On WordNet, Text Mining, and Knowledge Bases of the Future

On WordNet, Text Mining, and Knowledge Bases of the Future Peter Clark March 2006 Knowledge Systems Boeing Phantom Works

Introduction • Interested in text understanding & question-answering • use of world knowledge to go beyond text • Used WordNet as (part of) the knowledge repository • got some leverage • can we get more? • what would a WordNet KB look like?

Outline • Machine understanding and question-answering • An initial attempt • From WordNet to a Knowledge Base • Representation • Reasoning • Text Mining for Possibilistic Knowledge • A Knowledge Base of the Future?

On Machine Understanding • Consider “China launched a meteorological satellite into orbit Wednesday, the first of five weather guardians to be sent into the skies before 2008.” • Suggests: • there a rocket launch • China owns the satellite • the satellite is for monitoring weather • the orbit is around the Earth • etc. None of these are explicitly stated in the text

On Machine Understanding • Understanding = creating a situation-specific model (SSM), coherent with data & background knowledge • Data suggests background knowledge which may be appropriate • Background knowledge suggest ways of interpreting data ? ? Fragmentary, ambiguous inputs Coherent Model (situation-specific)

On Machine Understanding World Knowledge Assembly of pieces, assessment of coherence, inference ? ? Fragmentary, ambiguous inputs Coherent Model (situation-specific)

On Machine Understanding World Knowledge • Conjectures about the nature of the beast: • “Small” number of core theories • space, time, movement, … • can encode directly • Large amount of “mundane” facts • a dictionary contains many of these facts

Caption-Based Video Retrieval World Knowledge Coherent representation of the scene (elaborated, disambiguated) ? ? English captions describing a video segment (partial, ambiguous) Question-Answering, Search, etc.

Illustration: Caption-Based Video Retrieval Open Caption text Interpretation agent object Man Door Airplane is-part-of Elaboration (inference, scene-building) Open Airplane Door Man World Knowledge Query: Touch Search Door Person Video “A lever is rotated to the unarmed position” “…” “A man opens an airplane door” “…” Captions (manual authoring)

Semantic Retrieval • Query: “A person walking” → Result: “A man carries a box across a room” • “Someone injured” → “An employee was drilling a hole in a piece of wood. The drill bit of the drill broke. The drill twisted out of the employee's right hand. The drill injured the employee's right thumb.” • “An object was damaged” → the above caption (x 2) → “Someone broke the side mirrors of a Boeing truck.”

The Knowledge Base • Representation: • Horn-Clause rules • plus add/delete lists for “before” and “after” rules • Authored in simplified English • NLP system interactively translates to logic • WordNet + UT Austin relations as the ontology • ~1000 rules authored • just a drop in the ocean! • Reasoning: • depth-limited forward chaining • precondition/effects just asserted (no sitcalc simulation)

Some of the Rules in the KB: IF a person is carrying an entity that is inside a room THEN(almost) always the person is in the room. IF a person is picking an object up THEN(almost) always the person is holding the object. IF an entity is near a 2nd entity AND the 2nd entity contains a 3rd entity THEN usually the 1st entity is near the 3rd entity. ABOUT boxes: usually a box has a lid. BEFORE a person gives an object, (almost) always the person possesses the object. AFTER a person closes a barrier, (almost) always the barrier is shut. …1000 more…

Some of the Rules in the KB: IF a person is carrying an entity that is inside a room THEN (almost) always the person is in the room. isa(_Person1, person_n1), isa(_Carry1, carry_v1), isa(_Entity1, entity_n1), isa(_Room1, room_n1), agent(_Carry1, _Person1), object(_Carry1, _Entity1), is-inside(_Entity1, _Room1), ==== (almost) always ===> is-inside(_Person1, _Room1).

Critique: 2 Big Questions Hanging • Representation: The Knowledge Base • Unscalable to build the KB from scratch • WordNet helped a lot • Could it be extended to help more? • What would that WordNet KB look like? • How could it be built? • Reasoning: • Deductive inference is insufficient • How looks with large, noisy, uncertain knowledge?

What Knowledge Do We Need? "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Like system to infer that: The bomb exploded The explosion caused the devastation The shrine was damaged … System needs to know: Bombs can explode Explosions can destroy things Destruction ≈ devastation Attacks are usually done by people …

What Knowledge Do We Need? "Israeli troops were engaged in a fierce gun battle with militants in a West Bank town. An Israeli soldier was killed. Like system to infer that: There was a fight. The soldier died. The soldier was shot. The soldier was a member of the Israeli troops. … System needs to know: A battle involves a fight. Soldiers use guns. Guns can kill. If you are killed you are dead. Soldiers belong to troops …

WordNet (Princeton Univ) • Is not a word net; is a concept net • 117,000 lexically motivated concepts (synsets) • organized into a taxonomy (hypernymy) • massively used in AI (~7000 downloads/month) 201173984 201378060: "shuffle", "ruffle", "mix": (mix so as to make a random order or arrangement; "shuffle the cards") 201174946 superclass / genls / supertype 201378060

WordNet (Princeton Univ) • Is not a word net; is a concept net • 117,000 lexically motivated concepts (synsets) • organized into a taxonomy (hypernymy) • massively used in AI (~7000 downloads/month) handle_v4 mix_v6: "shuffle", "ruffle", "mix": (mix so as to make a random order or arrangement; "shuffle the cards") manipulate_v2 superclass / genls / supertype mix_v6

The Evolution of WordNet lexical resource • v1.0 (1986) • synsets (concepts) + hypernym (isa) links • v1.7 (2001) • add in additional relationships • has-part • causes • member-of • entails-doing (“subevent”) • v2.0 (2003) • introduce the instance/class distinction • Paris isa Capital-City is-type-of City • add in some derivational links • explode related-to explosion • … • v10.0 (2010?) • ????? knowledge base?

WordNet as a Knowledge Base Got: just “isa” and “part-of” knowledge But still need: • Axioms about each concept! • From definitions and examples (?) • shallow extraction has been done (LCC and ISI) • getting close to useful logic II. Relational vocabulary (case roles, semantic relns) • could take from: FrameNet, Cyc, UT Austin III. Relations between word senses: • bank (building) vs. bank (institution) vs. bank (staff) • cut (separate) vs. cut (sweeping motion)

I. Knowledge in the word sense definitions: How much knowledge is in WordNet? • Ide & Veronis: • dictionaries have no broad contextual/world knowledge • e.g., no connection between “lawn” and “house” • Not true! WN1.6 1 sense of lawn Sense 1 lawn#1 -- (a field of cultivated and mowed grass) -> field#1 -- (a piece of land cleared of trees and usually enclosed) => yard#2, grounds#2 -- (the land around a house or other building; "it was a small house with almost no yard at all") WN1.7.1 garden -- (a yard or lawn adjoining a house)

I. Knowledge in the word sense definitions: How much knowledge is in WordNet? "lawn". WordNet seems to "know", among other things, that lawns • need watering • can have games played on them • can be flattened, mowed • can have chairs on them and other furniture • can be cut/mowed • things grow on them • have grass ("lawn grass" is a common compound) • leaves can get on them • can be seeded

I. Knowledge in the word sense definitions: How much knowledge is in WordNet? "accident" (ignoring derivatives like "accidentally") • accidents can block traffic • you can be prone to accidents • accidents happen • result from violent impact; passengers can be killed • involve vehicles, e.g., trains • results in physical damage or hurt, or death • there are victims • you can be blamed for accidents

Let’s take a look…

I. Knowledge in the word sense definitions: Generating Logic from Glosses • Definitions appear deceptively simple • really, huge representational challenges underneath hammer_n2:(a hand tool with a heavy rigid head and a handle; used to deliver an impulsive force by striking) launch_v3: (launch for the first time; "launch a ship") cut_n1: (the act of reducing the amount or number) love_v1: (have a great affection or liking for) theater_n5: (a building where theatrical performances can be held) • Want logic to be faithful but also simple (usable) • Claim: We can get away with a “shallow” encoding • all knowledge as Horn clauses • some loss of fidelity • gain in syntactic simplicity and reusability

I. Knowledge in the word sense definitions: Simplifying • “Timeless” representations • No tagging of facts with situations • Representation doesn’t handle change break_v4:(render inoperable or ineffective; "You broke the alarm clock when you took it apart!") Ax,y isa(x,Break_v4) & isa(y,Thing) & object(x,y) → property(y,Inoperable) Break object Thing Inoperable property

I. Knowledge in the word sense definitions: Simplifying • For statements about types, use instances instead: “hammer_n2:(… used to deliver an impulsive force by striking)” • Ax isa(x,Hammer_n2) → • Ed,f,s,y,z … & • isa(d, Deliver_v9) & • isa(s, Hit_v2) & • isa(f, Force_n3) & • purpose(x, d) & • object(d, f) & • subevent(d, s). object purpose Hammer Deliver Force has-part subevent Handle Head Strike property Rigid Heavy Strictly, should be purpose(x,Deliver-Impulsive-Force)

II: Relational Vocabulary • Is this enough? • No, also need relational vocabulary • Which relational vocabulary to use? • agent, patient, has-part, contains, destination, … • Possible sources: • UT Austin’s Slot Dictionary (~100 relations) • Cyc (~1000 relations) • FrameNet (??)

III. Relations between word senses: Nouns School_n1: an institution School_n2: a building School_n3: the process of being educated School_n4: staff and students School_n5: a time period of instruction • Nouns often have multiple, related senses • Reasoner needs to know these are related The school declared that the teacher’s strike was over. Students should arrive at 9:15am tomorrow morning. School_n1 (institution) School_n2 (building) School_n4 (staff,students)

III. Relations between word senses: Nouns • Can hand-code these relationships (slow) constituent staff and students (School_n4) institution (School_n1) participants constituent educational process (School_n3) building (School_n2) location during time period of instruction (School_n5)

III. Relations between word senses: Nouns • Can hand-code these relationships (slow) • BUT: The patterns repeat (Buitelaar) constituent staff and students (School_n4) institution (School_n1) participants constituent educational process (School_n3) building (School_n2) location during time period of instruction (School_n5) constituent institution members constituent participants building process location during time period

III. Relations between word senses: Nouns • Can hand-code these relationships (slow) • BUT: The patterns repeat (Buitelaar) • can encode and reuse the patterns constituent staff and students (School_n4) institution (School_n1) participants constituent educational process (School_n3) building (School_n2) location during time period of instruction (School_n5) constituent institution members constituent participants building process location during time period

III. Relations between word senses: Verbs • WordNet’s verb senses: • 41 senses of “cut” • linguistically not representationally motivated • “cut grass” (cut_v18) ≠ “cut timber” (cut_v31) ≠ “cut grain” (cut_v28) (“mow”, “chop”, “harvest”) • cut_v1 (separate) ≠ cut_v3 (slicing movement) • fails to capture commonality • Better: • Organize verbs into “mini taxonomy” • “Supersenses”, to group same meanings • Identify facets of verbs, use multiple inheritance • result of action • style of action

The Myth of Common-Sense:All you need is knowledge… “We don’t believe that there’s any shortcut to being intelligent; the “secret” is to have lots of knowledge.” Lenat & Guha ‘86 “Knowledge is the primary source of the intellectual power of intelligent agents, both human and computer.” Feigenbaum ‘96

The Myth of Common-Sense • Common, implicit assumption (belief?) in AI: • Knowledge is the key to intelligence • Acquisition of knowledge is bottleneck • Spawned from: • ’70s experience with Expert Systems • Feigenbaum’s “knowledge acquisition bottleneck” • Introspection

Thought Experiment… • Suppose we had • good logical translations of the WordNet definitions • good relational vocabulary • rich relationships between related word senses • How would these be used? • Would they be enough? • What else would be needed?

Initial Scenario Sentence "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn time causes Devastate Attack instrument object Shrine Bomb

One Elaboration Step (knowledge of bomb) "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn time causes Devastate Attack WordNet instrument object Shrine Bomb “bomb: an explosive device fused to detonate” Device Bomb Detonate purpose contains Explosive

One Elaboration Step (knowledge of bomb) "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn time causes Devastate Attack WordNet instrument object Shrine Bomb “bomb: an explosive device fused to detonate” Dawn Device causes Devastate Attack instrument Bomb Detonate Device purpose object Shrine contains Detonate Bomb purpose Explosive contains Explosive

Additional, Relevant Knowledge in WordNet “bomb: an explosive device fused to detonate” “bombing: the use of bombs for sabotage; a tactic frequently used by terrorists” Device agent Terrorist Bombing Bomb Detonate purpose instrument contains Bomb Explosive “destroy: damage irrepairably” causes “plastic explosive: an explosive material …intended to destroy” Destroy Damage Destroy “explode: destroy by exploding” Destroy causes purpose Explode Explosive

Multiple Elaboration Steps "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn WordNet time causes {Devastate, Destroy} Attack instrument object Shrine Bomb “bomb: an explosive device fused to detonate” Dawn time causes Devastate Attack instrument object {Detonate, Explode} Shrine Bomb contains Explosive

Multiple Elaboration Steps "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn WordNet time causes {Devastate, Destroy} Attack instrument object Shrine Bomb “bomb: an explosive device fused to detonate” “bombing: the use of bombs for sabotage; a tactic frequently used by terrorists” Dawn time agent causes Devastate Terrorist Attack instrument object {Detonate, Explode} Shrine Bomb contains Explosive

Multiple Elaboration Steps "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn WordNet time causes {Devastate, Destroy} Attack instrument object Shrine Bomb “bomb: an explosive device fused to detonate” “bombing: the use of bombs for sabotage; a tactic frequently used by terrorists” “plastic explosive: an explosive material …intended to destroy” Dawn time agent causes {Devastate, Destroy} Terrorist Attack instrument object {Detonate, Explode} Shrine Bomb purpose contains Explosive

Multiple Elaboration Steps "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn WordNet time causes {Devastate, Destroy} Attack instrument object Shrine Bomb “bomb: an explosive device fused to detonate” “bombing: the use of bombs for sabotage; a tactic frequently used by terrorists” “plastic explosive: an explosive material …intended to destroy” “destroy: damage irrepairably” Dawn time agent causes {Devastate, Destroy} causes Terrorist Attack Damage instrument object {Detonate, Explode} Shrine Bomb purpose contains Explosive

Multiple Elaboration Steps "A dawn bomb attack devasted a major Shiite shrine in Iraq..." Dawn WordNet time causes {Devastate, Destroy} Attack instrument object Shrine Bomb “bomb: an explosive device fused to detonate” “bombing: the use of bombs for sabotage; a tactic frequently used by terrorists” “plastic explosive: an explosive material …intended to destroy” “destroy: damage irrepairably” “explode: destroy by exploding” Dawn time agent causes {Devastate, Destroy} causes Terrorist Attack Damage instrument causes object {Detonate, Explode} Shrine Bomb purpose contains Explosive

How this really works… • Pieces may not “fit together” so neatly • multiple ways of saying the same thing • Uncertainty at all stages of the process • definitions are often only typical facts • errors in both English and translations • Process is not a chain of deductions, rather • is a search of possible elaborations • looking for the most “coherent” elaboration • More “crystallization” rather than “deduction”

On WordNet, Text Mining, and Knowledge Bases of the Future