Bayesian Logic Programs
for
Plan Recognition and Machine Reading
Sindhu Raghavan
Advisor: Raymond Mooney
PhD Oral Defense
Nov 29th, 2012
1
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
2
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
3
Machine Reading
Machine reading involves the automatic extraction
of knowledge from natural language text
Example
“Barack Obama is the current President of the USA…….
Obama was born on August 4, 1961, in Hawaii, USA…….”
Extracted facts
nationState(usa)
person(barackobama)
isLedBy(usa,barackobama)
hasBirthPlace(barackobama,usa)
employs(usa, barackobama)
Data is relational in
nature - several entities
and several relations
between them
4
Characteristics of Real World Data
• Relational or structured data
– Several entities in the domain
– Several relations between entities
– Not always independent and identically
distributed (i.i.d)
• Presence of noise or uncertainty
– Uncertainty in the types of entities
– Uncertainty in the relations
Traditional approaches like first-order logic or probabilistic
models can handle either structured data or uncertainty,
but not both.
5
Statistical Relational Learning (SRL)
• Integrates first-order logic and probabilistic graphical
models [Getoor and Taskar, 2007]
– Overcome limitations of traditional approaches
• SRL formalisms
–
–
–
–
Stochastic Logic Programs (SLPs) [Muggleton, 1996]
Probabilistic Relational Models (PRMs) [Friedman et al., 1999]
Bayesian Logic Programs (BLPs) [Kersting and De Raedt, 2001]
Markov Logic Networks (MLNs) [Richardson and Domingos, 2006]
6
Statistical Relational Learning (SRL)
• Integrates first-order logic and probabilistic graphical
models [Getoor and Taskar, 2007]
– Overcome limitations of traditional approaches
• SRL formalisms
–
–
–
–
Stochastic Logic Programs (SLPs) [Muggleton, 1996]
Probabilistic Relational Models (PRMs) [Friedman et al., 1999]
Bayesian Logic Programs (BLPs) [Kersting and De Raedt, 2001]
Markov Logic Networks (MLNs) [Richardson and Domingos, 2006]
7
Bayesian Logic Programs (BLPs)
[Kersting and De Raedt, 2001]
• Integrate first-order logic and Bayesian
networks
• Why BLPs?
– Efficient grounding mechanism that includes only
those variables that are relevant to the query
– Easy to extend by incorporating any type of logical
inference to construct networks
– Well suited for capturing causal relations in data
8
Objectives
Plan Recognition
Machine Reading
9
Objectives
Plan recognition involves predicting the top-level plan of
an agent based on its observed actions
Machine Reading
10
Objectives
Plan Recognition
Machine Reading involves automatic extraction of
knowledge from natural language text
11
Common characteristics
• Inference and learning from partially observed
or incomplete data
• Plan recognition
– Top-level plan is not observed
– Some of the executed actions can be unobserved
• Machine Reading
– Information that is implicit is rarely observed in
data
– Common sense knowledge is not always explicitly
stated
12
Thesis Contributions
• Plan Recognition
– Bayesian Abductive Logic Programs (BALPs) [ECML 2011]
• Machine Reading
– BLPs for learning to infer implicit facts from natural
language text [ACL 2012]
– Online rule learner for learning common sense
knowledge from natural language extractions [In
Submission]
– Approach to scoring first-order rules (common sense
knowledge) using WordNet [In Submission]
13
Thesis Contributions
• Plan Recognition
– Bayesian Abductive Logic Programs (BALPs) [ECML 2011]
• Machine Reading
– BLPs for learning to infer implicit facts from natural
language text [ACL 2012]
– Online rule learner for learning common sense
knowledge from natural language extractions [In
Submission]
– Approach to scoring first-order rules (common sense
knowledge) using WordNet [In Submission]
14
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
15
Bayesian Logic Programs (BLPs)
[Kersting and De Raedt, 2001]
• Set of Bayesian clauses a|a1,a2,....,an
– Definite clauses that are universally quantified
– Range-restricted, i.e variables{head} Í variables{body}
– Associated conditional probability table (CPT)
• P(head|body)
• Bayesian predicates a, a1, a2, …, an have finite
domains
– Combining rule like noisy-or for mapping multiple CPTs
into a single CPT
• Given a set of Bayesian clauses and a query, SLD
resolution is used to construct ground Bayesian
networks for probabilistic inference
16
Probabilistic Inference and Learning
• Probabilistic inference
– Marginal probability
• Exact Inference
• Sample Search [Gogate and Dechter, 2007]
• Learning [Kersting and De Raedt, 2008]
– Parameters
• Expectation Maximization
• Gradient-ascent based learning
17
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
18
Plan Recognition
• Predict an agent’s top-level plan based on its
observed actions
• Abductive reasoning involving inference of
cause from effect
• Since SLD resolution used in BLPs is deductive
in nature, BLPs cannot be used as is plan
recognition
19
Extending BLPs for Plan Recognition
Logical
Abduction
BLPs
BALPs
BALPs – Bayesian Abductive Logic Programs
20
Extending BLPs for Plan Recognition
Stickel’s
Abduction
Algorithm
BLPs
BALPs
BALPs – Bayesian Abductive Logic Programs
21
Experimental Evaluation
• Data
• Monroe [Blaylock and Allen, 2005]
• Linux [Blaylock and Allen, 2005]
• Story Understanding [Ng and Mooney, 1992]
• Systems compared
–
–
–
–
–
BALPs
MLN-HCAM [Singla and Mooney, 2011]
Blaylock and Allen’s system [Blaylock and Allen, 2005]
ACCEL-Simplicity [Ng and Mooney, 1992]
ACCEL-Coherence [Ng and Mooney, 1992]
22
Summary of Results
• Monroe and Linux
– BALPs outperform both MLN-HCAM and the system
by Blaylock and Allen
• Story Understanding
– BALPS outperform both MLN-HCAM and ACCELSimplicity
– ACCEL-Coherence outperforms BALPs and other
systems
• Specifically developed for text interpretation
• Automatic learning of model parameters using
EM
23
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
24
Machine Reading
• Natural language text is typically “incomplete”
– Some information is always implicit
– Common sense information is not always explicitly
stated
– Grice’s maxim of quantity [1975]
• Information extraction (IE) systems extract
information that is explicitly stated [Cowie and
Lenhert, 1996; Sarawagi, 2008]
– Cannot extract information that is implicit
25
Example
Natural language text
“Barack Obama is the President of the
United States of America.”
Query
“Barack Obama is a citizen of what
country?”
IE systems cannot answer this query since
citizenship information is not explicitly stated.
26
Objective
• Infer implicit facts from explicitly stated
information
– Extract explicitly stated facts using an off-the-shelf
IE system
– Learn common sense knowledge in the form of
first-order rules to deduce additional facts
– Use BLPs for inference of additional facts
27
Related Work
• Logical deduction based approaches
– Learning propositional rules [Nahm and Mooney, 2000]
– Purely logical deduction is brittle since it cannot assign
probabilities to inferences
– Learning probabilistic first-order rules using FOIL and
FARMER [Carlson et al., 2010; Doppa et al., 2010]
– Probabilities are not computed using well-founded
probabilistic graphical models
• Use MLN based approaches for inferring additional
facts [Schoenmackers et al., 2010; Sorower et al., 2011]
– “Brute force” inference could result in intractably large
networks for large domains
– Scaling of MLNs to large domains [Schoenmackers et al., 2010;
Niu et al., 2012]
28
Objectives
• BLPs for learning to infer implicit facts from
natural language text
• Online rule learner for learning common sense
knowledge from natural language extractions
• Approach to scoring first-order common sense
knowledge using WordNet
29
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
30
.
Barack
. .Obama is the
current. President of
. Obama was
USA…….
. August 4,
born on
System Architecture
1961, in Hawaii, USA.
Training
Documents
BLP Weight Learner
.
.
nationState(USA)
.
Person(BarackObama)
.
isLedBy(USA,BarackObama)
.
hasBirthPlace(BarackObama,USA)
.
hasCitizenship(BarackObama,USA)
Information Extractor
Extracted
(IBM SIRE)
Facts
First-Order
Logical Rules
Rule learner
nationState(B) ∧ isLedBy(B,A)  hasCitizenship(A,B)
nationState(B) ∧ employs(B,A)  hasCitizenship(A,B)
Bayesian Logic
Program (BLP)
BLP Inference
Engine
hasCitizenship(A,B) | nationState(B) , isLedBy(B,A) .9
hasCitizenship(A,B) | nationState(B) , employs(B,A) .6
hasCitizenship(mahathir-mohamad, malaysia)
0.75
Test
Document
Extractions
nationState(malaysia)
Person(mahathir-mohamad)
isLedBy(malaysia,mahathir-mohamad)
employs(malaysia,mahatir-mohamad)
Inferences with
probabilities
31
System Architecture
Training
Documents
Information Extractor
Extracted
(IBM SIRE)
Facts
BLP Weight Learner
First-Order
Logical Rules
Bayesian Logic
Program (BLP)
BLP Inference
Engine
Inferences with
probabilities
Inductive Logic
Programming
(LIME)
Test
Document
Extractions
32
Inductive Logic Programming (ILP)
for learning first-order rules
Positive instances
Target relation
hasCitizenship
(BarackObama, USA)
hasCitizenship(X,Y)
hasCitizenship
(GeorgeBush, USA)
Rules
hasCitizenship
(IndiraGandhi,India)
.
.
ILP
Rule Learner
.
.
Negative instances
hasCitizenship
(BarackObama, India)
hasCitizenship
(GeorgeBush, India)
hasCitizenship
(IndiraGandhi,USA)
.
.
nationState(Y) ∧
person(X)∧
isLedBy(Y,X) 
hasCitizenship
(X,Y)
KB
hasBirthPlace(BarackObama,USA)
person(BarackObama)
nationState(USA)
nationState(India)
.
.
33
Inference using BLPs
Test document
“Barack Obama is the current President of the USA…….
Obama was born on August 4, 1961, in Hawaii, USA…….”
Extracted facts
nationState(usa)
person(barackobama)
isLedBy(usa,barackobama)
hasBirthPlace(barackobama,usa)
employs(usa, barackobama)
Learned rules
nationState(B) ∧ person(A) ∧ isLedBy(B,A)  hasCitizenship(A,B)
nationState(B) ∧ person(A) ∧ employs(B,A)  hasCitizenship(A,B)
34
Logical Inference - Proof 1
nationState(B) ∧ person(A) ∧ isLedBy(B,A)  hasCitizenship(A,B)
nationState(usa) person(barackobama)
isLedBy(usa,barackobama)
hasCitizenship(barackobama,usa)
35
Logical Inference - Proof 2
nationState(B) ∧ person(A) ∧ employs(B,A)  hasCitizenship(A,B)
nationState(usa) person(barackobama)
employs(usa,barackobama)
hasCitizenship(barackobama,usa)
36
Bayesian Network Construction
isLedBy
(usa,
barack
obama)
nationState
(usa)
person
(barack
obama)
employs
(usa,
barack
obama)
hasCitizenship
(barackobama, usa)
37
Bayesian Network Construction
isLedBy
(usa,
barack
obama)
nationState
(usa)
person
(barack
obama)
employs
(usa,
barack
obama)
hasCitizenship
(barackobama, usa)
38
Bayesian Network Construction
isLedBy
(usa,
barack
obama)
nationState
(usa)
person
(barack
obama)
employs
(usa,
barack
obama)
hasCitizenship
(barackobama, usa)
39
Bayesian Network Construction
isLedBy
(usa,
barack
obama)
person
(barack
obama)
nationState
(usa)
employs
(usa,
barack
obama)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
dummy1
-
-
-
-
-
-
-
-
-
-
-
-
dummy2
hasCitizenship
(barackobama, usa)
40
Experimental Evaluation
• Data
– DARPA’s intelligence community (IC) data set
from the Machine Reading Project (MRP)
– Consists of news articles on politics, terrorism,
and other international events
– 10,000 documents in total
• Perform 10-fold cross validation
41
Experimental Evaluation
• Learning first-order rules using LIME [McCreath and
Sharma, 1998]
– Learn rules for 13 target relations
– Learn rules using both positive and negative instances
and using only positive instances
– Include all unique rules learned from different models
• Learning BLP parameters
– Learn noisy-or parameters using Expectation
Maximization (EM)
– Set priors to maximum likelihood estimates
42
Experimental Evaluation
• Performance evaluation
– Lack of ground truth for evaluation
– Manually evaluated inferred facts from 40 documents,
randomly selected from each test set
– Compute precision
– Fraction of inferences that are correct
– Compute two precision scores
• Unadjusted (UA) – does not account for extractor’s mistakes
• Adjusted (AD) – account for extractor’s mistakes
– Rank inferences using marginal probabilities and
evaluate top-n
43
Experimental Evaluation
• Systems compared
– BLP Learned Weights
• Noisy-or parameters learned using EM
– BLP Manual Weights
• Noisy-or parameters set to 0.9
– Logical Deduction
– MLN Learned Weights
• Learn weights using generative online weight learner
– MLN Manual Weights
• Assign a weight of 10 to all rules and MLE priors to all
predicates
44
Unadjusted Precision
1
BLP Manual Weights
BLP Learned Weights
MLN Manual Weights
MLN Learned Weights
Logical Deduction
0.9
Unadjusted Precision
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
600
700
800
900
1000
Top−n inferences
45
Inferior performance of EM
• Insufficient training data
• Lack of ground truth information for relations
that can be inferred
– Implicit relations seen less frequently in training
data
– EM learns lower weights for rules corresponding
to implicit relations
46
Performance of MLNs
• Inferior performance of MLNs
– Insufficient training data for learning
– Use of closed world assumption for inference and learning
– Lack of strictly typed ontology
• GeopoliticalEntity could be an Agent as well as Location
• Improvements to MLNs
– Integrity constraints to avoid inference of spurious facts
like employs(a,a)
– Incorporate techniques proposed by Sorower et al. [2011]
47
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
48
Limitations of LIME
• Assumes data is accurate
– Negative instances artificially generated are
usually noisy and inaccurate
– Extraction errors result in noisy data
• Does not scale to large corpora
Develop an approach that can learn first-order rules from
noisy and incomplete IE extractions
49
Online Rule Learning
• Incorporates the incomplete nature of natural
language text
– Body consists of relations that are explicitly stated
– Head is a relation that can be inferred
• Relations that are implicit occur less frequently
than those that are explicitly stated
– Use frequency of occurrence as a heuristic to
distinguish different types of relations
• Process examples in an online manner to scale to
large corpora
50
Approach
• For each example, construct a directed graph
of relation extractions
• Add directed edges between nodes that share
one or more constants
– Relations connected by edges are related and
participate in the same rule
• Traverse the graph to learn first-order rules
Learning from positive instances only
51
Example
“Barack Obama is the current President of the
USA……. Obama, citizen of the USA was born
on August 4, 1961, in Hawaii, USA…….”
Extracted facts
nationState(USA)
person(BarackObama)
isLedBy(USA,BarackObama)
hasBirthPlace(BarackObama,USA)
hasCitizenship(BarackObama,USA)
52
Example
“Barack Obama is the current President of the
USA……. Obama, citizen of the USA was born
on August 4, 1961, in Hawaii, USA…….”
Extracted facts
nationState(USA)
person(BarackObama)
isLedBy(USA,BarackObama)
hasBirthPlace(BarackObama,USA)
hasCitizenship(BarackObama,USA)
Entities
53
Example
“Barack Obama is the current President of the
USA……. Obama, citizen of the USA was born
on August 4, 1961, in Hawaii, USA…….”
Extracted facts
nationState(USA)
person(BarackObama)
isLedBy(USA,BarackObama)
hasBirthPlace(BarackObama,USA)
hasCitizenship(BarackObama,USA)
Relations
54
Directed graph construction
?
isLedBy
(USA,
Barack Obama)
hasBirthPlace
(Barack Obama,
USA)
hasCitizenship
(Barack Obama,
USA)
isLedBy
33
hasBirthPlace
25
hasCitizenship
17
55
Graph Traversal
isLedBy
(USA,
Barack Obama)
hasBirthPlace
(Barack Obama,
USA)
isLedBy(USA, Barack Obama) 
hasBirthPlace(Barack Obama, USA)
56
Graph Traversal
isLedBy
(USA,
Barack Obama)
hasBirthPlace
(Barack Obama,
USA)
isLedBy(USA, Barack Obama) ∧
person(Barack Obama) ∧
nationState(USA) 
hasBirthPlace(Barack Obama, USA)
57
Graph Traversal
isLedBy
(USA,
Barack Obama)
hasBirthPlace
(Barack Obama,
USA)
isLedBy(X, Y) ∧
person(Y) ∧
nationState(X) 
hasBirthPlace(Y, X)
58
Rules learned
isLedBy(X, Y) ∧ person(Y) ∧ nationState(X)
 hasBirthPlace(Y, X)
isLedBy(X, Y) ∧ person(Y) ∧ nationState(X)
 hasCitizenship(Y, X)
hasBirthPlace(X, Y) ∧ person(X) ∧ nationState(Y)
 hasCitizenship(X, Y)
59
Sample rules
employs(X, Y) ∧ commercialOrganization(X)
 hasMemberPerson(X, Y)
isLedBy(X, Y) ∧ nationState(X)
 hasCitizenship(Y, X)
isLedBy(X, Y) ∧ nationState(X) ∧ person(Y)
 hasBirthPlace(Y, X)
60
Experimental Evaluation
• Learn first-order rules for 14 target relations
– Full-set
– Subset
• 10 target relations
• Manually set noisy-or parameters to 0.9
• Systems compared
– Online Rule Learner (ORL)
– LIME [McCreath and Sharma, 1998]
– Combined
61
Full-set
1
ORL
LIME
COMBINED
0.9
Unadjusted Precision
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
Top−n
600
700
800
900
1000
62
Inferior performance of ORL on Full-set
• Several incorrect inferences with high
marginal probabilities
– Instances of thingPhysicallyDamaged and
eventLocationGPE
– High probabilities due to multiple rules inferring
these instances
– Rules not very accurate resulting in inaccurate
inferences
63
Subset
1
ORL
LIME
COMBINED
Unadjusted Precision
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
Top−n
600
700
800
900
1000
64
Running Time
ORL
LIME
3.8 mins
11.23 hrs
• LIME
– Learns rules for one target relation at a time
– Includes time taken to learn from positive only
and from positive and negative examples
• ORL
– Learns rules for all target relations at once
65
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
66
Scoring first-order rules
• Predicate names employ English words
• Confident rules typically have predicates
whose words are semantically related
• Use word similarity or relatedness to calculate
weights
– Word similarity computed using WordNet
• Compute weights between 0 and 1, which are
then used as noisy-or parameters
– Higher weights indicate more confident rules
67
WordNet
[Fellbaum, 1998]
• Lexical knowledge base consisting of 130,000
English words
• Nouns, verbs, adjectives, and adverbs organized
into “synsets” (synonym sets)
• wup [Wu and Palmer, 1994] similarity measure to
compute word similarity
– Computes scaled similarity scores between 0 and 1
– Computes the depth of the least common subsumer
of the given words and scales it by the sum of the
depths of the given words
68
Scoring rules using WUP
• Compute word similarity using wup for every pair
of words (wi,wj)
– wi refers to words in the body
– wj refers to words in the head
• Compute average similarity for all pairs of words
• Predicate names like hasCitizenship and
hasMember are segmented into has, citizenship,
and member
– Stop words are removed
69
Example
employs(X,Y) ∧ governmentOrganization(X) 
hasMember(X,Y)
70
Example
employs(X,Y) ∧ governmentOrganization(X) 
hasMember(X,Y)
(employs, government, organization) 
(member)
71
Example
employs(X,Y) ∧ governmentOrganization(X) 
hasMember(X,Y)
(employs, government, organization) 
(member)
Word pair
employs, member
government, member
organization, member
Average
wup score
.50
.75
.85
.70
72
Example
employs(X,Y) ∧ governmentOrganization(X) 
hasMember(X,Y) (.70)
(employs, government, organization) 
(member)
employs(X,Y) ∧ person(Y) ∧ nationState(X)
hasBirthPlace(Y,X) (.67)
(employs, person, nation, state)  (birth, place)
73
Scoring rules using WUP
• WUP-AVG
– Use words from both entities and relations
– Use the average similarity between all pairs of words as
the weight
• WUP-MAX
– Use words from both entities and relations
– Use maximum similarity among all pairs of words as the
weight
• WUP-MAX-REL
– Use words from relations only
– Use maximum similarity among all pairs of words as the
weight
74
Experimental Evaluation
• Target relations
– Full-set
– Subset
• Models
– COMBINED
• Rule scoring approaches compared
–
–
–
–
–
WUP-AVG
WUP-MAX
WUP-MAX-REL
Default (Manual weights set to 0.9)
EM (Weights learned from EM)
75
Full-set
1
Default
WUP−AVG
WUP−MAX
WUP−MAX−REL
0.9
Unadjusted Precision
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
600
700
800
900
1000
Top−n
76
Subset
1
Default
WUP−AVG
WUP−MAX
WUP−MAX−REL
EM
0.9
Unadjusted Precision
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
600
700
800
900
1000
Top−n
77
Summary
• BLP approach for inferring implicit facts with
high precision
• Superior performance of BLPs over purely
logical deduction and MLNs
• Efficient learning of probabilistic first-order
rules using online rule learning
• Efficacy of WUP-AVG for scoring first-order
rules
78
Outline
• Motivation
• Background
– Bayesian Logic Programs (BLPs)
• Plan Recognition
• Machine Reading
– BLPs for inferring implicit facts
– Online Rule Learning
– Scoring Rules using WordNet
• Future Work
• Conclusions
79
Future Work
• Plan recognition
– Structure learning of abductive knowledge bases
for BALPs
– Comparison of BALPs to other SRL models
•
•
•
•
ProbLog [Kimmig et al., 2008]
PRISM [Sato, 1995]
Poole’s Horn Abduction [Poole, 1993]
Abductive Stochastic Logic Programs [Tamaddoni-Nezhad,
Chaleil, Kakas, & Muggleton, 2006]
80
Future Work
• Machine Reading
– Large scale evaluation using crowdsourcing
– Comparison of BLPs to existing approaches on
machine reading [Schoenmackers et al., 2010; Carlson et al.,
2010; Doppa et al., 2010; Sorower et al., 2011]
– Alternate approaches to scoring rules
• Use models from distributional semantics [Garrette et
al., 2011]
81
Long-term Directions
• Parameter learning
– Using approximate inference techniques
– Discriminative learning of parameters
• Lifted inference for BLPs and BALPs
82
Conclusions
• Demonstrated the efficacy of BLPs on two
diverse tasks
– Plan recognition
• BALPs
– Machine reading
• Infer implicit facts from natural language text
• Online rule learner for efficient learning of first-order
rules from noisy IE extractions
• Scoring first-order rules using WordNet
83
Conclusions
• Demonstrated superior performance of BLPs
over MLNs on both tasks
• Contributions could have direct impact on the
advancement of applications that use plan
recognition and machine reading
– SIRI
– IBM’s Watson system
84
Questions
85