FrameNet Meets the Semantic Web
Srini Narayanan
Charles Fillmore
Collin Baker
Miriam Petruck
Outline of Presentation
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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
• 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 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
An important goal of our work is to present
information about the words in a form that will
prove usable in various NLP applications:
1. Question Answering (Berkeley, Colorado)
2. Semantic Extraction (Berkeley, SRI, Colorado)
3. Machine Translation (San Diego State)
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
• 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
1.
2.
3.
4.
5.
6.
characterize frames
find words that fit the frames
develop descriptive terminology
extract sample sentences
annotate selected examples
derive "valence" descriptions
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
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 computerassisted.
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.
SAMPLE ANNOTATIONS
Types of Words / Frames
o
o
o
o
o
o
o
events
artifacts, built objects
natural kinds, parts and aggregates
terrain features
institutions, belief systems, practices
space, time, location, motion
etc.
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
Initial state:
Vendor has Goods, wants Money
Customer wants Goods, has Money
Transition:
Vendor transmits Goods to Customer
Customer transmits Money to Vendor
Final state:
Vendor has Money
Customer has Goods
Sample Event Frame:
Commercial Transaction
Initial state:
Vendor has Goods, wants Money
Customer wants Goods, has Money
Transition:
Vendor transmits Goods to Customer
Customer 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
Verbs:
pay, spend, cost, buy, sell, charge
Nouns:
cost, price, payment
Adjectives: expensive, cheap
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.
She bought some carrots from the greengrocer for a dollar.
Customer
Vendor
from
BUY
Goods
for
Money
She paid a dollar to the greengrocer for some carrots.
Customer
to
Vendor
PAY
Goods
for
Money
She paid the greengrocer a dollar for the carrots.
Customer
Vendor
PAY
Goods
for
Money
She spent a dollar on the carrots.
Customer
Vendor
SPEND
Goods
on
Money
The greengrocer sold some carrots to her for a dollar.
Customer
to
Vendor
SELL
Goods
for
Money
The greengrocer sold her some carrots for a dollar.
Customer
Vendor
SELL
Goods
for
Money
The greengrocer charged a dollar for a bunch of carrots.
Customer
Vendor
CHARGE
Goods for
Money
The greengrocer charged her a dollar for the carrots.
Customer
Vendor
CHARGE
Goods
for
Money
A bunch of carrots costs a dollar.
Customer
Vendor
COST
Goods
Money
A bunch of carrots cost her a dollar.
Customer
Vendor
COST
Goods
Money
It costs a dollar to ride the bus.
Customer
IT
Vendor
COST
Goods
to do X
Money
It cost me a dollar to ride the bus.
Customer
IT
Vendor
COST
Goods
to do X
Money
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
• One of the things we do is characterize such
information packets - beginning with
informal descriptions.
• We can begin with Revenge.
The Revenge frame
The Revenge frame involves a situation in
which
a) A has done something to harm B and
b) B takes action to harm A in turn
c) B's action is carried out independently of
any legal or other institutional setting
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
• 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
• 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
• 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
• We extract from our corpus examples of
sentences showing the uses of each word in
the frame.
Obviously we need to conduct a more regimented search,
grouping examples with related structures.
Examples of simple use are swamped by the idiomatic phrase
"with a vengeance".
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
• 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.
I avenged my brother.
I avenged his death.
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?
• 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
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
It's these word-by-word
• Sometimes with with
she got even with me specializations in
FE-marking that make
• Sometimes with at
automatic FE recognition
they got back at you
difficult.
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?
• Sometimes it's the injured_party
I've got to avenge my brother
• .Sometimes it's the injury
My life goal is to avenge my brother's
murder.
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.
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?
• Frames
• Lexical entries
• Annotations
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,
Sample from lexical unit list
• * 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)
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
• Revenge inherits Punishment/Reward
• Revenge uses the Hostile_encounter frame
• (see existing tentative frame hierarchy)
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 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
• 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?
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
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
• Current: 7700 Lexical Units
–
–
–
–
FN1: 1600 Lexical units
FN2: 4400 Lexical Units
Created (not yet annotated): 1280 LU
Other : in process, problems, etc.
Current Status
• 500 Frames
• 7700 Lexical Units
• 130,000 Annotated sentences
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
• 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
• 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
Some Comparisons
Is FN an ontology?
• Not exactly, but some users use FN frames
as an ontology of event types.
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?
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?
• 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.
Comparison with Dictionaries
American Heritage Dictionary
• avenge v.
1. To inflict a
punishment or penalty
in return for; revenge
2. 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
• avenge v.
1. To inflict a
punishment or penalty
in return for; revenge
2. 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);
avengeprepositionally;
The FEs of the direct objects are expressed
"in return for" marks the injury; "for" or "on behalf of" marks
the injured_party.
American Heritage Dictionary
• avenge v.
1. To inflict a
punishment or penalty
in return for [ ];
revenge
2. 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
revenge definer added qualifications
on the missing
argument, avenge definer didn't.
American Heritage Dictionary
• avenge v.
1. To inflict a
punishment or penalty
in return for; revenge
2. 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
avenge definer claims avenge and
revenge are
synonym in sense 1; the revenge definer claims avenge
and revenge are synonyms in sense 2.
American Heritage Dictionary
• avenge v.
1. To inflict a
punishment or penalty
in return for; revenge
2. 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
revenge definer included "seek vengeance", not supported
by FN examples.
American Heritage Dictionary
• avenge v.
1. To inflict a
punishment or penalty
in return for; revenge
2. 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
Both definers include "take vengeance" in their definitions, as
if that's more transparent than the simple verb.
Comparison with WordNet
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 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 to
opener";
"The
army
retaliated two
for the
Hard
figure out
whatIsraeli
motivates
distinguishing
senses;
personal
institutional?
Hamasvs.bombing")
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
Like opener
FrameNet,
theseIsraeli
entriesarmy
include
Definitions
and Hamas
Examples.
"; "The
retaliated
for the
FrameNet
limits
bombing
") its examples to attested sentences from a Corpus.
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
The WN
sentence templates
*> Somebody
----s PPare impoverished structurally
and do not indicate the semantic roles. In fact, retaliate is
wrongly described as taking a simple object.
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
The*>
identity
of the P----s
in PP
Somebody
PPis important: strike back at
marks the offender, as does retaliate against; retaliate
for marks the injury.
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
Where WordNet merely shows that the words in the
*>second
Somebody
synset can----s
occurPP
intransitively, FN would say
something about the anaphoric nature of the omitted
offender.
Comparison with ontologies
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
• 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
• 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
• 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
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 #$HeldCaptive."
Trial
comment : [[Def]] "The subcollection of
#$LegalConflict events whose instances are
heard and decided by a court and are
officiated by a #$Judge."
requiredActorSlots : [[Mon]] plaintiffs
[[Mon]] defendants
Legal activities
comment : [[Def]] "The collection of all events
performed with the purpose of enforcing laws, that
are performed by people officially charged with
this this duty. Includes most activities of law
enforcement officials (such as police) including
detection of crime, identification of offenders, and
arrests."
LawEnforcementOfficer
comment : [[Def]] "An instance of
PersonTypeByOccupation, and a specialization of
PersonWithOccupation. Each instance of
LawEnforcementOfficer is a person whose job is to detect,
stop, and/or punish people engaged in illegal activities. The
collection LawEnforcementOfficer includes members of
local, state, and special police (e.g., transit police) forces, as
well as federal agents (e.g., members of border patrols,
national security agents). Consequently, a given instance of
Law EnforcementOfficer typically also belongs to one of
the following collections: #$StateEmployee,
#$LocalGovernment Employee, or
NationalGovernmentEmployee (see Public
SectorEmployee)."
FrameNet for Applications
• 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
• FrameNet
• A DAML + OIL Representation of
FrameNet
• An Example: Encoding the Criminal
Process Frame
• Web Applications of FrameNet.
• Summary and Future Work
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
• 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
<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
<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
<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
<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 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
Frame Element
Description
Court
Defendant
The court where the
process takes place
The charged individual
Judge
The presiding Judge
Prosecution
FE indentifies the
attorneys’ prosecuting the
defendant
Attorneys’ defending the
defendant
Defense
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
<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
<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
<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
<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
<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
• 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 Outline
• FrameNet
• A DAML + OIL Representation of
FrameNet
• An Example: Encoding the Criminal
Process Frame
• Applications of FrameNet.
• Summary and Future Work
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
• Or can FrameNet be automated
• Frame Simulation and Inference
– Translation from frame structure to a simulation based
inference tool (KarmaSIM)
• (COLING 2002)
Semantic Extraction
• Behrang Mohit and Srini Narayanan
– HLT-NAACL 2003.
Enhancing IE Techniques
• 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
• 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 FrameElement 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)
• 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
• 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
• Factorized Representation of State uses
Dynamic Belief Nets (DBN’s)
– Probabilistic Semantics
– Structured Representation
States and Domain Knowledge
• Factorized
Representation
using Dynamic
Belief Nets
(DBN’s)
– Probabilistic
Semantics
– Structured
Representation
Active Event Representations
• 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
process
atomic
process
inputs
(conditional) outputs
preconditions
(conditional) effects
composite
process
composedBy
control
constructs
sequence
If-then-else
fork
while
...
Sequence: P1;P2
start
Ready
finish
Atomic
Process
P1
Done(P1)
Atomic
Process
P2
Done(P1;P2)
Fork: P1|| P2
Done(P1 || P2)
start
finish
Ready(P1)
Atomic
Process
P1
Ready(P2)
Atomic
Process
P2
Concurrent-Sync
start
finish
Ready(P1)
Atomic
Process
P1
Done(P1)
Ready(P2)
Atomic
Process
P2
Done(P2)
Implementation
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
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
• 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
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
• Scaling up to relational structures
• Supports linear (sequence) but not
branching (concurrency, coordination)
dynamics
Structured
Probabilistic Inference
Probabilistic inference for Events
– 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
• 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
 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
• 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 DAMLS/OWL-S
• Hypothesis: Simulation based inference over
semantic relations is useful for question
answering.
http://www.icsi.berkeley.edu/NTL
http://www.icsi.berkeley.edu/framenet
http://www.icsi.berkeley.edu/NTL
http://www.icsi.berkeley.edu/framenet