A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources
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1 A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources Arnab Dutta, Christian Meilicke, Simone Ponzetto
2 Which Superman exactly? Brandon Routh was an actor in Superman
3 Uncertainty in the Web Underspecified terms Which superman? Absence of clear semantics Is superman always the 1983 movie in all triples it occurs?
4 Information Extraction (IE) Projects Closed (e.g. DBpedia, Yago) maintain a well-defined schema URI used to uniquely identify an entity x Restricted to Wikipedia mostly Open (e.g. Nell, ReVerb) work on large sets of unstructured data x necessarily do not maintain a schema domain independent scale easily to web corpus
5 NELL can someone semantify me please? I can offer new facts that I have learnt from the entire Web I can help you. You can use my ontology. But enrich me, I am just restricted to Wikipedia DBpedia
6 Approach Baseline Most frequent sense based approach Probabilistic exploits additional information to make intelligent choices
7 Approach: Baseline Exploit Wikipedia Corpus (used Wikiprep) Anchors link to pages, often multiple ones Anchors analogous to NELL terms
8 Approach: Baseline Exploit Wikipedia Corpus (used Wikiprep) Anchors link to pages, often multiple ones Anchors analogous to NELL terms
9 Approach: Baseline Exploit Wikipedia Corpus (used Wikiprep) Anchors link to pages, often multiple ones Anchors analogous to NELL terms
10 Approach: Baseline (contd.) Compute probability from the anchor link counts to pages Get top-k candidates from the distribution Page Title Link count Superman 4133 Superman (film) 521 Superman (comic book) 491
11 Baseline drawbacks Not exploiting the context Most frequent sense is not always the intended reference
12 Approach: Probabilistic define a set of matching hypotheses exploit the ontology of DBpedia penalize the wrong hypotheses if it leads to any inconsistency
13 Big Picture Brandon routh actorstarredinmovie superman
14 Big Picture Brandon routh actorstarredinmovie superman db:brandon_routh db:superman Db:Superman_(comic_book) db:superman_(film)
15 Big Picture Brandon routh actorstarredinmovie superman db:brandon_routh db:superman Db:Superman_(comic_book) db:superman_(film)
16 Big Picture Brandon routh actorstarredinmovie superman db:brandon_routh db:superman Db:Superman_(comic_book) db:superman_(film)
17 Methodology I. Probabilistic type generation range(actorstarredinmovie, Person, w 1 ) range(actorstarredinmovie, Artist, w 2 ) range(actorstarredinmovie, Athlete, w 3 ) II. Formulating as an inference task in a Markov Network A state which is most likely to hold
18 I. Probabilistic Type Generation
19 I. Probabilistic Type Generation Scan through all the NELL triples <, actorstarredinmovie, > <brandon routh, actorstarredinmovie, superman>
20 I. Probabilistic Type Generation Scan through all the NELL triples <, actorstarredinmovie, > <brandon routh, actorstarredinmovie, superman> Get the top-1 references of the instances db:brandon_routh db:superman
21 I. Probabilistic Type Generation Scan through all the NELL triples <, actorstarredinmovie, > <brandon routh, actorstarredinmovie, superman> Get the top-1 references of the instances db:brandon_routh db:superman Get the direct types for them dbo:person dbo:fictionalcharacter
22 Tree Generation Work (100) Direct type counts Cartoon (20) Film (40) WrittenWork (80) Book (30) Play (20) Comics (10) Selection Preference: Film > Book > Play = Cartoon >..
23 Node Scoring Up-score V u (n)= V o (n) + α * Σ V u (n) child of n Down-score V d (n)= V d (parent(n)) + (1-α) V u (n)
24 Tree Generation (α-tree) Person (100) V d Cartoon (20) Film (40) WrittenWork (80) Book (30) Play (20) Comics (10)
25 Tree Generation (α-tree) Person (100, (100) 185) V u V d Cartoon (20, (20) 20) Film (40, (40) 40) WrittenWork (80, (80) 110) Book (30, (30) 30) Play (20, (20) 20) Comics (10, (10) 10)
26 Tree Generation (α-tree) Person Person (100, (100) 185) (100, 185, 185) V u V d Cartoon Cartoon (20, (20) 20) (20, 20, 195) Film Film (40, (40) 40) (40, 40, 205) WrittenWork WrittenWork (80, (80) 110) (80, 110, 240) Book Book (30, (30) 30) (30, 30, 255) Play Play (20, (20) 20) (20, 20, 250) Comics Comics (10, (10) 10) (10, 10, 245) Selection Preference: Book > Play > Comics >..
27 II. Formulate as a Markov Logic Network
28 II. Formulate as a Markov Logic Network Add weighted/un-weighted first order rules Un-weighted sameas(a,b) Λ sameas(b, c) => sameas(a, c) x sameas(x, y) <= 1 weighted w: nelltriple(s, prop, o) Λ sameasconf(o, db:instance, conf) Λ istype(db:instance, db:class) => sameas(o, db:instance)
29 II. Formulate as a Markov Logic Network Add weighted/un-weighted first order rules Un-weighted sameas(a,b) Λ sameas(b, c) => sameas(a, c) x sameas(x, y) <= 1 weighted w: nelltriple(s, prop, o) Λ sameasconf(o, db:instance, conf) Λ istype(db:instance, db:class) => sameas(o, db:instance) Add soft truths as observed (evidences) sameasconf(superman, db:superman, w1) sameasconf (superman, db:superman_(film), w2) sameasconf (superman, db:superman_(comic_book), w3) and many more. Add hard truths istype(db:superman, ComicsCharacter) istype(db:superman_(film), Film)
30 II. Formulate as a Markov Logic Network Add weighted/un-weighted first order rules Un-weighted sameas(a,b) Λ sameas(b, c) => sameas(a, c) x sameas(x, y) <= 1 weighted w: nelltriple(s, prop, o) Λ sameasconf(o, db:instance, conf) Λ istype(db:instance, db:class) => sameas(o, db:instance) Add soft truths as observed (evidences) sameasconf(superman, db:superman, w1) sameasconf (superman, db:superman_(film), w2) sameasconf (superman, db:superman_(comic_book), w3) and many more. Add hard truths istype(db:superman, ComicsCharacter) istype(db:superman_(film), Film) Ask for the most likely state of the network MAP query (RockIt system used as the engine)
31 Grounding the Markov Network
32 Grounding the Markov Network istype(dbinst,c) sameasconf(o, dbinst, conf) nelltriple(s, p, o)
33 Grounding the Markov Network istype(dbinst,c) sameasconf(o, dbinst, conf) nelltriple(s, p, o) W 11 : istype(db:superman_(film), Film) Λ nelltriple(s, p, superman) Λ sameasconf(superman, db:superman_(film), w2) => sameas(superman, db:superman_(film))
34 Grounding the Markov Network istype(dbinst,c) sameasconf(o, dbinst, conf) nelltriple(s, p, o) W 11 : istype(db:superman_(film), Film) Λ nelltriple(s, p, superman) Λ sameasconf(superman, db:superman_(film), w2) => sameas(superman, db:superman_(film)) W 12 : istype(db:superman_(film), WrittenWork) Λ nelltriple(s, p, superman) ) Λ sameasconf(superman, db:superman_(film), w2) => sameas (superman, db:superman_(film))
35 Grounding the Markov Network istype(dbinst,c) sameasconf(o, dbinst, conf) nelltriple(s, p, o) W 11 : istype(db:superman_(film), Film) Λ nelltriple(s, p, superman) Λ sameasconf(superman, db:superman_(film), w2) => sameas(superman, db:superman_(film)) W 12 : istype(db:superman_(film), WrittenWork) Λ nelltriple(s, p, superman) ) Λ sameasconf(superman, db:superman_(film), w2) => sameas (superman, db:superman_(film))... and many many more such instantiations
36 Grounding the Markov Network istype(dbinst,c) sameasconf(o, dbinst, conf) nelltriple(s, p, o) W 11 : istype(db:superman_(film), Film) Λ nelltriple(s, p, superman) Λ sameasconf(superman, db:superman_(film), w2) => sameas(superman, db:superman_(film)) W 12 : istype(db:superman_(film), WrittenWork) Λ nelltriple(s, p, superman) ) Λ sameasconf(superman, db:superman_(film), w2) => sameas (superman, db:superman_(film))... and many many more such instantiations
37 Can we still do better? If the output is a refinement, can t we use it again to generate type weights again?
38 Bootstrapping Take all the sameas hypotheses Includes the correct and incorrect mappings Use the top-1 mapping to create type weights Assumption is the most frequent mapping is correct Add the sameas and the type weights to compete Result is a better mapping set (refined than top-1) Use it again to get the type weights.
39 Bootstrapping in action
40 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3)
41 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Type weights from top-1 only
42 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Type Weights Type weights from top-1 only 2.3: Work -1.1: WrittenWork -2.2: Film -3.9: FictionalCharacter
43 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Type Weights Type weights from top-1 only 2.3: Work -1.1: WrittenWork -2.2: Film -3.9: FictionalCharacter Refined mappings
44 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Type Weights Type weights from top-1 only 2.3: Work -1.1: WrittenWork -2.2: Film -3.9: FictionalCharacter No suitable mappings Refined mappings
45 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Feed again Type weights from top-1 only Type Weights 2.3: Work -1.1: WrittenWork -2.2: Film -3.9: FictionalCharacter No suitable mappings Refined mappings
46 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Feed again Type weights from top-1 only Type Weights 2.3: Work -1.1: WrittenWork -2.2: Film -3.9: FictionalCharacter No suitable mappings Refined mappings
47 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Feed again Type weights from refined maps only Type Weights 2.3: Work -1.1: WrittenWork -2.2: Film -3.9: FictionalCharacter Refined mappings No suitable mappings
48 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Feed again Type weights from refined maps only Type Weights 2.3: Type WorkWeights recomputed -1.1: WrittenWork -2.2: 3.5: Film Film 1.0: Work -3.9: FictionalCharacter -2.3: Politician Refined mappings No suitable mappings
49 Bootstrapping in action All same as hypotheses sameas(superman, db:superman, 1.9) sameas(superman, db:superman_(film), 1.3) Feed again Type weights from refined maps only Type Weights 2.3: Type WorkWeights recomputed -1.1: WrittenWork -2.2: 3.5: Film Film 1.0: Work -3.9: FictionalCharacter -2.3: Politician Refined mappings No suitable mappings sameas(superman, db:superman_(film))
50 Experiments Nell data set consisting of 100 triples for 12 randomly chosen properties Manually create a gold standard Learn the parameter alpha (α)
51 Learning α Divide α in steps of between [0, 1] Large number of samples for 6 properties, D train Get the maximum α on the D train Apply the α on the rest of the 6 properties, D test 35% case α = % case α = % cases α = 0.625
52 Variation of scores with α
53 Comparative Values (α = 0.5)
54 Comparative Values (α = 0.5)
55 Limitations Unable to disambiguate instances with similar types. e.g. Mel Gibson and William Gibson, both of type dbo:person Missing DBpedia type information
56 Looking ahead Mapping of properties as well Potential scope of generating new knowledge Apply on ReVerb data set as well
57 Thank You
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