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Encoding Extraction as Inferences

Encoding Extraction as Inferences. J. William Murdock 1 , Paulo Pinheiro da Silva 2 , David Ferrucci 1 , Christopher Welty 1 , Deborah McGuinness 2. 1 IBM Watson Research Center 19 Skyline Drive Hawthorne, NY 10532, USA {murdockj,ferrucci,welty}@us.ibm.com. 2 Knowledge Systems Laboratory

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Encoding Extraction as Inferences

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  1. Encoding Extraction as Inferences J. William Murdock1, Paulo Pinheiro da Silva2, David Ferrucci1, Christopher Welty1,Deborah McGuinness2 1IBM Watson Research Center 19 Skyline Drive Hawthorne, NY 10532, USA {murdockj,ferrucci,welty}@us.ibm.com 2Knowledge Systems Laboratory Stanford University Stanford, CA 94305, USA {pp,dlm}@ksl.stanford.edu

  2. Idea • Providing a declarative representation of knowledge extraction processes • To provide browsable explanations • For other metacognition tasks • Checking consistency, determining trust, selecting components, etc. • Reuse, adapt, and integrate existing representation/explanation technology: Inference Web • Reuse, adapt, and integrate existing extraction technology: UIMA

  3. Pre-Existing Inference Web Technology • Enables browsing representations of processes • Requires that systems to describe their processing as inferences • Has been used with theorem-proving technology • Lends itself to formal, inference representation • We are now applying this technique to knowledge extraction • Requires a new perspective: extraction as inference

  4. Pre-Existing Inference Web Component: IW Browser

  5. Pre-Existing UIMA Technology • Architecture and framework for creating, composing, and deploying multi-modal analytics • UIMA provides a declarative model of structure, requirements, of analytic components. • UIMA provides shared programming interfaces and knowledge structures for analytics • Because components share interface and structures, one can provide generic tools that interact with components • E.g., a tool to record analysis tasks as inferences

  6. Pre-Existing UIMA Tooling: Semantic Analysis Workbench

  7. Formalizing UIMA Analytics for IW • A taxonomy of extraction tasks expressed as inference rules • A language for expressing the conclusions of a given extraction inference rule • Components that record extraction traces using tasks in the taxonomy • Client side software iterates through internal extraction results and infers what processes were performed • Server side registrar produces formally represented semantic web nodes

  8. Relation:OwnerOf(e1, e2) Entity: e1 Entity: e2 Taxonomy (1): Extraction 1) Entity Recognition:A span refers to some entity of a specified type 2) Relation Recognition:A span refers to some relation of a specified type 3) Relation Annotation Argument Identification:An annotation fills a role in a relation annotation 4) Entity Identification:An entity annotation refers to a particular entity Extraction Results 5) Relation Identification:A relation annotation refers to a particular relation 6) Extracted Entity Classification:An entity has a particular type Recorded Trace Entity: e1 (Person) Entity: e2 (Organization) ER ER RR ER OwnerOf(Relation Annotation) RAAI RAAI EI EI RI Organization(Entity Annotation) Person(Entity Annotation) Person(Entity Annotation) EEC EEC Thomas Gradgrind is the owner of Gradgrind Foods. He lives in New York City.

  9. Relation:<HasOwner #e2 #e1> Entity: #e1 Entity: #e2 Relation:OwnerOf(e1, e2) Entity: e1 Entity: e2 EM EM TEC TEC Taxonomy (2): Semantic Integration 7) Entity Mapping:An entity encoded in the target ontology is derived from an entity or relation encoded in the type system Integration Results 8) Relation Mapping:A relation in the target ontology is derived from an entity or relation instance in the type system 9) Target Entity Classification:An entity is a member of a class in the target ontology Entity: #e1(Person) Entity: #e2(Company) Recorded Trace ER ER RR ER Entity: e1 (Person) Entity: e2 (Organization) RAAI RAAI EI EI RI OwnerOf(Relation Annotation) EEC EEC RM Organization(Entity Annotation) Person(Entity Annotation) Person(Entity Annotation) Thomas Gradgrind is the owner of Gradgrind Foods. He lives in New York City.

  10. Direct Assertion He lives in New York City. Entity Recognition Entity Recognition IBM EAnnotator IBM ACE-model Annotator Thomas Gradgrind(Person) is the owner of Gradgrind Foods. He(Person) lives in New York City. Entity Identification IBM Cross-Annotator Coreference “Thomas Gradgrind(Person)”,“He(Person)”refer to E1 Extracted Entity Classification IBM Cross-Annotator Coreference (hasClass E1Person) Visualizing the Recorded Trace Direct Assertion Direct Assertion (subClassOfPerson Entity) Thomas Gradgrind is the owner of Gradgrind Foods.

  11. Representing Effects • Internal representations are used to encode the conclusions for the steps in the trace • Source text is encoded in the original natural language • Intermediate results relating to text are encoded using a specialized formalism: Thomas Gradgrind(Person) is the owner of Gradgrind Foods. refersToMemberOf(0-16, com.ibm.hutt.Person) • Final results (extracted knowledge) is represented in KIF. • May want to use KIF for intermediate results too.

  12. Entity Recognition IBM EAnnotator Thomas Gradgrind(Person) is the owner of Gradgrind Foods. Proof Markup Language (Simplified) <rdf:RDF xmlns:iw=http://inferenceweb.stanford.edu/2004/07/iw.owl# xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns# xmlns:owl="http://www.w3.org/2002/07/owl#"> <iw:MethodRule rdf:about="http://(...)/registry/MPR/EntityRecognition.owl#EntityRecognition"/> <iw:InferenceEngine rdf:about="http://(...)/registry/IE/IBMEAnnotator.owl#IBMEAnnotator"/> <iw:NodeSet rdf:about="http://localhost:8080/ieiw/id_2.owl#id_2"> <iw:Language rdf:about="http://(...)/registry/LG/UimaLogic.owl#UimaLogic"/> <iw:conclusion>refersToMemberOf(0-16 , com.ibm.hutt.Person)</iw:conclusion> <iw:isConsequentOf rdf:parseType="Collection"> <iw:InferenceStep iw:hasIndex="0"> <iw:hasAntecedent rdf:parseType="Collection"> <iw:NodeSet rdf:about="http://localhost:8080/ieiw/id_0.owl#id_0"/> </iw:hasAntecedent> </iw:InferenceStep> </iw:isConsequentOf> </iw:NodeSet> </rdf:RDF>

  13. Accessing Traces in the Semantic Analysis Workbench

  14. Future Work: Taxonomy Extensions • Other primitive text extraction tasks • e.g., assigning canonical form and variant forms to entities • Primitive extraction tasks from other modalities • audio, images, video, etc. • Non-primitive extraction tasks • e.g., aggregating entity identification, entity classification, etc. into coreference resolution • More abstract explanations at a non-primitive level, with ability to drill-down to primitive tasks

  15. Future Work: Other Metacognition Applications • Determining trust for extracted knowledge • Inference Web trace connects conclusions to sources via inference steps. • Trust for a conclusion depends on trust of the sources and trust of the inference steps. • Checking consistency • e.g., rejecting entity recognition over a span that does not exist • Automatically selecting extraction components • Based on information about the component in the IW Registry • Based on past IW traces (CBR)

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