Improving recall for conjunctive queries on NLP graphs

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Part I of the talk I gave at Columbia University, 11 Oct 2012

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Improving recall for conjunctive queries on NLP graphs

  1. 1. “30 are better than one” Improving recall for conjunctive queries on NLP graphs Chris Welty, Ken Barker, Lora Aroyo, Shilpa Arora Tex Text t Answering Conjunctive SPARQL Queries over NLP Graphs (c)Lora Warhol Andy AroyoWednesday, October 17, 12 1
  2. 2. Goal: hypothesis generation & validation framework for NLP Graphs Hypothesis: within this framework, there is value in the secondary extraction graph for conjunctive query answering the probability of a secondary graph statement being correct increases significantly when that statement generates a new result to a conjunctive query over the primary graph Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 2
  3. 3. Machine Reading ProgramWednesday, October 17, 12 3
  4. 4. The MRP Vision to decrease the cost of maintaining critical system DBs can we replace the human without changing the LSW can we build a machine reader for this DB SME W yS Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 4
  5. 5. The MRP Vision to decrease the cost of maintaining critical system DBs can we replace the human without changing the LSW can we build a machine reader for this DB SME W yS query Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 4
  6. 6. The MRP Vision to decrease the cost of maintaining critical system DBs can we replace the human without changing the LSW can we build a machine reader for this Machine Reader! W yS query Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 4
  7. 7. SRI Answer to the Vision replacing the human, but still with a DB NLP components must make their best guess, without any knowledge of the specific task at hand, e.g. the query DB SME W yS query Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 5
  8. 8. SRI Answer to the Vision replacing the human, but still with a DB NLP components must make their best guess, without any knowledge of the specific task at hand, e.g. the query NLP Stack DB W yS query Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 5
  9. 9. SRI Answer to the Vision replacing the human, but still with a DB NLP components must make their best guess, without any knowledge of the specific task at hand, e.g. the query NLP Stack DB W Machine yS query Legac Reader! Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 5
  10. 10. The MRP Vision to decrease the cost of maintaining critical system DBs can we replace the human without changing the LSW can we build a machine reader for this DB SME W query yS Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 6
  11. 11. The MRP Vision to decrease the cost of maintaining critical system DBs can we replace the human without changing the LSW can we build a machine reader for this Machine DB SME W query Reader! yS Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 6
  12. 12. The MRP Vision the NLP process is not a one-shot deal the query provides context for what the user is seeking and thus an opportunity to re-interpret the text NLP NLP Graphs Stack Machine DB SME W query Reader! re-interpret yS Legac Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 6
  13. 13. NLP Stack • Contains NER, CoRef, RelEx, entity disambiguation • RelEx: SVM learner with output score: probabilities/ confidences for each known relation that the sentence expresses it between each pair of mentions • Run over target corpus producing NLP graph • nodes are entities (clusters of mentions produced by coref) • edges are type statements between entities and classes in the ontology, or relations detected between mentions of these entities in the corpus Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 7
  14. 14. RDF for NLP • use SemTech to influence the NLP stack vs. NLP components to only feed the knowledge integration layer • to store the results of IE in RDF Graphs (NLP Graphs), where: • each triple has a confidence of the NLP components and provenance indicating where the triple was stated in natural language text • triple - not an expression of truth, but a representation of what an NLP component, or a human annotator, read in a document • confidence - not that the triple is true, but reflects the confidence that the text states the triple (component level confidence) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 8
  15. 15. “... Mr. X of India ...” “... in countries like, India, Iran, Iraq ...” Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 9
  16. 16. Person GPE “... Mr. X of India ...” Mr. X India citizenOf NLP Stack sameAs GPE Country “... in countries like, India, Iran, Iraq ...” India Iran Iraq subPlaceOf Evidence Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 9
  17. 17. Mr. X rdf:type rdf:type citizenOf India Country India Person GPE rdf:type subPlaceOf rdf:type Iran Iraq rdf:subClassOf NLP Graph RDF Graph The nodes & arcs refer to the results of NLP, not “truth” There is error (precision, recall) There is confidence associated with each triple Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 9
  18. 18. NLP Stack produces • two NLP graphs • primary graph = the single best type, relation & coreference results for each mention or mention pair • secondary graph = all possibilities considered by the NLP stack Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 10
  19. 19. SPARQL Queries on NLP Graphs 19-Sept-2012 Hypothesis Generation for Answering Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 11
  20. 20. Conjunctive Query Find Jordanian citizens who are members of Hezbollah SELECT ?p WHERE { ?p mric:citizenOf geo:Jordan . mric:Hezbollah mric:hasMember ?p . find all bindings for the variable ?p that satisfy the query report where in the target corpus the answers were found (spans of text expressing the relations in the query) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 12
  21. 21. Conjunctive Queries Recall n • [Π Recall(R ) ] x Recall k=1 k coref • for conjunctive query of n terms recall could be O(Recalln) • for complex queries Recall becomes dominating factor, where the overall Recall gets severely degraded by term Recall • in our experiments: query recall <.1 for n>3 • all NLP components had to work correctly to get an answer Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 13
  22. 22. ... solution? • find solutions to subsets of a conjunctive SPARQL query as candidate solutions to the full query • attempt to confirm the candidate solutions using various kinds of inference, external resources & secondary extraction results Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 14
  23. 23. ... in other words hypothesis generation that focuses on parts of an NLP graph that almost match a query, identifying statements that if proven would generate new query solutions we are looking for missing links in a graph that, if added, would result in a new query solution 19-Sept-2012 Hypothesis Generation for Answering Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 15
  24. 24. Q: R1(x,y) R2(x,z) R3(z,w) R3 R1 R3? R2 R3? R3? so, each hypothesis set if added to the primary NLP graph would provide a new answer to the original query only validated hypotheses are added to the query result Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 16
  25. 25. Hypothesis Generation • Relaxes queries of size N by removing query terms Q • Finds solutions to the remaining set of terms • for each solution bind the variables in Q forming a hypothesis • If no solutions to subqueries of size N-1 are found, then N-2 • appropriate for queries that are almost answerable, e.g. when most of the terms in query are not missing • biased towards generating more answers to queries, e.g. perform poorly on queries for which the corpus does not contain the answer Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 17
  26. 26. find all terrorist organizations that were agents of bombings in Lebanon on October 23, 1983: SELECT ?t WHERE { ?t rdf:type mric:TerroristOrganization . ?b rdf:type mric:Bombing . ?b mric:mediatingAgent ?t . ?b mric:eventLocation mric:Lebanon . ?b mric:eventDate "1983-10-23" . } Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 18
  27. 27. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 19
  28. 28. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all bombings in Lebanon on 1983-10-23 with agents 1 (hypothesize that the agents are terrorist organizations) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 19
  29. 29. mric:bombing rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all bombings in Lebanon on 1983-10-23 with agents 1 (hypothesize that the agents are terrorist organizations) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 19
  30. 30. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all events in Lebanon on 1983-10-23 by terrorist orgs 2 (hypothesize that the events are bombings) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 20
  31. 31. mric:TerroristOrganization rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all events in Lebanon on 1983-10-23 by terrorist orgs 2 (hypothesize that the events are bombings) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 20
  32. 32. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all bombings in Lebanon on 1983-10-23 3 (all known terrorist organizations are hypothetical agents) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 21
  33. 33. mric:bombing mric:TerroristOrganization rdf:type rdf:type t b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all bombings in Lebanon on 1983-10-23 3 (all known terrorist organizations are hypothetical agents) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 21
  34. 34. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all bombings by terrorist orgs on 1983-10-23 4 (hypothesize that the bombings were in Lebanon) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 22
  35. 35. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventDate 1983-10-23 find all bombings by terrorist orgs on 1983-10-23 4 (hypothesize that the bombings were in Lebanon) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 22
  36. 36. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 find all bombings by terrorist orgs in Lebanon (hypothesize that the bombings were on 1983-10-23) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 23
  37. 37. mric:bombing mric:TerroristOrganization rdf:type rdf:type t mric:mediatingAgent b mric:eventLocation mric:Lebanon find all bombings by terrorist orgs in Lebanon (hypothesize that the bombings were on 1983-10-23) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 23
  38. 38. mric:bombing mric:TerroristOrganization rdf:type find all bombings by terrorist orgs in Lebanon rdf:type (hypothesize that the bombing1 was on t mric:mediatingAgent b 1983-10-23) mric:eventLocation mric:Lebanon Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 24
  39. 39. mric:bombing mric:TerroristOrganization rdf:type find all bombings by terrorist orgs in Lebanon rdf:type (hypothesize that the bombing1 was on t mric:mediatingAgent b 1983-10-23) mric:eventLocation mric:Lebanon mric:bombing mric:TerroristOrganization rdf:type rdf:type racr:orgs65 t mric:mediatingAgent racr: bombing1 b mric:eventLocation mric:Lebanon Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 24
  40. 40. mric:bombing mric:TerroristOrganization rdf:type find all bombings by terrorist orgs in Lebanon rdf:type (hypothesize that the bombing1 was on t mric:mediatingAgent b 1983-10-23) mric:eventLocation mric:Lebanon mric:bombing mric:TerroristOrganization rdf:type rdf:type racr:orgs65 t mric:mediatingAgent racr: bombing1 b mric:eventLocation mric:eventDate mric:Lebanon 1983-10-23 Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 24
  41. 41. Hypothesis Validation • a stack of hypothesis checkers: (1) report confidence whether a hypothesis holds and (2) provide provenance: a pointer to a span of text that supports the hypothesis • to limit complex computational tasks, e.g. formal reasoning or choosing between multiple low- confidence extractions • such tasks are made more tractable by using hypotheses as goals, e.g. a reasoner may be used effectively by constraining to only a part of the graph connected to a hypothesis Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 25
  42. 42. Hypothesis Checkers • knowledge base (previous work) • taxonomic inference & complex rules • rules derived directly from the ontology • general, domain-independent rules, e.g. family relationships, and geo knowledge • TyCor (previous work) • secondary extraction graph (new work) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 26
  43. 43. Rules Derived from Ontology • simple superclass-subclass rules (Bombing (?x) → Attack (?x)) • simple relation-subrelation rules (hasSon (?x, ?y) → hasChild (?x, ?y))  • simple relation inverse rules (hasChild (?x,?y) hasParent (?y,?x)) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 27
  44. 44. Complex Rules from Ontology • 40 complex rules based on specialization of the domain or range of sub-relations (hasSubGroup (?x, ?y) & HumanOrganization (?x)   → hasSubOrganization (?x, ?y)) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 28
  45. 45. Core Claim: Secondary Graph is a productive source for hypothesis validation in conjunction with the primary graph to answer a query 19-Sept-2012 Hypothesis Generation for Answering Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 29
  46. 46. Secondary Graph • an NLP Graph generated from *all* the interpretations considered by the NLP stack, so obviously quite large • multiple mentions, mention types, multiple entities, multiple entity types & multiple relations between them • pruned at a particular confidence threshold Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 30
  47. 47. Experimental Setup testing the ideas 19-Sept-2012 Hypothesis Generation for Answering Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 31
  48. 48. Initial MRP Setup find mentions of the ontology types & relations in the corpus & extract them into an RDF Graph • OWL target ontology: types & binary relations • 10-50K documents - Gigaword (sub)corpus • 79 docs manually annotated (mentions of the target relations & their argument types) • 50 SPARQL queries (expected to be answered in NLP Graph) • query results evaluated manually • each query has at least one correct answer in the corpus • some queries have over 1000 correct answers Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 32
  49. 49. Initial MRP Evaluation • required extensive manual effort: • no match between system node IDs and GS node IDs • provenance for evaluators to find mentions from a graph • evaluators semi-automatically map the system result entity IDs to GS entity IDs • expensive, error-prone & difficult to reproduce ... • difficult to test systems adequately before the evaluation • only 50 queries were used - not enough for significant system validation, e.g. not able to tune system thresholds Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 33
  50. 50. How did we change this? • we decided to sacrifice corpus size in favor of having entity IDs (eliminating the manual step in the evaluation) • we created a gold standard corpus • 169 docs manually annotated with types, relations, coreference and entity names • generated Gold-Standard NLP graph from manually annotated data • automatically generated SPARQL queries from GS graph • we ran only the RelEx component using GS mentions, types & coref giving us the GS entity IDs in the system graph measure performance of system results against these GS results Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 34
  51. 51. Evaluation & Test Data • 60 train, 60 devtest & 49 final (blind) test • manually annotated with NER, coref, relations • extracted from Gigaword • split to balance distribution of 48 domain relations • generated Gold-Standard NLP graph from manually annotated data • RelEx component trained & applied using GS mentions, types & coref • increases the F-measure (F=.28) of the RelEx output, but used in the baseline and in the test experiments so it doesn’t affect the results Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 35
  52. 52. SPARQL Evaluation Queries • 475 test queries for the devtest set and 492 for test. • generated from the GS NLP graph for each document set by: • extracting random connected subsets of the graph containing 2-8 domain relations (not including rdf:type) • adding type statements for each node • replacing nodes that had no proper names in text with select variables • run the query over the same GS graph and the results became our gold standard results for query evaluation (since they had variables the results would be different than what we started with) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 36
  53. 53. NLP Graphs from RelEx Output • RelEx: a set of SVM binary classifiers, one per relation • for each sentence in the corpus, for each pair of mentions in that sentence, for each relation it produces a probability that that pair is related by the relation • NLP graphs are generated by selecting relations from RelEx output in two ways: • Primary: takes only the top scoring relation between any mention pair above a confidence threshold (0, .1 and .2) • Secondary: takes all relations between all mention pairs above 0 confidence • All type triples come from the Gold Standard (GS) • Precision & Recall are determined by automatically comparing system query results to the GS query results (for every query we know all the answers) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 37
  54. 54. Threshold Choices • Threshold .2 --> max F1=.28 on devset for RelEx • Threshold .1 --> guessed threshold before having any data to back it up • we could have tried more thresholds but it was a lot of work • in our experiments, we explored threshold space over hundreds of queries - satisfactory to tune the threshold parameters Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 38
  55. 55. Graph Notation • We refer to the graphs by document set (dev or test) and top/ all @threshold, e.g. • devTop@.2 = NLP Graph on dev set using top relations above .2 confidence • testAll@0 = NLP graph on test set using all  relations above 0 confidence • 3 primary graphs, in all cases using top, and selecting relations at thresholds 0, .1, and .2  • 1 secondary graph using the all@0 setting (R=.97) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 39
  56. 56. This Evaluation Setup allows to run experiments repeatedly over hundreds of queries with no manual intervention 19-Sept-2012 Hypothesis Generation for Answering Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 40
  57. 57. 6 Experiments • 3 for dev, 3 for test • each experiment compares query results from only PG to query results using the PG+SG for hypothesis validation • the three experiments compare performance at different primary graph thresholds Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 41
  58. 58. 0-threshold primary graph with & without secondary graphF1 secondary graph: all@0 for a given PG threshold we vary the SG threshold for validated hypotheses (x-axis) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 42
  59. 59. .1-threshold primary graph with & without secondary graphF1 secondary graph: all@0 the red line indicates the PG threshold - the PG-only flattens below this threshold as expected Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 43
  60. 60. .1-threshold primary graph with & without secondary graphF1 secondary graph: all@0 best performance point (.01 SG threshold) the red line indicates the PG threshold - the PG-only flattens below this threshold as expected Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 43
  61. 61. .2-threshold primary graph with & without secondary graphF1 secondary graph: all@0 the best performing configuration for dev is .2 threshold PG with SG hypotheses validated at .01 threshold Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 44
  62. 62. .2-threshold primary graph with & without secondary graphF1 secondary graph: all@0 best performance point (.01 SG threshold) the best performing configuration for dev is .2 threshold PG with SG hypotheses validated at .01 threshold Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 44
  63. 63. Performance Text the test set was truly blind, we ran it only once R - expected, F - hoped, P - surprised the probability of a relation holding between two mentions increases significantly if that relation would complete a conjunctive query result Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 45
  64. 64. Performance Text the test set was truly blind, we ran it only once R - expected, F - hoped, P - surprised the probability of a relation holding between two mentions increases significantly if that relation would complete a conjunctive query result Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 45
  65. 65. Example: Generated Query Q161: "Find events in which the leader of Venezuela is the mediating agent" ?e1 mric:MediatingAgent ?p1 geo:Venezuela mric:isLedBy ?p1 geo:Venezuela rdf:type mric:GeopoliticalEntity ?p1 rdf:type mric:Person ?e1 rdf:type mric:Event Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 46
  66. 66. Example: Generated Query Q161: "Find events in which the leader of Venezuela is the mediating agent" ?e1 mric:MediatingAgent ?p1 geo:Venezuela mric:isLedBy ?p1 geo:Venezuela rdf:type mric:GeopoliticalEntity ?p1 rdf:type mric:Person ?e1 rdf:type mric:Event no solutions in PG Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 46
  67. 67. Example: Generated Query Q161: "Find events in which the leader of Venezuela is the mediating agent" ?e1 mric:MediatingAgent ?p1 geo:Venezuela mric:isLedBy ?p1 geo:Venezuela rdf:type mric:GeopoliticalEntity ?p1 rdf:type mric:Person ?e1 rdf:type mric:Event find binding for p1 (346) Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 46
  68. 68. Example: Generated Query Q161: "Find events in which the leader of Venezuela is the mediating agent" ?e1 mric:MediatingAgent ?p1 geo:Venezuela mric:isLedBy ?p1 geo:Venezuela rdf:type mric:GeopoliticalEntity ?p1 rdf:type mric:Person ?e1 rdf:type mric:Event generates 346 hypotheses finds support in SG for isLedBy("Venezuela", "Hugo Chavez") Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 46
  69. 69. Questions? @laroyo http://lora-aroyo.org Answering Conjunctive SPARQL Queries over NLP Graphs Lora AroyoWednesday, October 17, 12 47

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