Patra R, Bartolini DB, Santambrogio MD (2019) Fast and accurate entity linking via graph embedding. In: Proceedings of the 2nd joint international workshop on graph data management experiences and systems (GRADES) and network data analytics (NDA), pp 1–9. https://doi.org/10.1145/3327964.3328499

  48. Pohl K, Böckle G, van Der Linden FJ (2005) Software product line engineering: foundations, principles and techniques. Springer

  49. Rama-Maneiro E, Vidal JC, Lama M (2020) Collective disambiguation in entity linking based on topic coherence in semantic graphs. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2020.105967

    Article  Google Scholar 

  50. Sakor A, Mulang IO, Singh K, Shekarpour S, Vidal ME, Lehmann J, Auer S (2019) Old is gold: linguistic driven approach for entity and relation linking of short text. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1, pp 2336–2346. https://doi.org/10.18653/v1/N19-1243

  51. Sevgili Ö, Panchenko A, Biemann C (2019) Improving neural entity disambiguation with graph embeddings. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp 315–322. https://doi.org/10.18653/v1/P19-2044

  52. Sevgili O, Shelmanov A, Arkhipov M, Panchenko A, Biemann C (2020) Neural entity linking: a survey of models based on deep learning. ArXiv preprint arXiv:2006.00575

  53. Shekarpour S, Marx E, Ngonga Ngomo AC, Auer S (2014) Sina: semantic interpretation of user queries for question answering on interlinked data. Web Semant: Sci, Serv Agents World Wide Web. https://doi.org/10.1016/j.websem.2014.06.002

  54. Shen W, Wang J, Han J (2015) Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans Knowl Data Eng 27(2):443–460. https://doi.org/10.1109/TKDE.2014.2327028

    Article  Google Scholar 

  55. Singh K, Radhakrishna AS, Both A, Shekarpour S, Lytra I, Usbeck R, Vyas A, Khikmatullaev A, Punjani D, Lange C, et al. (2018) Why reinvent the wheel: let’s build question answering systems together. In: Proceedings of the 2018 world wide web conference, pp 1247–1256. https://doi.org/10.1145/3178876.3186023

  56. Singhal A (2012) Introducing the knowledge graph: things, not strings. Off Google Blog 5:16. https://blog.google/products/search/introducingknowledge-graph-things-not

  57. Usbeck R, Gusmita RH, Ngomo AN, Saleem M (2018) 9th challenge on question answering over linked data (QALD-9). In: SemDeep-4 and NLIWOD-4 and ISWC vol 2241, pp 58-64. CEUR-WS.org

  58. Usbeck R, Ngomo ACN, Conrads F, Röder M, Napolitano G (2018) 8th challenge on question answering over linked data (qald-8). Language 7:1

    Google Scholar 

  59. Usbeck R, Ngomo ACN, Haarmann B, Krithara A, Röder M, Napolitano G (2017) 7th open challenge on question answering over linked data (qald-7). In: Semantic web evaluation challenge. Springer, pp 59–69. https://doi.org/10.1007/978-3-319-69146-6_6

  60. Usbeck R, Ngomo ACN, Röder M, Gerber D, Coelho SA, Auer S, Both A (2014) Agdistis-graph-based disambiguation of named entities usinglinked data. In: International semantic web conference. Springer, pp 457–471. https://doi.org/10.1007/978-3-319-11964-9_29

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Authors and Affiliations

  1. LIRE Laboratory, Abdelhamid Mehri Constantine 2 University, Constantine, Algeria

    Wissem Bouarroudj & Zizette Boufaida

  2. LIAS/ISAE-ENSMA, Poitiers University, Poitiers, France

    Ladjel Bellatreche

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  1. Wissem Bouarroudj
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Bouarroudj, W., Boufaida, Z. & Bellatreche, L. Named entity disambiguation in short texts over knowledge graphs. Knowl Inf Syst 64, 325–351 (2022). https://doi.org/10.1007/s10115-021-01642-9

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