Knowledge Harvesting: Achievements and Challenges
Abstract
This article gives an overview on knowledge harvesting: automatically constructing large high-quality knowledge bases from Internet sources. The first part reviews key principles and best-practice methods. The second part points out open challenges for future research.
References
[1]
Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: ACM DL (2000)
[2]
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC - 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52
[3]
Banko, M., et al.: Open information extraction from the web. In: IJCAI (2007)
[4]
Bast, H., Buchhold, B., Haussmann, E.: Semantic search on text and knowledge bases. Found. Trends Inf. Retrieval 10(2–3) (2016)
[5]
Bollacker, K., et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD (2008)
[6]
Brin, S.: Extracting patterns and relations from the world wide web. In: Atzeni, P., Mendelzon, A., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999). https://doi.org/10.1007/10704656_11
[7]
Carlson, A.J., et al.: Toward an architecture for never-ending language learning. In: AAAI (2010)
[8]
Chen, X., Shrivastava, A., Gupta, A.: NEIL: extracting visual knowledge from web data. In: ICCV (2013)
[9]
Chiticariu, L., Li, Y., Reiss, F.: Transparent machine learning for information extraction: state-of-the-art and the future. In: EMNLP (Tutorial) (2015)
[10]
Craven, M., et al.: Learning to construct knowledge bases from the world wide web. Art. Intell. 118(1) (2000)
[11]
Dalvi, N., et al.: A web of concepts. In: PODS (2009)
[12]
Del Corro, L., Gemulla, R.: ClausIE: clause-based open information extraction. In: WWW 2013 (2013)
[13]
Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)
[14]
Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. Morgan & Claypool, San Rafael (2009)
[15]
Dong, X.L., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD (2014)
[16]
Dong, X.L., et al.: Knowledge-based trust: estimating the trustworthiness of web sources. PVLDB 8(9) (2015)
[17]
Ernst, P., et al.: KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences. BMC Bioinform. 16(157) (2015)
[18]
Etzioni, O., et al.: Unsupervised named-entity extraction from the web: an experimental study. Art. Intell. 165(1) (2005)
[19]
Fellbaum, C., Miller, G.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
[20]
Galarraga, L., et al.: Canonicalizing open knowledge bases. In: CIKM (2014)
[21]
Galarraga, L., et al.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6) (2015)
[22]
Galarraga, L., et al.: Predicting completeness in knowledge bases. In: WSDM (2017)
[23]
Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: COLING (1992)
[24]
Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool, San Rafael (2011)
[25]
Hoffart, J., et al.: YAGO2: a spatially and temporally enhanced knowledge base from wikipedia. Art. Intell. 194 (2013)
[26]
Hoffart, J., Altun, Y., Weikum, G.: Discovering emerging entities with ambiguous names. In: WWW 2014 (2014)
[27]
Hoffart, J., et al.: The knowledge awakens: keeping knowledge bases fresh with emerging entities. In: WWW 2016 (2016)
[28]
Ferrucci, D.A.: “This is Watson”. IBM J. Res. Dev. 56(3/4) (2012). Special Issue
[29]
Kobren, A., et al.: Getting more for less: optimized crowdsourcing with dynamic tasks and goals. In: WWW 2015 (2015)
[30]
Koller, D., Friedman, N.: Probabilistic Graphical Models. MIT Press, Cambridge (2009)
[31]
Kozareva, Z., Hovy, E.H.: Learning arguments and supertypes of semantic relations using recursive patterns. In: ACL (2010)
[32]
Krause, S., Li, H., Uszkoreit, H., Xu, F.: Large-scale learning of relation-extraction rules with distant supervision from the web. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 263–278. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35176-1_17
[33]
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521 (2015)
[34]
Lenat, D., Feigenbaum, E.: On the thresholds of knowledge. Art. Intell. 47(1) (1991)
[35]
Lenat, D.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11) (1995)
[36]
Li, Y., et al.: A survey on truth discovery. SIGKDD Explor. 17(2) (2015)
[37]
Ling, X., Weld, D.: Temporal information extraction. In: AAAI (2010)
[38]
Ling, X., Singh, S., Weld, D.: Design challenges for entity linking. TACL 3 (2015)
[39]
Mausam, et al.: Open language learning for information extraction. In: EMNLP-CoNLL (2012)
[40]
Mintz, M., et al.: Distant supervision for relation extraction without labeled data. In: ACL/IJCNLP (2009)
[41]
Mitchell, T., et al.: Never-ending learning. In: AAAI (2015)
[42]
Mukherjee, S., Weikum, G., Danescu-Niculescu-Mizil, C.: People on drugs: credibility of user statements in health communities. In: KDD (2014)
[43]
Nakashole, N., Theobald, M., Weikum, G.: Scalable knowledge harvesting with high precision and high recall. In: WSDM (2011)
[44]
Navigli, R., Ponzetto, S.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Art. Intell. 193 (2012)
[45]
Nickel, M., et al.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1) (2016)
[46]
Nie, Z., Wen, J.-R., Ma, W.-Y.: Statistical entity extraction from the web. Proc. IEEE 100(9) (2012)
[47]
Pasca, M.: Open-domain fine-grained class extraction from web search queries. In: EMNLP (2013)
[48]
Pasternack, J., Roth, D.: Latent credibility analysis. In: WWW 2013 (2013)
[49]
Popat, K., et al.: Where the truth lies: explaining the credibility of emerging claims on the web and social media. In: WWW 2017 (2017)
[50]
Ponzetto, S., Strube, M.: Deriving a large-scale taxonomy from wikipedia. In: AAAI (2007)
[51]
Preda, N., et al.: Active knowledge: dynamically enriching RDF knowledge bases by web services. In: SIGMOD (2010)
[52]
Pujara, J., Miao, H., Getoor, L., Cohen, W.: Knowledge graph identification. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 542–557. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_34
[53]
Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E., Weikum, G.: YAGO: a multilingual knowledge base from wikipedia, wordnet, and geonames. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 177–185. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_19
[54]
Rohrbach, A., et al.: A dataset for movie description. In: CVPR (2015)
[55]
Sarawagi, S.: Information extraction. Found. Trends Databases 1(3) (2008)
[56]
Shen, W., Wang, J., Han, J.: Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2) (2015)
[57]
Shin, J., et al.: Incremental knowledge base construction using deepdive. PVLDB 8(11) (2015)
[58]
Speer, R., Havasi, C.: Representing general relational knowledge in ConceptNet 5. In: LREC (2012)
[59]
Staab, S., Studer, R.: Handbook on Ontologies. Springer, Heidelberg (2009)
[60]
Suchanek, F., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: WWW 2007 (2007)
[61]
Suchanek, F., Sozio, M., Weikum, G.: SOFIE: a self-organizing framework for information extraction. In: WWW 2009 (2009)
[62]
Talaika, A., et al.: IBEX: harvesting entities from the web using unique identifiers. In: WebDB 2015 (2015)
[63]
Talukdar, P., Wijaya, D., Mitchell, T.: Coupled temporal scoping of relational facts. In: WSDM 2012 (2012)
[64]
Tandon, N., et al.: WebChild: harvesting and organizing commonsense knowledge from the web. In: WSDM (2014)
[65]
Tandon, N., et al.: Knowlywood: mining activity knowledge from hollywood narratives. In: CIKM (2015)
[66]
Tandon, N., et al.: Commonsense in parts: mining part-whole relations from the web and image tags. In: AAAI 2016 (2016)
[67]
Venetis, P., et al.: Recovering semantics of tables on the web. PVLDB 4(9) (2011)
[68]
Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10) (2014)
[69]
Wang, R., Cohen, W.: Iterative set expansion of named entities using the web. In: ICDM (2008)
[70]
Wang, Z., et al.: XLore: a large-scale English-Chinese bilingual knowledge graph. In: ISWC (2013)
[71]
Wang, Y., et al.: Coupling label propagation and constraints for temporal fact extraction. In: ACL (2012)
[72]
Wu, F., Hoffmann, R., Weld, D.: Information extraction from wikipedia: moving down the long tail. In: KDD (2008)
[73]
Wu, W., et al.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD (2012)
[74]
Yao, L., et al.: Structured relation discovery using generative models. In: EMNLP (2011)
[75]
Zhu, J., et al.: StatSnowball: a statistical approach to extracting entity relationships. In: WWW 2009 (2009)
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Published: 11 March 2022
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