Empirical Semantics
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This document discusses empirical semantics and insights that can be gained from observing large knowledge bases. It notes that formal semantics often does not accurately model real-world knowledge and proposes some challenges for developing alternative semantic models. Specifically, it suggests empirical study has shown that identity, meaningful names, and different predicate patterns are not adequately captured. The goal is to develop descriptive theories of knowledge based on observation rather than prescriptive theories.
Empirical Semantics
- 1. Empirical Semantics modelling knowledge as it is, not as it should be Frank van Harmelen Vrije Universiteit Amsterdam Creative Commons License CC BY 3.0: Allowed to copy, redistribute remix & transform But must attribute 1 Many thanks to all at KR&R@VU: Wouter Beek, Joe Raad, Peter Bloem, Stefan Schlobach, Zhisheng Huang, and many others over the years
- 2. The ‘K’ in ‘Semantic Web’ stands for ‘Knowledge’ Frank van Harmelen Vrije Universiteit Amsterdam Creative Commons License CC BY 3.0: Allowed to copy, redistribute remix & transform But must attribute 2 Many thanks to all at KR&R@VU: Wouter Beek, Joe Raad, Peter Bloem, Stefan Schlobach, Zhisheng Huang, and many others over the years
- 5. OWL Semantics fits on one A4
- 6. OWL Semantics fits on one A4 • The world consists of – Objects (“individuals”) – Sets of objects (“types”) – Pairs of objects (“relations”) • The world can be described by operations of these sets: 𝑇1 ∪ 𝑇2, 𝑇1 ∩ 𝑇2, 𝑇1 T2
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- 10. So we need an observational instrument
- 12. LOD Laundromat Beek & Rietveld et al. 2014, LOD laundromat: a uniform way of publishing other people's dirty data http://lodlaundromat.org/pdf/lodla undry.pdf HDT Fernández & Martínez-Prieto & Gutiérrez, 2013, Binary RDF representation for publication and exchange (HDT) LDF Verborgh & Vander Sande et al. 2014, Web-Scale Querying through Linked Data Fragments
- 13. LOD-a-lot 1 file 28,362,198,927 unique triples >650K data documents LDF queries in real time Surprisingly efficient 524 GB of disk space 16 GB of RAM Only 144 secs loading time Only €305,- hardware cost Meta-Data for a lot of LOD http://www.semantic-web-journal.net/content/meta-data-lot-lod-2 http://lod-a-lot.lod.labs.vu.nl/
- 14. Insights from Empirical Semantics: 1. Identity correctness 14 Joe Raad Wouter Beek
- 15. owl:sameAs is not optional 15 But in practice it’s broken under the formal semantics
- 16. Meet our observatory: http://SameAs.cc • 559 million owl:sameAs statements (we created an HDT file in 4 hours on 1 CPU core) = 4.5GB + 2.2GB index) • 50 million equivalence classes after inference (5 hours on 2CPU cores; 9.3Gb disk only(!) RocksDB) 16
- 17. The largest equivalence class has 177.749 entities and contains: • Albert Einstein • all countries of the world • the empty string Formal Semantics says: This is obviously broken…. 17 Refl: ∀𝑥: (𝑥 = 𝑥) Symm: ∀𝑥, 𝑦: (𝑥 = 𝑦) → (𝑦 = 𝑥) Trans: ∀𝑥, 𝑦, 𝑧: 𝑥 = 𝑦 ∧ 𝑦 = 𝑧 → (𝑥 = 𝑧)
- 18. Oldest known knowledge graph (Pssss, this is not a new problem…) 18 FatherSon Holy Spirit
- 19. A modern example: Barak Obama
- 20. A modern example: Barak Obama
- 21. Community 0 1. dbpedia.org/resource/B_hussein_obama 2. dbpedia.org/resource/Barack_H_Obama,_Jr 3. dbpedia.org/resource/Barak_hussein_obama 4. dbpedia.org/resource/President_Barack 5. dbpedia.org/resource/Senator_Barack_Obama 6. dbpedia.org/resource/Obama … 99. dbpedia.org/resource/Hussein_Obama Community 3 1. dbpedia.org/resource/Presidency_of_Barack_Obama 2. dbpedia.org/resource/Barack_Obama_Administration 3. dbpedia.org/resource/Barack_Obama_Cabinet 4. dbpedia.org/resource/Obama_White_House 5. dbpedia.org/resource/Obama_regime 6. dbpedia.org/resource/America_under_Obama … 52. dbpedia.org/resource/Presidential_transition_of_Barac k_Obama Debugging identity by community detection Communities correspond to roles: - Person - Senator - President - Government
- 22. Message from Empirical Semantics It’s not the users that got owl:sameAs wrong, It’s the formal semantics that got reality wrong Challenge: What alternative semantic model of equality would fit the empirically observed usage better?
- 23. Insights from Empirical Semantics: 2. Meaningful names 23 Steven de Rooij Peter Bloem Wouter Beek (ISWC 2016) http://www.cs.vu.nl/~frankh/postscript/ISWC2016.pdf
- 24. Symbols or words? (or: blasphemy for logicians) Formal Semantics says: Symbol names are supposed to be meaningless Aspirin headache analgesic pain symptomdrug treats treats
- 25. Measure mutual information content between URL-string and semantics E(x) = efficient encoding of x, If x y then E(x+y) E(x) else E(x+y) E(x)+E(y) Mutual information content M(x,y) =E(x) + E(y) – E(x+y) Take x = symbol name of x as a string Take 𝑦1 = types of x (≈ semantics of x) Take 𝑦2 = properties of x (≈ semantics of x) Calculate M(x, 𝑦1) and M(x, 𝑦2) for all symbols in 600k datasets
- 26. But URL-strings do encode meaning! Fraction of datasets with redundancy for types/predicates at significance level > 0.99 BTW, this is 600.000 datapoints (RDF docs) Properties Types
- 27. Message from Empirical Semantics Users shouldn’t stop using meaningful names, Formal semantics should capture their meaning Challenge: What alternative semantic models could capture meaningful names?
- 28. Zhisheng Huang (ISWC 2008) Insights from Empirical Semantics: 3. Meaningful names for local consistency
- 29. Knowledge will be inconsistent Because of: • Homonyms • Different ontological models • migration from legacy data • integration of multiple sources • ….
- 30. Inconsistency through migration DICE terminology, in daily use at Amsterdam Medical Centre for registration of Intensive Care patients • Brain CentralNervousSystem • Brain BodyPart • CentralNervousSystem NervousSystem • BodyPart NervousSystem
- 31. Inconsistency through automated learning • Reservoir Lake • Lake WaterRegion • Reservoir HydrographicStructure • HydrographicStrure Facility • Disjoint(WaterRegion, Facility), 100% expert agreement on this disjointness…. Inconsistency through merging SUMO(1000) + CYC(1.6M) → 6000 inconsistencies…
- 32. Local consistency s(T,,2)s(T,,0)s(T,,1) But… how to define s(T,𝜙,n)?
- 33. Symbols as words Waterregion basin Lake Reservoir H. structure Facility Google Distance (symbols as words!)
- 34. Reservoir Lake Lake WaterRegion Reservoir HydrographicStructure HydrographicStrure Facility Disjoint(WaterRegion, Facility) Google Distance for selection function in local consistency reasoning ISWC08 Formal Semantics says: this isn’t supposed to work!
- 35. Insight from Empirical Semantics Users shouldn’t stop using meaningful names, Formal semantics should capture their meaning Challenge: What alternative semantic models would capture meaningful names?
- 36. Challenge for Empirical Semantics: 4. network structures for different predicates Tobias Kuhn Wouter Beek http://ceur-ws.org/Vol-1946/paper-05.pdf
- 37. skos:exactMatch
- 38. foaf:knows
- 39. osspr:contains
- 41. Message from Empirical Semantics None of these patterns have any semantic impact (you can’t even detect them under the traditional semantics) Challenge: What alternative semantic models would take such different patterns into account?
- 42. So what…
- 43. So what #1 (pragmatic) • We now have larger KB’s than ever before • We now have the instruments to observe and analyse these very large KB’s • We can use these insights for better tools: – query & inference – publish & maintain – visualise & explain – …
- 44. My secret hope is that this will help us to understand the patterns of knowledge: Not a prescriptive theory of what knowledge should be, But a descriptive theory of what knowledge is actually like So what #2 (pretentious)