Abstract
Today’s knowledge bases (KBs) capture facts about the world’s entities, their properties, and their semantic relationships in the form of subject-predicate-object (SPO) triples. Domain-oriented KBs, such as DBpedia, Yago, Wikidata or Freebase, capture billions of facts that have been (semi-)automatically extracted from Wikipedia articles. Their commercial counterparts at Google, Bing or Baidu provide back-end support for search engines, online recommendations, and various knowledge-centric services.
This invited talk provides an overview of our recent contributions—and also highlights a number of open research challenges—in the context of extracting, managing, and reasoning with large semantic KBs. Compared to domain-oriented extraction techniques, we aim to acquire facts for a much broader set of predicates. Compared to open-domain extraction methods, the SPO arguments of our facts are canonicalized, thus referring to unique entities with semantically typed predicates. A core part of our work focuses on developing scalable inference techniques for querying an uncertain KB in the form of a probabilistic database. A further, very recent research focus lies also in scaling out these techniques to a distributed setting. Here, we aim to process declarative queries, posed in either SQL or logical query languages such as Datalog, via a proprietary, asynchronous communication protocol based on the Message Passing Interface.
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References
Abdelaziz, I., Harbi, R., Khayyat, Z., Kalnis, P.: A survey and experimental comparison of distributed SPARQL engines for very large RDF data. PVLDB 10(13), 2049–2060 (2017)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Ives, Z.: DBpedia: a nucleus for a web of open data. In: ISWC, pp. 11–15 (2007)
Benjelloun, O., Sarma, A.D., Halevy, A.Y., Theobald, M., Widom, J.: Databases with uncertainty and lineage. VLDB J. 17(2), 243–264 (2008)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)
Dylla, M., Miliaraki, I., Theobald, M.: A temporal-probabilistic database model for information extraction. PVLDB 6(14), 1810–1821 (2013a)
Dylla, M., Miliaraki, I., Theobald, M.: Top-k query processing in probabilistic databases with non-materialized views. In: ICDE, pp. 122–133 (2013b)
Dylla, M., Sozio, M., Theobald, M.: Resolving temporal conflicts in inconsistent RDF knowledge bases. In: BTW, pp. 474–493 (2011)
Dylla, M., Theobald, M., Miliaraki, I.: Querying and learning in probabilistic databases. In: Reasoning Web, pp. 313–368 (2014)
Gurajada, S., Seufert, S., Miliaraki, I., Theobald, M.: TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing. In: SIGMOD, pp. 289–300 (2014a)
Gurajada, S., Seufert, S., Miliaraki, I., Theobald, M.: Using graph summarization for join-ahead pruning in a distributed RDF engine. In: SWIM, pp. 41:1–41:4 (2014b)
Gurajada, S., Theobald, M.: Distributed processing of generalized graph-pattern queries in SPARQL 1.1. CoRR, abs/1609.05293 (2016a)
Gurajada, S., Theobald, M.: Distributed set reachability. In: SIGMOD, pp. 1247–1261 (2016b)
Hoffart, J., Seufert, S., Nguyen, D.B., Theobald, M., Weikum, G.: KORE: keyphrase overlap relatedness for entity disambiguation. In: CIKM, pp. 545–554 (2012)
Mutsuzaki, M., et al.: Trio-one: layering uncertainty and lineage on a conventional DBMS. In: CIDR, pp. 269–274 (2007)
Nakashole, N., Theobald, M., Weikum, G.: Scalable knowledge harvesting with high precision and high recall. In: WSDM, pp. 227–236 (2011)
Nakashole, N., Weikum, G., Suchanek, F.M.: PATTY: a taxonomy of relational patterns with semantic types. In: EMNLP-CoNLL, pp. 1135–1145 (2012)
Nguyen, D.B., Abujabal, A., Tran, K., Theobald, M., Weikum, G.: Query-driven on-the-fly knowledge base construction. PVLDB 11(1), 66–79 (2017a)
Nguyen, D.B., Hoffart, J., Theobald, M., Weikum, G.: AIDA-light: high-throughput named-entity disambiguation. In: LDOW (2014)
Nguyen, D.B., Theobald, M., Weikum, G.: J-NERD: joint named entity recognition and disambiguation with rich linguistic features. TACL 4, 215–229 (2016)
Nguyen, D.B., Theobald, M., Weikum, G.: J-REED: joint relation extraction and entity disambiguation. In: CIKM, pp. 2227–2230 (2017b)
Papaioannou, K., Theobald, M., Böhlen, M.H.: Supporting set operations in temporal-probabilistic databases. In: ICDE, pp. 1180–1191 (2018)
Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: WWW, pp. 697–706 (2007)
Suciu, D., Olteanu, D., Ré, C., Koch, C.: Probabilistic databases. Synth. Lect. Data Manag. 3(2), 1–180 (2011)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Comm. of the ACM 57(10), 78–85 (2014)
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Theobald, M. (2019). From Big Data to Big Knowledge. In: Catania, B., Královič, R., Nawrocki, J., Pighizzini, G. (eds) SOFSEM 2019: Theory and Practice of Computer Science. SOFSEM 2019. Lecture Notes in Computer Science(), vol 11376. Springer, Cham. https://doi.org/10.1007/978-3-030-10801-4_5
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