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Acquiring and Using World Knowledge using a Restricted Subset of English

Acquiring and Using World Knowledge using a Restricted Subset of English. Peter Clark, Phil Harrison, Tom Jenkins, John Thompson, Rick Wojcik Boeing Phantom Works, Seattle. Introduction. Knowledge acquisition is still a major bottleneck automated methods are good but still very restricted

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Acquiring and Using World Knowledge using a Restricted Subset of English

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  1. Acquiring and Using World Knowledgeusing a Restricted Subset of English Peter Clark, Phil Harrison, Tom Jenkins, John Thompson, Rick Wojcik Boeing Phantom Works, Seattle

  2. Introduction • Knowledge acquisition is still a major bottleneck • automated methods are good but still very restricted • Our approach: • Knowledge entry using Controlled Language • Hits “sweet spot” between logic and full NLP • language interpreter generates logic output • Outline: • Our Controlled Language Processing technology • Discussion on Natural Language as a basis for KR

  3. “A ball falls from a cliff” “xy B(x) R(x,y)C(y)” “Consider the following possible situation in which a ball first…” too hard for the user too hard for the computer to understand TheLanguage Spectrum Unrestricted natural language Formal language Controlled English

  4. Short sentences No pronouns Rewritten in CPL (computer can understand): An object is thrown from a cliff. The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground. The object falls 125 m to the ground. What is the duration of the fall? Simple sentence structures CPL (Computer-Processable Language) Original text (incomprehensible to computer): An object is thrown with a horizontal velocity of 20 m/s from a cliff that is 125 m above level ground. If air resistance is negligible, how long does it take the object to fall to the ground?

  5. isa(_Object1, object_n1) isa(_Cliff2, cliff_n1) isa(_Throw3, throw_v1) object(_Throw3, _Object1) origin(_Throw3, _Cliff2) Throw object origin Object Cliff Target Interpretation • Sentences in first-order logic • Capable of supporting machine inference “An object is thrown from a cliff”

  6. isa(_Person1, person_n1) isa(_Room2, room_n1) isa(_Entity3, entity_n1) isa(_Carry4, carry_v1) object(_Carry4, _Entity3) agent(_Carry4, _Person1) is-inside(_Entity4, _Room2) =====> is-inside(_Person1, _Room2) Carry agent object Person Object is-inside is-inside Room Target Interpretation • Sentences in first-order logic • Capable of supporting machine inference IF “a person is carrying an entity that is inside a room” THEN “the person is in the room.”

  7. Throw object origin Object Cliff Overview of Processing “An object is thrown from a cliff” Parser & LF Generator Word sense disambiguator Linguistic Knowledge Relational disambiguator Coreference identifier World Knowledge Structural reorganizer (_Object13320 instance_of object_n1) (_Cliff13321 instance_of cliff_n1) (_Throw13319 instance_of throw_v1) (_Throw13319 object _Object13320) (_Throw13319 origin _Cliff13321)

  8. Entering Quantified Expressions (Rules) • Seven “rule templates” used: IFsentence THENsentence ABOUTobject: sentence object ISnoun/verb phrase BEFOREsentence, sentence BEFOREsentence, it is not true thatsentence AFTERsentence, sentence AFTERsentence, it is not true thatsentence Processing: • Each sentence processed as a ground assertion • Quantifiers are added (Prolog-style) • “Action” templates become situation calculus rules

  9. CPL (Controlled english) An object is thrown from a cliff. The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground. Rewriting advice Logic An object is thrown from a cliff. The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground. Paraphrase of system’s understanding KB Overall Flow of Processing Original text

  10. Part II: DiscussionControlled Languages:Strengths and challenges

  11. Strengths… xy B(x) R(x,y)C(y)??? “A man is driving a truck towards the factory” • CPL is easy to use, appears viable • built KB with over 1000 rules • KB is • inference-capable • easy to inspect and organize • Makes knowledge entry accessible to many users • major achievement

  12. Original text: “attack: intense adverse criticism” CPL: “IF a person attacks a 2nd person THEN the first person criticizes the 2nd person intensely.” Challenges: 1. Reformulating in a Controlled Language is not trivial • Task is not just grammatical reformulation • Rather: • “natural” English leaves much knowledge implicit • CPL author must make that explicit

  13. Original text: “axis: the center around which something rotates” CPL: “IF an object is rotating THEN the object is turning around the object’s axis.” Challenges: 1. Reformulating in a Controlled Language is not trivial • Task is not just grammatical reformulation • Rather: • “natural” English leaves much knowledge implicit • CPL author must make that explicit

  14. “The man ate the sandwich on the plate” “The man ate on the plate. He ate the sandwich.” ?????? 2. Users may not be aware of system’s mistakes • User must be able to spot misinterpretations easily • System’s paraphrase must be unambiguous • User must know how to correct them

  15. “The man ate the sandwich on the plate” “The man ate on the plate. He ate the sandwich.” “the man ate the sandwich that was on the plate” 2. Users may not be aware of their mistakes • User must be able to spot errors easily • System’s paraphrase must be unambiguous • User must know how to correct them

  16. 3. Natural-Language-based knowledge representations have limited expressivity “Natural language is very expressive” • …not to the computer! (Avoid “wishful semantics”) • Expressiveness = • the amount the computer understands • the amount it is able to use to draw conclusions from • Everything else is meaningless to the computer • e.g., CPL can’t express: • constraints, defaults, some quantification patterns

  17. NL-based KR “Traditional” KR distance(_Walk1, _Mile1) count(_Mile1, 10) distance(_Walk1, _Distance1) value(_Distance1, 10, mile)   4. Sometimes, linguistically motivated representations are poor • Language-based KR: • Most concepts correspond to words • Structure of KB will mirror structure of language • Is this bad? Sometimes… “… walked for 10 miles”

  18. 5. (Lack of) Canonicalization “conducting a test of an entity” “testing an entity” • Many ways to say the same thing • System needs to realize the equivalence BUT: often NL-based KRs will not  Solutions: • Add equivalence rules. (But there are lots!!) • e.g., “Conducting a X of Y ↔ Xing a Y” • Have the interpreter normalize the input. • Restrict the input language.

  19. Summary • CPL = a restricted English language for knowledge • Hits “sweet spot” between logic and full NLP • Produces inference-capable representations • Is viable, used to build a large KB • But: No “free lunch” • requires skill to use it effectively • NL-based KRs are becoming more important! • Web: need semantically meaningful annotations • AI: need better knowledge acquisition tools • Some exciting possibilities ahead (esp. at Boeing!)

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