Framenet meets the semantic web
Download
1 / 155

FrameNet Meets the Semantic Web - PowerPoint PPT Presentation


  • 713 Views
  • Updated On :

FrameNet Meets the Semantic Web. Srini Narayanan Charles Fillmore Collin Baker Miriam Petruck. Outline of Presentation. Semantic Frames and the FrameNet Project Status of FrameNet Data and Software Details on the FrameNet process Comparison to other ontologies/resources

Related searches for FrameNet Meets the Semantic Web

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'FrameNet Meets the Semantic Web' - RoyLauris


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Framenet meets the semantic web l.jpg

FrameNet Meets the Semantic Web

Srini Narayanan

Charles Fillmore

Collin Baker

Miriam Petruck


Outline of presentation l.jpg
Outline of Presentation

  • Semantic Frames and the FrameNet Project

  • Status of FrameNet Data and Software

  • Details on the FrameNet process

  • Comparison to other ontologies/resources

  • Afternoon session: Going through the annotation process demo.


The framenet project l.jpg
The FrameNet Project

  • Phase I (NSF, 1997-2000)

    • ICSI, U-Colorado

    • Conceptual basis, used existing tools, and perl

  • Phase II (NSF, 2000-2003)

    • ICSI, U-Colorado, SRI, SDSU

    • Scaling up, uses SQL database and Java-based in house tools. Pilot applications developed.


The framenet project4 l.jpg
The FrameNet Project

C Fillmore PI (ICSI)

Co-PI’s: S Narayanan (ICSI, SRI)D Jurafsky (U Colorado) J M Gawron (San Diego State U)

Staff:

C Baker Project Manager

B Cronin Programmer

C Wooters Database Designer


Applications l.jpg
Applications

An important goal of our work is to present information about the words in a form that will prove usable in various NLP applications:

  • Question Answering (Berkeley, Colorado)

  • Semantic Extraction (Berkeley, SRI, Colorado)

  • Machine Translation (San Diego State)


Frames and understanding l.jpg
Frames and Understanding

  • Hypothesis: People understand things by performing mental operations on what they already know. Such knowledge is describable in terms of information packets called frames.


Framenet in the larger context l.jpg
FrameNet in the Larger Context

  • The long-term goal is to reason about the world in a way that humans understand and agree with.

  • Such a system requires a knowledge representation that includes the level of frames.

  • FrameNet can provide such knowledge for a number of domains.

  • FrameNet representations complement ontologies and lexicons.


The core work of framenet l.jpg
The core work of FrameNet

  • characterize frames

  • find words that fit the frames

  • develop descriptive terminology

  • extract sample sentences

  • annotate selected examples

  • derive "valence" descriptions


Lexicon building l.jpg
Lexicon Building

  • We study words,

  • describe the frames or conceptual structures which underlie them,

  • examine sentences that contain them

    (from a vast corpus of written English),

  • and record the ways in which information from the associated frames are expressed in these sentences.


The core data l.jpg
The Core Data

The basic data on which FrameNet descriptions are based take the form of a collection of annotated sentences, each coded for the combinatorial properties of one word in it. The annotation is done manually, but several steps are computer-assisted.


The process l.jpg
The Process

  • Sentences containing a given word are extracted from the corpus and made available for annotation.

  • Student annotators select the phrases that identify particular semantic roles in the sentences, and tag them with the name of these roles.

  • Automatic processes then provide grammatical information about the tagged phrases.



Types of words frames l.jpg
Types of Words / Frames

  • events

  • artifacts, built objects

  • natural kinds, parts and aggregates

  • terrain features

  • institutions, belief systems, practices

  • space, time, location, motion

  • etc.


Event frames l.jpg
Event Frames

Event frames have temporal structure, and generally have constraints on what precedes them, what happens during them, and what state the world is in once the event has been completed.


Sample event frame commercial transaction l.jpg
Sample Event Frame:Commercial Transaction

Initial state:Vendor has Goods, wants MoneyCustomer wants Goods, has Money

Transition:Vendor transmits Goods to CustomerCustomer transmits Money to Vendor

Final state:Vendor has Money

Customer has Goods


Sample event frame commercial transaction16 l.jpg
Sample Event Frame:Commercial Transaction

Initial state:Vendor has Goods, wants MoneyCustomer wants Goods, has Money

Transition:Vendor transmits Goods to CustomerCustomer transmits Money to Vendor

Final state:Vendor has Money

Customer has Goods

(It’s a bit more complicated than that.)


Partial wordlist for commercial transactions l.jpg
Partial Wordlist for Commercial Transactions

Verbs: pay, spend, cost, buy, sell, charge

Nouns: cost, price, payment

Adjectives: expensive, cheap


Meaning and syntax l.jpg
Meaning and Syntax

  • The various verbs that evoke this frame introduce the elements of the frame in different ways.

    • The identities of the buyer, seller, goods and money

  • Information expressed in sentences containing these verbs occurs in different places in the sentence depending on the verb.


Slide19 l.jpg

Sheboughtsome carrotsfrom the greengrocerfor a dollar.

Customer

Vendor

from

BUY

for

Goods

Money


Slide20 l.jpg

Shepaida dollarto the greengrocerfor some carrots.

Customer

Vendor

to

PAY

for

Goods

Money


Slide21 l.jpg

Shepaidthe greengrocera dollarfor the carrots.

Customer

Vendor

PAY

for

Goods

Money


Slide22 l.jpg

Shespenta dollaron the carrots.

Customer

Vendor

SPEND

on

Goods

Money


Slide23 l.jpg

The greengrocer sold some carrotsto herfor a dollar.

Customer

Vendor

to

SELL

for

Goods

Money


Slide24 l.jpg

The greengrocersoldhersome carrotsfor a dollar.

Customer

Vendor

SELL

for

Goods

Money


Slide25 l.jpg

The greengrocerchargeda dollarfor a bunch of carrots.

Customer

Vendor

CHARGE

for

Goods

Money


Slide26 l.jpg

The greengrocerchargedhera dollarfor the carrots.

Customer

Vendor

CHARGE

for

Goods

Money


Slide27 l.jpg

A bunch of carrotscostsa dollar.

Customer

Vendor

COST

Goods

Money


Slide28 l.jpg

A bunch of carrotscosthera dollar.

Customer

Vendor

COST

Goods

Money


Slide29 l.jpg

Itcostsa dollarto ride the bus.

IT

Customer

Vendor

COST

Goods

to do X

Money


Slide30 l.jpg

Itcostmea dollarto ride the bus.

IT

Customer

Vendor

COST

Goods

to do X

Money


Framenet product l.jpg
FrameNet Product

  • For every target word,

  • describe the frames or conceptual structures which underlie them,

  • and annotate example sentences that cover the ways in which information from the associated frames are expressed in these sentences.


Fn work characterizing frames l.jpg
FN work: characterizing frames

  • One of the things we do is characterize such information packets - beginning with informal descriptions.

  • We can begin with Revenge.


The revenge frame l.jpg
The Revenge frame

The Revenge frame involves a situation in which

  • A has done something to harm B and

  • B takes action to harm A in turn

  • B's action is carried out independently of any legal or other institutional setting


Fn work finding words in frame l.jpg
FN work: finding words in frame

  • We look for words in the language that bring to mind the individual frames.

  • We say that the words evoke the frames.


Vocabulary for revenge l.jpg
Vocabulary for Revenge

  • Nouns: revenge, vengeance, reprisal, retaliation

  • Verbs: avenge, retaliate, revenge, get back (at), get even (with), pay back

  • Adjectives: vengeful, vindictive


Fn work choosing fe names l.jpg
FN work: choosing FE names

  • We develop a descriptive vocabulary for the components of each frame, called frame elements (FEs).

  • We use FE names in labeling the constituents of sentences exhibiting the frame.


Fes for revenge l.jpg
FEs for Revenge

  • Frame Definition: Because of some injury to something or someone important to an avenger, the avenger inflicts a punishment on the offender. The offender is the person responsible for the injury. The injured_party may or may not be the same individual as the avenger.

  • FE List: avenger, offender, injury, injured_party, punishment.


Fn work collecting examples l.jpg
FN work: collecting examples

  • We extract from our corpus examples of sentences showing the uses of each word in the frame.


Slide40 l.jpg

Obviously we need to conduct a more regimented search,

grouping examples with related structures.



Fn work annotating examples l.jpg
FN work: annotating examples

  • We select sentences exhibiting common collocations and showing all major syntactic contexts.

  • Using the names assigned to FEs in the frame, we label the constituents of sentences that express these FEs.


Fn work summarizing results l.jpg
FN work: summarizing results

  • Automatic processes summarize the results, linking FEs with information about their grammatical realization.

  • The output is presented in the form of various reports in the public website, in XML format in the data release.




Querying the data meaning to form l.jpg
Querying the data: meaning to form

Through various viewers built on the FN database we can, for example, ask how particular FEs get expressed in sentences evoking a given frame.


By what syntactic means is offender realized l.jpg
By what syntactic means is offender realized?

  • Sometimes as direct object: we'll pay you back for that

  • Sometimes with the preposition on they'll take vengeance on you

  • Sometimes with against we'll retaliate against them

  • Sometimes with with she got even with me

  • Sometimes with at

    they got back at you


By what syntactic means is offender realized52 l.jpg
By what syntactic means is offender realized?

  • Sometimes as direct object: we'll pay you back for that

  • Sometimes with the preposition on they'll take vengeance on you

  • Sometimes with against we'll retaliate against them

  • Sometimes with with she got even with me

  • Sometimes with at

    they got back at you

It's these word-by-word

specializations in

FE-marking that make

automatic FE recognition

difficult.


Querying the data form to meaning l.jpg
Querying the data: form to meaning

Or, going from the grammar to the meaning, we can choose particular grammatical contexts and ask which FEs get expressed in them.


What fe is expressed by the object of avenge l.jpg
What FE is expressed by the object of avenge?

  • Sometimes it's the injured_partyI've got to avenge my brother

  • .Sometimes it's the injuryMy life goal is to avenge my brother's murder.


Evaluation l.jpg
Evaluation

  • Lexical coverage. We want to get all of the important words associated with each frame.

  • Combinatorics. We want to get all of the syntactic patterns in which each word functions to express the frame.


Evaluation56 l.jpg
Evaluation

  • We do not ourselves collect frequency data. That will wait until methods of automatic tagging get perfected.

  • In any case, the results will differ according to the type of corpus - financial news, children's literature, technical manuals, etc.


What do we end up with l.jpg
What do we end up with?

  • Frames

  • Lexical entries

  • Annotations


Sample from frames list l.jpg
Sample from frames list

Creating, Crime_scenario, Criminal_investigation, Criminal_process, Cure. Custom, Damaging, Dead_or_alive, Death, Deciding, Deny_permission, Departing, Desirability, Desiring, Destroying, Detaining, Differentiation, Difficulty, Dimension, Direction, Dispersal, Documents, Domain, Duplication, Duration, Eclipse, Education_teaching,Emanating, Emitting, Emotion_active, Emotion_directed, Emotion_heat, Employing, Employment, Emptying, Encoding, Endangering, Entering_of_plea, Entity, Escaping, Evading. Evaluation, Evidence, Excreting, Execution,


Sample from lexical unit list l.jpg

* augmentation.N (Expansion)

* augur.V (Omen)

* August.N (Calendric_unit)

* aunt.N (Kinship)

* auntie.N (Kinship)

* austere.A (Frugality)

* austerity.N (Frugality)

* author.V (Text_creation)

* authoritarian.A (Strictness)

* authorization.N (Documents)

* autobahn.N (Roadways)

* autobiography.N (Text)

* automobile.N (Vehicle)

* autumn.N (Calendric_unit)

* avalanche.N (Quantity)

* avenge.V (Revenge)

* avenger.N (Revenge)

* avenue.N (Roadways)

* aver.V (Statement)

Sample from lexical unit list


Added value frame relatedness l.jpg
Added Value: frame relatedness

  • We have ways of linking frames to each other, through relations of

    • inheritance

    • subframe

    • "using"

  • We would like to explore how our frame relationships can be mapped onto ontological relations.


Frame to frame relations l.jpg
Frame-to-frame relations

  • Revenge inherits Punishment/Reward

  • Revenge uses the Hostile_encounter frame

  • (see existing tentative frame hierarchy)


Added value semantic types l.jpg
Added Value: semantic types

  • We also have the means of adding semantic types to words, frames and frame elements.

  • Some of these:

    • negative vs. positive (disaster vs. bonanza),

    • punctual vs. stative (arrive vs. reside),

    • artifact vs. natural kind (building vs. tree).


Added value semantic types63 l.jpg
Added Value: semantic types

  • For the kinds of nouns that occupy particular FE slots in given frames, we should be able to use the WordNet noun taxonomies.

  • This is done in some related work


Added value support verbs l.jpg
Added Value: support verbs

  • In the case of the event nouns, we keep track of which verbs can combine with which nouns to signal occurrences of the frame evoked by the noun.

    • take a bath (bathe)

    • have an argument (argue)

    • wreak vengeance,

    • take revenge,

    • exact retribution.


Can annotation be automated l.jpg
Can annotation be automated?

Gildea, D & D Jurafsky, 2000, Automatic labeling of semantic roles, Association for Computational Linguistics, Hong Kong.

Mohit & Narayanan, 2003, Semantic Extraction using Wide-coverage lexical resources, HLT-NAACL 2003.


The database l.jpg
The Database

The information collected from the data (and a certain amount of information inserted manually by the lexicographers) is stored in a MySQL database.


Current status l.jpg
Current Status

  • Current: 7700 Lexical Units

    • FN1: 1600 Lexical units

    • FN2: 4400 Lexical Units

    • Created (not yet annotated): 1280 LU

    • Other : in process, problems, etc.


Current status69 l.jpg
Current Status

  • 500 Frames

  • 7700 Lexical Units

  • 130,000 Annotated sentences


Data distribution l.jpg
Data Distribution

Distributed as XML files with accompanying DTDs

Separate files and DTDs for

  • Frame and FE data

  • –Annotation data

  • Frame relation data

  • Easy to parse with standard XML tools.

    • Approximately 100 research groups have been authorized to download release 1.0 of the FN data (Oct., 2002).

  • Next release scheduled for August, 2003


  • Framenet software distribution l.jpg
    FrameNet Software Distribution

    • All software is pure Java, and can be run on any platform for which a JVM is available

    • Has been successfully run on Solaris, Linux, Mac OS X, and Windows 9x/2000 with very minor modifications

    • Server and clients currently being used in Barcelona for annotation in Spanish FN.

    • We will streamline the installation process if demand warrants

    • We plan to publicly release the full software suite in August, 2003.


    Multi lingual framenets l.jpg
    Multi-Lingual FrameNets

    • Spanish FrameNet

      • Prof. Carlos Subirats, U A Barcelona

      • Parallel to English FrameNet, using same frames

    • German FrameNet

      • Prof. Manfred Pinkal, U Saarlandes

      • Complete annotation of existing parsed corpus,

      • using English frames where possible

    • Japanese FrameNet

      • Prof. Kyoko Ohara, Keio U

      • Collecting own corpus, building search tools



    Is fn an ontology l.jpg
    Is FN an ontology?

    • Not exactly, but some users use FN frames as an ontology of event types.


    Is fn a thesaurus l.jpg
    Is FN a thesaurus?

    Yes, because it groups words into meaning categories, by way of shared membership in frames.


    How is fn different from wn l.jpg
    How is FN different from WN?

    FN does not explicitly display semantic relations between words of the sort found in WordNet. (synonymy, antonymy, hyponymy, meronymy, etc.)

    Furthermore, FN includes many opposing pairs (hot, cold; tall, short) in the same frame.


    Are fn annotations a treebank l.jpg
    Are FN annotations a treebank?

    • FrameNet accumulates annotations, but FN annotations are mainly sentences in which only one word is analyzed thoroughly.

    • Unlike existing treebanks, e.g., U Penn's PropBank, FN has a richer semantics.



    American heritage dictionary l.jpg

    avenge v.1. To inflict a punishment or penalty in return for; revenge2. To take vengeance on behalf of

    revenge v.1. To inflict punishment in return for (injury or insult)2. To seek or take vengeance for (oneself or another person); avenge

    American Heritage Dictionary


    American heritage dictionary80 l.jpg

    avenge v.1. To inflict a punishment or penalty in return for; revenge2. To take vengeance on behalf of

    revenge v.1. To inflict punishment in return for (injury or insult)2. To seek or take vengeance for (oneself or another person); avenge

    American Heritage Dictionary

    The FEs of the direct objects are expressed prepositionally;

    "in return for" marks the injury; "for" or "onbehalfof" marks

    the injured_party.


    American heritage dictionary81 l.jpg

    avenge v.1. To inflict a punishment or penalty in return for [ ]; revenge2. To take vengeance on behalf of [ ]

    revenge v.1. To inflict punishment in return for (injury or insult)2. To seek or take vengeance for (oneself or another person); avenge

    American Heritage Dictionary

    revengedefiner added qualifications on the missing

    argument, avengedefiner didn't.


    American heritage dictionary82 l.jpg

    avenge v.1. To inflict a punishment or penalty in return for; revenge2. To take vengeance on behalf of

    revenge v.1. To inflict punishment in return for (injury or insult)2. To seek or take vengeance for (oneself or another person); avenge

    American Heritage Dictionary

    avengedefiner claims avengeand revengeare

    synonym in sense 1; the revengedefiner claims avenge

    and revengeare synonyms in sense 2.


    American heritage dictionary83 l.jpg

    avenge v.1. To inflict a punishment or penalty in return for; revenge2. To take vengeance on behalf of

    revenge v.1. To inflict punishment in return for (injury or insult)2. To seek or take vengeance for (oneself or another person); avenge

    American Heritage Dictionary

    revengedefiner included "seek vengeance", not supported

    by FN examples.


    American heritage dictionary84 l.jpg

    avenge v.1. To inflict a punishment or penalty in return for; revenge2. To take vengeance on behalf of

    revenge v.1. To inflict punishment in return for (injury or insult)2. To seek or take vengeance for (oneself or another person); avenge

    American Heritage Dictionary

    Both definers include "take vengeance" in their definitions, as

    if that's more transparent than the simple verb.



    We make fewer distinctions l.jpg
    We make fewer distinctions.

    1. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother")

    2. retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Empire strikes back"; "The Giants struck back and won the opener"; "The Israeli army retaliated for the Hamas bombing")


    We make fewer distinctions87 l.jpg
    We make fewer distinctions.

    1. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother")

    2. retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Empire strikes back"; "The Giants struck back and won the opener"; "The Israeli army retaliated for the Hamas bombing")

    Hard to figure out what motivates distinguishing two senses;

    personal vs. institutional?


    We make fewer distinctions88 l.jpg
    We make fewer distinctions.

    1. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother")

    2. retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Empire strikes back"; "The Giants struck back and won the opener"; "The Israeli army retaliated for the Hamas bombing")

    Like FrameNet, these entries include Definitions and Examples.

    FrameNet limits its examples to attested sentences from a Corpus.


    Fn has more detailed syntax l.jpg
    FN has more detailed syntax.

    revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother")

    *> Somebody ----s something

    retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Israeli army retaliated for the Hamas bombing")

    *> Somebody ----s

    *> Somebody ----s PP

    The WN sentence templates are impoverished structurally

    and do not indicate the semantic roles. In fact, retaliate is

    wrongly described as taking a simple object.


    Fn has more detailed syntax90 l.jpg
    FN has more detailed syntax.

    revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother")

    *> Somebody ----s something

    retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Israeli army retaliated for the Hamas bombing")

    *> Somebody ----s

    *> Somebody ----s PP

    The identity of the P in PP is important: strike backat

    marks the offender, as does retaliateagainst; retaliate

    for marks the injury.


    Fn has more detailed syntax91 l.jpg
    FN has more detailed syntax.

    revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother")

    *> Somebody ----s something

    retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Israeli army retaliated for the Hamas bombing")

    *> Somebody ----s

    *> Somebody ----s PP

    Where WordNet merely shows that the words in the

    second synset can occur intransitively, FN would say

    something about the anaphoric nature of the omitted

    offender.



    Switching frames l.jpg
    Switching frames

    • Revenge is a simple frame, but neither SUMO nor OpenCYC seem to have any conceptual link to it.

    • A particular family of frames that we have concentrated on are those that make up the steps and institutions of Criminal_process.


    Complex frames l.jpg
    Complex Frames

    • With Criminal_process we have, for example,

      • sub-frame relations (one frame is a component of a larger more abstract frame) and

      • temporal relations (one process precedes another)


    Inferencing l.jpg
    Inferencing

    • These are the frames with which we are trying to set up inferencing rules for texts about crime reports. (Details in the presentation later.)


    In sumo l.jpg
    In SUMO

    • SUMO (Adam Pease) deals with only the upper ontology, and moves toward our frame along this path, stopping at legal action.

      • entity

      • process

      • intentional process

      • social interaction

      • contest

      • legal action


    In opencyc arrestingsomeone l.jpg
    In OpenCYC: ArrestingSomeone

    ArrestingSomeone: "A specialization of Social Occurrence and CapturingAnimal. In each instances of ArrestingSomeone a law enforcement officer arrests another person, who is then taken into custody. See the related constant #$HeldCaptive."


    Trial l.jpg
    Trial

    comment : [[Def]] "The subcollection of #$LegalConflict events whose instances are heard and decided by a court and are officiated by a #$Judge."

    requiredActorSlots : [[Mon]] plaintiffs [[Mon]] defendants


    Legal activities l.jpg
    Legal activities

    comment : [[Def]] "The collection of all events performed with the purpose of enforcing laws, that are performed by people officially charged with this this duty. Includes most activities of law enforcement officials (such as police) including detection of crime, identification of offenders, and arrests."


    Lawenforcementofficer l.jpg
    LawEnforcementOfficer

    comment : [[Def]] "An instance of PersonTypeByOccupation, and a specialization of PersonWithOccupation. Each instance of LawEnforcementOfficer is a person whose job is to detect, stop, and/or punish people engaged in illegal activities. The collection LawEnforcementOfficer includes members of local, state, and special police (e.g., transit police) forces, as well as federal agents (e.g., members of border patrols, national security agents). Consequently, a given instance of Law EnforcementOfficer typically also belongs to one of the following collections: #$StateEmployee, #$LocalGovernment Employee, or NationalGovernmentEmployee (see Public SectorEmployee)."


    Framenet for applications l.jpg
    FrameNet for Applications

    • Semantic Web (http://www.semanticweb.org)

      • FN database in DAML+OIL (http://www.ai.sri.com/~narayana/frame-desc.daml)

    • Semantic Extraction using FrameNet

    • Frame Simulation and Inference

      • Translation from frame structure to a simulation based inference tool (KarmaSIM)

        • (COLING 2002)


    Talk outline l.jpg
    Talk Outline

    • FrameNet

    • A DAML + OIL Representation of FrameNet

    • An Example: Encoding the Criminal Process Frame

    • Web Applications of FrameNet.

    • Summary and Future Work


    Semantic web l.jpg
    Semantic Web

    • The World Wide Web (WWW) contains a large and expanding information base.

    • HTML is accessible to humans but does not formally describe data in a machine interpretable form.

    • XML remedies this by allowing for the use of tags to describe data (ex. disambiguating crawl)

    • Ontologies are useful to describe objects and their inter-relationships.

    • DAML+OIL (http://www.daml.org) is an markup language based on XML and RDF that is grounded in description logic and is designed to allow for ontology development, transfer, and use on the web.


    Framenet entities and relations l.jpg
    FrameNet Entities and Relations

    • Frames

      • Background

      • Lexical

    • Frame Elements (Roles)

    • Binding Constraints

      • Identify

    • ISA(x:Frame, y:Frame)

    • SubframeOf (x:Frame, y:Frame)

    • Subframe Ordering

      • precedes

    • Annotation


    A daml oil frame class l.jpg
    A DAML+OIL Frame Class

    <daml:Class rdf:ID="Frame">

    <rdfs:comment> The most general class </rdfs:comment>

    <daml:unionOf rdf:parseType="daml:collection">

    <daml:Class rdf:about="#BackgroundFrame"/>

    <daml:Class rdf:about="#LexicalFrame"/>

    </daml:unionOf>

    </daml:Class>

    <daml:ObjectProperty rdf:ID="Name">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="&rdf-schema;#Literal"/>

    </daml:ObjectProperty>


    Daml oil frame element l.jpg
    DAML+OIL Frame Element

    <daml:ObjectProperty rdf:ID= "role">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="&daml;#Thing"/>

    </daml:ObjectProperty>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="frameElement">

    <daml:samePropertyAs rdf:resource="#role"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="FE">

    <daml:samePropertyAs rdf:resource="#role"/>

    </daml:ObjectProperty>


    Fe binding relation l.jpg
    FE Binding Relation

    <daml:ObjectProperty rdf:ID="bindingRelation">

    <rdf:comment> See http://www.daml.org/services </rdf:comment>

    <rdfs:domain rdf:resource="#Role"/>

    <rdfs:range rdf:resource="#Role"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="identify">

    <rdfs:subPropertyOf rdf:resource="#bindingRelation"/>

    <rdfs:domain rdf:resource="#Role"/>

    <daml-s:sameValuesAs rdf:resource="#rdfs:range"/>

    </daml:ObjectProperty>


    Subframes and ordering l.jpg
    Subframes and Ordering

    <daml:ObjectProperty rdf:ID="subFrameOf">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="#Frame"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="precedes">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="#Frame"/>

    </daml:ObjectProperty>


    Talk outline110 l.jpg
    Talk Outline

    • FrameNet

    • A DAML + OIL Representation of FrameNet

    • An Example: Encoding the Criminal Process Frame

    • Applications of FrameNet.

    • Summary and Future Work



    The criminal process frame in daml oil l.jpg
    The Criminal Process Frame in DAML+OIL

    <daml:Class rdf:ID="CriminalProcess">

    <daml:subClassOf rdf:resource="#BackgroundFrame"/>

    </daml:Class>

    <daml:Class rdf:ID="CP">

    <daml:sameClassAs rdf:resource="#CriminalProcess"/>

    </daml:Class>


    Daml oil representation of the criminal process frame elements l.jpg
    DAML+OIL Representation of the Criminal Process Frame Elements

    <daml:ObjectProperty rdf:ID="court">

    <daml:subPropertyOf rdf:resource="#FE"/>

    <daml:domain rdf:resource="#CriminalProcess"/>

    <daml:range rdf:resource="&CYC;#Court-Judicial"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="defense">

    <daml:subPropertyOf rdf:resource="#FE"/>

    <daml:domain rdf:resource="#CriminalProcess"/>

    <daml:range rdf:resource="&SRI-IE;#Lawyer"/>

    </daml:ObjectProperty>


    Fe binding constraints l.jpg
    FE Binding Constraints Elements

    <daml:ObjectProperty rdf:ID="prosecutionConstraint">

    <daml:subPropertyOf rdf:resource="#identify"/>

    <daml:domain rdf:resource="#CP.prosecution"/>

    <daml-s:sameValuesAs rdf:resource="#Trial.prosecution"/>

    </daml:ObjectProperty>

    • The idenfication contraints can be between

      • Frames and Subframe FE’s.

      • Between Subframe FE’s

    • DAML does not support the dot notation for paths.


    Criminal process subframes l.jpg
    Criminal Process Subframes Elements

    <daml:Class rdf:ID="Arrest">

    <rdfs:comment> A subframe </rdfs:comment>

    <rdfs:subClassOf rdf:resource="#LexicalFrame"/>

    </daml:Class>

    <daml:Class rdf:ID="Arraignment">

    <rdfs:comment> A subframe </rdfs:comment>

    <rdfs:subClassOf rdf:resource="#LexicalFrame"/>

    </daml:Class>

    <daml:ObjectProperty rdf:ID="arraignSubFrame">

    <rdfs:subPropertyOf rdf:resource="#subFrameOf"/>

    <rdfs:domain rdf:resource="#CP"/>

    <rdfs:range rdf:resource="#Arraignment"/>

    </daml:ObjectProperty>


    Specifying subframe ordering l.jpg
    Specifying Subframe Ordering Elements

    <daml:Class rdf:about="#Arrest">

    <daml:subClassOf>

    <daml:Restriction>

    <daml:onProperty rdf:resource="#precedes"/>

    <daml:hasClass rdf:resource="#Arraignment"/>

    </daml:Restriction>

    </daml:subClassOf>

    </daml:Class>


    Daml oil cp annotations l.jpg
    DAML+OIL CP Annotations Elements

    <fn:Annotation>

    <tpos> "36352897" </tpos>

    <frame rdf:about ="&fn;Arrest">

    <time> In July last year </time>

    <authorities> a German border guard </authorities>

    <target> apprehended </target>

    <suspect>

    two Irishmen with Kalashnikov assault rifles.

    </suspect>

    </frame>

    </fn:Annotation>


    Current status of daml encoding l.jpg
    Current Status of DAML Encoding Elements

    • All FrameNet 1 data is available in DAML+OIL

      • annotations

      • frame descriptions.

    • The translator has also been updated to handle the more complex semantic relations (both frame and frame element based) in FrameNet 2.

    • We plan to release both the XML and the DAML+OIL versions of all FrameNet 2 releases.


    Talk outline120 l.jpg
    Talk Outline Elements

    • FrameNet

    • A DAML + OIL Representation of FrameNet

    • An Example: Encoding the Criminal Process Frame

    • Applications of FrameNet.

    • Summary and Future Work


    Framenet for applications121 l.jpg
    FrameNet for Applications Elements

    • Semantic Web (http://www.semanticweb.org)

      • FN database in DAML+OIL (http://www.ai.sri.com/~narayana/frame-desc.daml)

    • Semantic Extraction using FrameNet

      • Or can FrameNet be automated

  • Frame Simulation and Inference

    • Translation from frame structure to a simulation based inference tool (KarmaSIM)

      • (COLING 2002)


  • Semantic extraction l.jpg
    Semantic Extraction Elements

    • Behrang Mohit and Srini Narayanan

      • HLT-NAACL 2003.


    Enhancing ie techniques l.jpg
    Enhancing IE Techniques Elements

    • IE techniques currently use no inference (mostly!)

      • Robert Pickett was charged with felony possession of a handgun and sentenced to 5 years in a federal prison.

        • Says Pickett was arrested

    • Frame-based inferences can be useful for a variety of applications including individual/topic tracking, bridging inferences/co-reference resolution.

    • FrameNet subframe structure and bindings can be exploited for this purpose.


    A simulation semantics for inference l.jpg
    A Simulation Semantics for Inference Elements

    • Frame Structure and bindings specify parameters for a simulation/enactment of the event

    • Based on previous work (IJCAI 99, AAAI 99, CogSci 2000, COLING 2002, WWW 2002)

      • using an “X-schema” based representation, we simulate the temporal and inferential structure of the Frame-Element and Frame/Subframe relations from FrameNet.

      • Direct translation from both the mySQL FN database and the DAML+OIL representation


    Reasoning about events for nl applications qa nlu l.jpg
    Reasoning about Events for NL applications (QA, NLU) Elements

    • Reasoning about dynamics

      • Complex event structure

        • Multiple stages, interruptions, resources, framing

      • Evolving events

        • Conditional events, presuppositions.

      • Nested temporal and aspectual references

        • Past, future event references

      • Metaphoric references

        • Use of motion domain to describe complex events.

    • Reasoning with Uncertainty

      • Combining Evidence from Multiple, unreliable sources

      • Non-monotonic inference

        • Retracting previous assertions

        • Conditioning on partial evidence


    Previous work l.jpg
    Previous work Elements

    • Models of event structure that are able to deal with the temporal and aspectual structure of events

    • Models frame-based and metaphoric inference about event structure.

    • Based on an active semantics of events and a factorized graphical model of complex states.

      • Models event stages, embedding, multi-level perspectives and coordination.

      • Event model based on a Stochastic Petri Net representation with extensions allowing hierarchical decomposition.

      • State is represented as a Temporal Bayes Net (T(D)BN).

      • The Event-State representation requires branching time bayes nets with synchronization or Coordinated Bayes Nets (CBN)


    States l.jpg
    States Elements

    • Factorized Representation of State uses Dynamic Belief Nets (DBN’s)

      • Probabilistic Semantics

      • Structured Representation


    States and domain knowledge l.jpg
    States and Domain Knowledge Elements

    • Factorized Representation using Dynamic Belief Nets (DBN’s)

      • Probabilistic Semantics

      • Structured Representation


    Active event representations l.jpg
    Active Event Representations Elements

    • Actions and events are coded in active representations called x-schemas which are extensions to Stochastic Petri nets.

    • x-schemas are fine-grained and can be used for monitoring and control as well as for inference.

    • Badler’s (U Penn) group uses same idea for commanding simulated robots (Jack). Nils Nilsson (SU) uses a similar idea for robot planning called Teleo-Reactive programs.

    • Semantic basis for DAML-S, process descriptions of the Semantic Web


    Compositional primitives l.jpg

    inputs Elements

    (conditional) outputs

    preconditions

    (conditional) effects

    composedBy

    control

    constructs

    sequence

    while

    ...

    If-then-else

    fork

    Compositional Primitives

    process

    atomic

    process

    composite

    process


    Sequence p1 p2 l.jpg

    Done(P1) Elements

    Ready

    start

    finish

    Atomic

    Process

    P1

    Atomic

    Process

    P2

    Done(P1;P2)

    Sequence: P1;P2


    Fork p1 p2 l.jpg

    Done(P1 Elements|| P2)

    Ready(P1)

    Ready(P2)

    Fork: P1|| P2

    start

    finish

    Atomic

    Process

    P1

    Atomic

    Process

    P2


    Concurrent sync l.jpg

    Done(P2) Elements

    Done(P1)

    start

    finish

    Atomic

    Process

    P1

    Ready(P1)

    Atomic

    Process

    P2

    Ready(P2)

    Concurrent-Sync


    Implementation l.jpg
    Implementation Elements

    DAML-S translation to the modeling environment KarmaSIM[Narayanan, 97] (http://www.icsi.berkeley.edu/~snarayan)

    Basic Program:

    Input: DAML-S description of Frame relations

    Output: Network Description of Frames in KarmaSIM

    Procedure:

    • Recursively construct a sub-network for each control construct. Bottom out at atomic frame.

    • Construct a net for each atomic frame

    • Return network


    A precise notion of contingency relations l.jpg
    A Precise Notion of Contingency Relations Elements

    Activation:

    Executing one schema causes the enabling, start or continued execution of another schema. Concurrent and sequential activation.

    Inhibition:

    Inhibitory links prevent execution of the inhibited x-schema by activating an inhibitory arc. The model distinguishes between concurrent and sequential inhibition, mutual inhibition and aperiodicity.

    Modification:

    The modifying x-schema results in control transition of the modified xschema. The execution of the modifying x-schema could result in the

    interruption, termination, resumption of the modified x-schema.


    Results of model l.jpg
    Results of Model Elements

    • Captures fine grained distinctions needed for interpretation

      • Frame-based Inferences (COLING02)

      • Aspectual Inferences (Cogsci98, CogSci01, IJCAI 99, CL03)

      • Metaphoric Inferences (AAAI99)

      • Biological Evidence (CogSci03, BL03)

    • Sufficient Inductive bias for verb learning (Bailey97, CogSci99), construction learning (Chang03, to Appear)

    • Model for DAML-S(ISWC02, WWW02, Computer Networks 03)


    Distributed operational dope semantics l.jpg
    Distributed OPErational Elements(DOPE) Semantics

    Maps Situation Calculus action axiomatization to CBN Formalism [Narayanan 99, NM2002, NM2003]

    Features of CBN representation

    • Can deal with quantitative information & resources

    • Natural representation of stochastic actions (selection and effects)

    • Variety of well established analysis and simulation techniques including mappings to other logics of change.

    • Natural representation of change, concurrency, and synchronization

    • Execution semantics


    Problems with t d bn l.jpg
    Problems with T(D)BN Elements

    • Scaling up to relational structures

    • Supports linear (sequence) but not branching (concurrency, coordination) dynamics


    Structured probabilistic inference l.jpg
    Structured ElementsProbabilistic Inference


    Probabilistic inference for events l.jpg
    Probabilistic inference for Events Elements

    • Filtering

      • P(X_t | o_1…t,X_1…t)

      • Update the state based on the observation sequence and state set

    • MAP Estimation

      • Argmaxh1…hnP(X_t | o_1…t, X_1…t)

      • Return the best assignment of values to the hypothesis variables given the observation and states

    • Smoothing

      • P(X_t-k | o_1…t, X_1…t)

      • modify assumptions about previous states, given observation sequence and state set

    • Projection/Prediction/Reachability

      • P(X_t+k | o_1..t, X_1..t)


    Open source framenet l.jpg
    Open-Source FrameNet Elements

    • Use the idea of open source Linux development

      • Frame hackers around the world

      • Distributed vanguard and peer review process

      • Pilot projects in large social networks (ICSI BCIS project)

    • Develop software and infrastructure

      • Frame Creation and Modification

      • Annotation structures

      • Common API for semantic resources.

      • Specialized domain FrameNets


    Summary l.jpg
    Summary Elements

    • The FrameNet Project is making good progress toward our goal of producing a lexicon for a significant number of English words with uniquely detailed information about their argument structure and the semantics associated with it.

    • We have an automatic translation from FrameNet to computational representations that

      • Are able to translate FN annotations and frame structure for use by Semantic Web researchers and use ontologies on the web for semantic typing of FE’s.

      • Translates Frame representations to a simulation semantics that can perform frame-based inference and may provide a scalable semantics for NL systems.


    Ongoing work question answering l.jpg
    Ongoing Work: Question Answering Elements

    • As part of the AQUAINT program (UCB, ICSI, Stanford), we are tasked with

      • coming up with a uniform formalism to encode frames, schemas and metaphors (ScaNaLU 2002)

      • Designing inference algorithms to reason with semantic schemas.

      • Others (UCB, Stanford) are tasked with trying to identify semantic relations from text.

      • One possible interchange language choice is DAML-S/OWL-S

    • Hypothesis: Simulation based inference over semantic relations is useful for question answering.


    Slide154 l.jpg

    http://www.icsi.berkeley.edu/NTL Elements

    http://www.icsi.berkeley.edu/framenet


    Slide155 l.jpg

    http://www.icsi.berkeley.edu/NTL Elements

    http://www.icsi.berkeley.edu/framenet