Frame Semantics,Constructions, and the

FrameNet Lexical DatabaseCollin F.Baker

International Computer Science Institute

Berkeley, California

Outline of Course

• Background

• What is a semantic frame?

• Representing FS Concepts

• The Lexicographic Process

• Full text annotation

• Automation

• Related projects

Background

Fillmore on Case Grammar

• 1968. The Case for Case

• 1969. Towards a Modern Theory ofCase

• 1977. The Case for Case Reopened

• S is underlyingly V + {Ncase1, Ncase2, …}• Saliency hierarchy ⇒ foregrounding

• Case hierarchy ⇒ grammatical function

Fillmore on FrameSemantics

• 1976 Frame semantics and the natureof language

• 1977 Scenes-and-frames semantics

• 1983 Frame semantics

• 1985 Frames and the Semantics ofUnderstanding

Construction Grammar

• Only 1 type of entity: constructions• Construction is pairing of form and meaning

(Saussure’s sign)• Words, MWEs are lexical constructions• Non-lexical constructions: Subject-predicate,

left isolation (“extraction”), modification• mono-stratal, no deep vs. surface• examples from What’s X doing Y? (Kay and

Fillmore 1999)

A Non-lexical Construction

A Lexical Construction

Embodied Conx Grammar

• http://www.icsi.berkeley.edu/NTL

• Outgrowth of Neural Theory of Languagegroup (neé L-zero)

• John Bryant, et al. (forthcoming) CognitiveLinguistics-- linguistic side

• Jerome Feldman From Molecule to Metaphor-- CogSci side

• How can a brain, made up of neurons andconnections between them, give rise tothought and language?

The FrameNet Project

• Creating a highly detailed lexicon ofEnglish predicators based on FrameSemantics

• Documenting their valences bymanually annotating corpus examples

• Human- and machine-readable output

• Full data athttp://framenet.icsi.berkeley.edu

What is a semantic frame?

What do you annotate?

Semantic Frames• Frame: Semantic frames are schematic

representations of situations involving variousparticipants, props, and other conceptualroles, each of which is called a frameelement (FE)

• These include events, states, and relations• What were called in earlier work on Frame

Semantics “scenes” and “scenarios” are allrepresented in FrameNet by one data type,the frame.

• Frames are connected to each other viaframe-to-frame relations

Frame Elements (FEs)• Frame Element (FE): The participants, props

and roles of a frame. These can includeagents, inanimate objects, and elements ofthe setting.

• The syntactic dependents (broadly construed)of a predicating word correspond to the frameelements of the frame (or frames) associatedwith that word.

• Each FE is defined relative to a single frame.Any connections between FEs have to bemade explicitly.

Lexical Unit (LU)• The pairing of a lemma with a meaning; a

word sense. The meaning is partiallyexpressed by the relation between the lemmaand a FN frame, i.e. between lexical form(s)and the semantic frame they evoke.

• Includes inflected forms see, saw seen• Includes multi-word expressions (MWEs):

pick out, lose sleep, spitting image, familypractitioner

• May be any part of speech: verbs, nouns,adjectives, prepositions, etc.

LU Definitions

• In addition to the connection to theframe, the FN database also includes adefinition of each LU.

• The LU definition is human-readable(we hope), similar to a dictionarydefinition, and represents aspects ofmeaning finer than the framedistinctions.

Placing Frame: Definition

• Generally without overall (translational)motion, an Agent places a Theme at alocation, the Goal, which is profiled. In thisframe, the Theme is under the control of theAgent/Cause at the time of its arrival at theGoal.

• This frame differs from Filling in that itfocuses on the Theme rather than the effecton the Goal entity. It differs from Removing infocusing on the Goal rather than the Sourceof motion for the Theme.

Placing Frame: FEs

• Agent/Cause

• Goal

• Theme

• Area

• Cotheme

• Degree

• Distance

• Manner

• Means

• Place

• Purpose

• Time...

Placing Frame: LUs

• archive.v, arrange.v, bag.v, bestow.v, billet.v,bin.v, bottle.v, box.v, brush.v, cage.v, cram.v,crate.v, dab.v, daub.v, deposit.v, drape.v,drizzle.v, dust.v, embed.v, file.v, garage.v,hang.v, heap.v, immerse.v, implant.v, inject.v,insert.v, ... put.v, rest.v, rub.v, set.v,sheathe.v, shelve.v, shoulder.v, shower.v,sit.v, situate.v, smear.v, sow.v, stable.v,stand.v, stash.v, station.v, stick.v, stow.v,stuff.v, tuck.v, warehouse.v

• Many of these incorporate the Goal FE

Placing Frame: Annotation

• [European heads of government ...AGENT] showered [telegrams ofcongratulation THEME] [on Clinton GOAL],saying...

• [I AGENT] plunged [my hands THEME][wrist deep DISTANCE] [in the fragrantherbs GOAL]...

The Revenge Frame

Revenge Frame: Definition

• This frame concerns the infliction of punishment inreturn for a wrong suffered. An Avenger performs aPunishment on a Offender as a consequence of anearlier action by the Offender, the Injury.

• The Avenger inflicting the Punishment need not bethe same as the Injured_Party who suffered theInjury, but the Avenger does have to share thejudgment that the Offender's action was wrong.

• The judgment that the Offender had inflicted an Injuryis made without regard to the law.

Revenge Frame: FEs

• Core:

– Avenger

– Injured_party

– Injury

– Offender

– Punishment

• Non-Core:

– Place

– Time

– Degree

– Manner

– Purpose, ...

Core FEs

• Inherent in the concept of the frameitself

• The most frame-specific FEs; core FEsdifferences serve to differentiate frames

• Must be annotated for every instance ofthe frame

Peripheral FEs

• Ontologically necessary, but not reallyframe-specific

• Often presupposed, deprofiled

• Bindings to high-level frames– Event ->Place, Time

– Intentionally_act -> Reason

Extra-thematic FEs

• Not actually part of the frame inquestion; �the same across frames

• Beneficiary, description, etc.

• Really amount to evocation of aseparate frame

• But appear frequently in sentences,should be annotated at the same time

Revenge Frame: LUs

• avenge.v, get_back.v, get_even.v,payback.n, retaliate.v, retaliation.n,retribution.n, retributive.a, retributory.a,revenge.n, revenge.v, revengeful.a,sanction.n, vengeance.n, vengeful.a,vindictive.a

• AND avenger.n, revenger.n

Revenge Frame: Annotation

• 1. [They AVENGER] took revenge [for thedeaths of two loyalist prisoners INJURY]

• 2. The next day, [the Roman forces AVENGER]took revenge [on their enemies OFFENDER]...]

• 3. [The ban PUNISHMENT] [is Cop] [PrinceCharles's AVENGER] revenge [for her refusal tospend Christmas with the rest of the royals...

INJURY]

Text Frame: Definition

• A Text is an entity that containslinguistic, symbolic information on aTopic, created by an Author at theTime_of_creation. It may be a physicalentity that is made of a certain Materialetc. It may be constructed for anHonoree.

Text frame: FEs

• Core– Text

• Non-core– Author

– Components

– Containing_text

– Genre

– Honoree

– Material

– Medium

– Time_of_creation

– Topic

– Use

Text Frame: LUs

• account.n, article.n, autobiography.n, ballad.n,benediction.n, biography.n, book.n, booklet.n,bulletin.n, chronicle.n, comedy.n, diary.n,drama.n, edition.n, editorial.n, elegy.n, epic.n,epigram.n, epilogue.n, epistle.n, essay.n,eulogy.n, exemplum.n, fable.n, fanzine.n,festschrift.n, fiction.n, ... sermon.n, song.n,sonnet.n, speech.n, spellbook.n, tetralogy.n,thriller.n, tome.n, tract.n, tractate.n, tragedy.n,treatise.n, trilogy.n, volume.n, whodunit.n,writings.n

Text Frame: Annotation

• [Shusaku Endo's AUTHOR] novel [of ideas TOPIC]can be read as symbolism or old-fashionedrealism...

• [Frodo's AUTHOR] elegy [for Gandalf HONOREE]ends on the word “died”...

• Has he seen the excellent [Glasgow EveningTimes CONTAINING TEXT] article [of 20 NovemberTIME OF CREATION] which...

• [Its AUTHOR] bulletin, [Christian MeditationNewsletter TITLE], containing ...

Apply heat Frame: Definition

• A Cook applies heat to Food, where theTemperature_setting of the heat andDuration of application may be specified. AHeating_instrument, generally indicated bya locative phrase, may also be expressed.Some cooking methods involve the use of aMedium (e.g. milk or water) by which heat istransferred to the Food. A less semanticallyprominent Food or Cook is markedCo_participant.

Apply heat frame: FEs

• Core– Container

– Cook

– Food

– Heating instrument

– Temperature setting

• Non-core– Co-participant– Degree– Duration– Manner– Means– Medium– Place– Purpose– Time

Apply heat frame: LUs

• bake.v, barbecue.v, blanch.v, boil.v,braise.v, broil.v, brown.v, char.v,coddle.v, cook.v, deep fry.v, fry.v, grill.v,microwave.v, parboil.v, poach.v, roast.v,saute.v, scald.v, sear.v, simmer.v,singe.v, steam.v, steep.v, stew.v,toast.v

Apply heat frame: Annotation

• 1. Bake [the souffles FOOD] [for 12minutes DURATION]. [CNI COOK] [INI HEATING

INSTRUMENT]

• [They COOK] boil [them FOOD] [in an ironsaucepan CONTAINER].

Null Instantiation• CNI (“constructional null instantiation”)

grammatically licensed– e.g.,missing subject of imperative

Please leave the room.• INI (“indefinite null instantiation”)

existential; lexically licensed– e.g., missing objects of some activity verbs

I’ve been baking all afternoon.• DNI (“definite null instantiation”)

anaphoric; lexically licensed– e.g., omitted complements of some cognitive

verbs She already knows.

The art of frame definition• Not a science, but not haphazard, either

• Try to make useful generalizationsacross reasonable-sized groups

• FN proceeds frame by frame rather thanlemma by lemma

• All LUs in a frame have the same FEs,with the same profiling / coreness

• Respect stative / inchoative / causativedistinction

The Lexicographic Process

• Vanguarding

• Subcorporation

• Annotation

• Reports and data distribution

“Vanguarding”

• Frames, FEs, and LUs defined–corpus, otherresources

• Rules written to extract examples of allsyntactic patterns, some collocations

• Rules can include commands to pre-markFEs on chunks

• “Other” subcorpus for unforeseen patterns

Subcorporation

• Non-interactive, command-line process

• Down-sample when necessary for verycommon lemmas

• Subcorpora produced by matchingchunked sentences against rules– Slow

– Parse-specific

– Complex, cascaded filter

Annotation

• Annotator chooses “good_ examples ofeach lexicographically relevantsyntactic pattern (“alternations”)

• All core FEs found are annotated foreach sentence

• If the predicator is a verb, core FEs notexpressed in the sentence areannotated as “null instantiated_

Annotation

• Each annotation set contains labels onmultiple layers: FE, GF, PT, etc.

• GF and PT added semi-automatically

• Process allows multiple targets(annotation sets) per sentence (notneeded for lexicographic work)

• Goal is ~15-20 clear examples / LU

Retaliate: Annotation report

Retaliate: Lexical entry report

Representing FrameSemantic Concepts

Frame-to-frame relations

1. Inheritance

2. Perspective on

3. Using

4. Subframe

5. Precedes

6. Causative of

7. Inchoative of

8. See also

FE-to-FE relations acrossframes

• Every frame relation (except See also)is accompanied by one or more FE-to-FE relations.

• At the moment, there is only one type ofFE-FE relation, which is “subtype of”(possibly improper).

• This may change.

FrameGrapher

Inheritance

• All FEs of parent are bound to FEs ofchild

• Child FEs need not have same name

• Child can have more FEs

• Child semantics is a subtype of parentsemantics

• Any within-frame FE-FE relations ofparent are duplicated in child

Perspective on

• Some parent FEs missing or deprofiledin child

• Commercial Transaction -- Buy, Sell

• Employment -- Hiring, Getting a job

Using

Subframe and Precedes

Causative of and Inchoative of

1. …others BOUND theforelegs, and the hind legs,together with rope.

2. Recombinant Oct-11 proteinBINDS specifically to anoctamer sequence…

3. …the BOUND protein candetach from the protectedDNA fragment andreassociate to other DNAfragments…

Causative of

Inchoative of

See also

• This is not a profoundly ontologicalrelationship, it’s more like “see also” ina print dictionary, alerting the reader toanother frame that seems similar

• Such links allow us to describedifferences between a set of similarframes in just one place.

What happened to thematicroles?

• They correspond fairly well to the FEs oftop-level frames:– Event:

– Act

– Intentionally act:

• Some also correspond fairly well tosemantic types on FEs

Traditional thematic rolesdon't fit some frames

• Similarity-- NB two ways of expressingthis:– reciprocally (The children are very similar

(to each other)) or

– unequally (John resembles his father.

– NB: ??His father resembles john.

• Not an event but a state; event-typeroles don’t apply

Traditional roles don’t fit (2)

• Causative replacing– The coach replaced [Smith OLD] with

[Jones NEW]

• Inchoative replacing– [France NEW] replaced [Brazil OLD] as

world champions.

FE-to-FE relations withinframes

• Excludes: 1 FE excludes another

• Requires: 1 FE requires the presence ofanother.

• [The children PARTICIPANTS] are verysimilar.

• [John FIGURE] is similar to [his fatherGROUND].

FE-to-FE relations withinframes (2): Coresets

• Any one of a set of FEs is sufficient fora pragmatically "complete” sentence

• She ran [here GOAL] [from the bus-stopSOURCE]

• She ran [for one kilometer DISTANCE]

• She ran [along the canal PATH]

• ?She ran. (OK in answer to the questionHow did she get here?...)

*LU-to-LU relations

• Implied relation of inter-substitutabilityfor all LUs within a frame

• But more true of some frames thanothers

• Positive and negative words in sameframe, marked with semantic types

• Use WordNet for most such relations

FN Semantic Types

• Ontological types--– relate to ontologies, WN hierarchy but

data-driven

– mostly on FEs

• Lexical types -- on LUs (pos/ neg)

• Framal types-- theory-internal e.g.“non-lexical frame”

Framal Semantic Types

Full text annotation

Deeper text understanding

• Although the main work of FN has beenlexicographic, the ultimate goal hasalways been deep understanding of fulltexts (Fillmore and Baker 2001)

• One annotation set for each frame-evoking expression

In October 2002,the U.S. State Department informed North Korea that the U.S. was aware of this program, andregardsit as a violation of Pyongyang's nonproliferation commitments.

Telling.inform

Telling.inform

In 2002,Time

that the U.S. was aware of this program ,and regards it as a violation of Pyongyang'snonproliferation commitments

Message

North KoreaAddressee

INFORMEDTarget

the U.S. State DepartmentSpeaker

Inform

• The meaning of inform that we wish to describebelongs to a Telling frame; here the emphasis is ongetting information to an addressee, and is thusdistinct from Saying.– The Telling frame is shared by inform, tell, notify, etc.,

Saying is shared by say, announce, state, whisper, etc.

• The meaning of inform in the Telling frame is distinctfrom the sense it has as a member of the Reportingframe, where it occurs as part of a phrasal verb,inform on. Other members of this frame are report(they reported me to the authorities), tell on, rat on,fink on.

Annotations withTelling.inform

Other patterns withTelling.inform?

• Those examples showed just one of the syntacticpatterns available to inform in the Telling frame:simple active sentences with a that-clauseexpressing the Message. If the Message isexpressed with a NP, the preposition of can beselected.– Passive with that: Were [you] informed [that

Shelly has left home]?– Active with of: [I] already informed [you] [of my

decision].– Passive with of: Had [you] been informed [of the

details]?• Other possibilities include on, about, and as to.

In October 2002,the U.S. State Department informed North Korea that the U.S. was aware of this program, andregardsit as a violation of Pyongyang's nonproliferation commitments.

Awareness.aware

Awareness.aware

of this programContent

was AWARETARGET

the U.S.Cognizer

Aware

• The adjective aware is assigned to the Awarenessframe, which it shares with cognizant, conscious anda number of verbs (know, realize, ...) and nouns(awareness) that signal a relation between aCognizer and a Content. As with Telling.inform, theContent (corresponding to Message) can beexpressed as a clause or as a NP:

– Are [you] aware [of our crisis]?

– Are [you] aware [that we are having a crisis]?

In October 2002,the U.S. State Department informed North Korea that the U.S. was aware of this program, andregardsit as a violation of Pyongyang's nonproliferation commitments.

Categorization.regard

Categorization.regard

as a violation of Pyongyang'snonproliferation commitments

Category

itItem

REGARDSTARGET

the U.S.Cognizer

Regard

• Regard, in the Categorization frame, isused to express the idea of assigningsome Item to a Category. Its framepartners include consider, classify,categorize and a few others.

In October 2002,the U.S. State Department informed North Korea that the U.S. was aware of this program, andregardsit as a violation of Pyongyang's nonproliferation commitments.

Compliance.violation

Compliance.violation

of Pyongyang's nonproliferationcommitments

Norm

a VIOLATIONTarget

itAct

Violation

• Violation, in the sense we have in mind, belongs tothe negative-response set of lexical units in theCompliance frame.

• Alongside of X violated the rule we have X is aviolation of the rule and X is in violation of the rule.

• The X in these formulas can stand for– the Protagonist (we),– the Act (what we did), or– the State_of_affairs (our situation).

In October 2002,the U.S. State Department informed North Korea that the U.S. was aware of this program, andregardsit as a violation of Pyongyang's nonproliferation commitments.

Commitment.commitment

Commitment.commitment

COMMITMENTS.Target

nonproliferationMessage

Pyongyang'sSpeaker

Commitment

• The noun commitment in the intended sense belongsto the Commitment frame.

• Other words sharing this frame include commit, vow,oath, swear, promise, covenant (some are verbs,some are nouns, some are both).

• All of them have to do with illocutionary acts that bindtheir speakers to a course of action.

• This sense of commitment takes the support verbmake.– Are you afraid to make a commitment?– You’ve made a commitment now, so you’d better honor it.

Support Verbs and Polysemy

Commitment also occurs in theInstitutionalization frame: committing a person to amental hospital. That meaning does not welcomethe support verb make.

The verb commit itself is a support verb forcrimes and sins: to commit murder is the same asto murder. But this use of commit has commission,not commitment, as its nominalization!

Full Text Annotation

• Text annotation is relatively recent taskfor FN– 5 texts from PropBank, for comparison with

PB annotation (NSF subcontract)

– 15 texts from NTI website-- CNS countryprofiles, etc. (AQUINAS)

• Must deal with syntactic complexity, etc.

Lexicography vs. Full Text

• Lexicography: Need to fill out frames, fullyexemplify LUs, create training data formachine learning

• Text annotation: Need to create new frames,LUs quickly, maximally reuse, fewerexamples of rare LUs, more of common ones

• Created new text-annotation form to speedup finding same LU across text(s)

PropBank• Closest annotation project to FrameNet in

many ways• Starts from Penn TreeBank, mainly WSJ• Annotates arguments and adjuncts of verbs,

with labels like Arg0, Arg1, ArgM• These general labels are given definitions

specific to each word sense• Some generalization across LUs, but no true

semantic frames• Now supplemented with NomBank for nouns

PropBank - WordNet -FrameNet Comparison

• Felipe Gonzalez election victory– (live)

– Capture 1

NIs and anaphora resolution

• The United Nations said Somaligunmen who had hijacked a U.N.-chartered vessel carrying food aidto tsunami victims released the shipafter holding it for more than twomonths.

• Words in orange are in FN

FN is not the only resourceyou need for NLP…

• Need treatment of negation or quantification

• Need mechanism for syntactic/ semanticcomposition

• Need discourse connectives

• Need specialist terms, noun taxonomies(NER)

• Need recognizers for time expressions,numbers, etc.

Automation

orCan’t this be done by a machine?

Some parts just can’t be…

• We are not basing our work on existingsense inventories; we want to be reallyaccurate about the number and natureof the participants in each frame.

• Unsupervised learning can only takeyou so far; we believe human judgmenthas to be part of the mix.

Frame assignment as WSD

• Title of paper by K. Erk (2005)

• Same basic problems as for machine-learning WSD-- insufficient data, inter-annotator agreement

• Same methods, variety of classificationalgorithms, well-researched

Automatic semantic rolelabeling (ASRL)

• Gildea & Jurafsky (2002)• Thompson, Levy and Manning (2003) --

generative HMM model

• Fleishman and Hovy (2003) “MaximumEntropy Approach to FrameNet Tagging

• Senseval 3 ASRL task (Litkowsky 2004)

Shalmaneser

• Erk & Padó 2006 LREC (“ShallowSemantic Parsing⇠”)

• FRED-frame assignment = supervisedWSD, Naïve Bayes classifier, MLE

• ROSY-role labeling, 1 or 2-step

• Trained on English, German

• Out of the box or experimental use,substituting components

Frame Induction• Rebecca Green’s Ph.D. thesis, U

Maryland, Green & Dorr 2004, 2005

• Used WordNet, LDOCE to find clustersof Vs, then clusters of associated Ns

• Induced prospective frames and namesfor frames and FEs in them

• Roughly 1500 frames, extensive lists ofwords in frames, but need more work

• Tested on text tiling task--small butsignificant improvement

"Rapid Vanguarding"

• Using principles of Kilgarriff's WordSketch Engine/ WASPbench

• WSE provides filtered summary ofcorpus data www.sketchengine.co.uk

• WASPbench allows grouping intoword senses (running at ITRI,Brighton)

Sample Word Sketch

Bootstrapping annotation

• We are close to situation in whichoutput of Shalmaneser can be input toFN annotation, output of annotation canbe training data for Shalmaneser

• Need to do error analysis, selectivesampling to create best training data

• Plan to create large corpus with FNannotation of differing quality-- platinum,gold, silver, …

Applications

Information Extraction

• Mohit and Narayanan 2003 HLT-NAACL “Semantic Extraction with Wide-Coverage Lexical Resources”

• IE using GATE on 100 crime stories

• Used FN frames as “seeds” for IEpatterns

• Walked WN hierarchy to increasecoverage

Question Answering

• Narayanan and Harabagiu 2004 Coling

• Analyse predicate argument structs andsemantic frames in qns and answers

• Coordinated Probablistic RelationalModel of events

• Probabalistic inference from extractedevents

Textual Entailment

• Aljoscha Burchardt and AnetteFrank 2006 (conference?)“Approximating Textual Entailment withLFG and FrameNet Frames”

• LFG f-struct -> FN roles

• PASCAL RTE challenge

• Computing match graphs for text andhypothesis

Modeling SentenceProcessing

• Ulrike Padó 2006 (conference?)

• Semantic/syntactic “garden path”– The hunter shot by the teenager was quite

young.

– The deer shot by the hunter was trulyimpressive.

• FN/PB comparison– PB better coverage

– FN better modeling

Related projects

The SALSA Project

• Manfred Pinkal, Universität desSaarlandes

• Starts from TIGER German treebank

• Adds FrameNet labels to constituents

• Own annotation tool, TIGER XML

• Where FN frames do not yet exist,creates “unknowns”, annotatessomewhat like PropBank

FrameNets in otherLanguages

• Spanish FrameNet: Carlos Subirats, UAutónoma, Barcelonahttp://gemini.uab.es/SFN

• Japanese FrameNet: Kyoko Ohara,Keio U (and others from Tokyo Univ)http://jfn.st.hc.keio.ac.jp

• German FrameNet: Hans C. Boas, UTexas, Austinhttp://gframenet.gmc.utexas.edu/

Spanish FrameNet

• Universidad Autónoma de Barcelona• Built corpus of 350 million words of Old and

New World Spanish• Own system for extracting sentences,

“subcorporation”• Using a copy of the FN software for

annotation• Using FN frames, with some modifications for

Spanish

Japanese FrameNet

• Own Corpus (20 million words), SearchTool

• JFN Desktop

• JFN Report System (Dynamic)

• HTML Report Creator (Static)

• Currently annotating LUs in framesrelated to Motion, Risk, Commerce,Theft, etc.

German FrameNet

• German corpora for GFN:– LDC German newspaper corpus

– Institut für Deutsche Sprache: Datenbankgesprochenes Deutsch

– Tagging with STTS

• Working on lexicon, pipeline for texts

FrameNets by Projection

• English ⇒ French: Guillaume Pitel,CNRS (Nancy)

• English ⇒ Chinese: BiFrameNet:Pascale Fung (HKUST)

• Related work: Padó and Lapata (2005)English ⇒ German, French

FrameSQL

• Prof. Hiroaki Sato, Senshu University,Kawasaki, Japan http://sato.fm.senshu-u.ac.jp/fn23/notes/

• FEs extracted from FN data, loaded into SQL database, form-based searchingover the web

• In areas where SFN has the sameframes as English, can do parallelsearches

Soccer FrameNet

• Thomas Schmidt, Ph.D. U Hamburg--ICSI post-Doc

• http://www.kicktionary.de

FN in OWL DL

• Jan Scheffczyk (Bundeswehr U, ICSIPost-doc) and Srini Narayanan (ICSI)(OntoLex 2006)

• Frame db in OWL DL, reasoning withstandard tools

• Frames, FEs = classes; annotations =instances

• Program for converting Annotation dbinto OWL DL, too big to distribute all

Linking FN to SUMO

• Jan Scheffczyk and Michael Ellsworth(ICSI) (OntoLex 2006)

• Links expressed in SUMO KIF

• Manually specified links from FNsemantic types to SUMO nodes

• Semi-automatically link roughly 1/2 ofFEs to SUMO nodes

• SUMO already linked to WN

From FN STs to SUMO nodes

Data releases

• Fifth data release “soon”-- R 1.3

• HTML for browsing and XML formachines

• OWL DL and program for generating

• Java API?

• The Book

• Hundreds of users around the world

Thanks!

• Talk to us

• http://framenet.icsi.berkeley.edu