learning relationships defined by linear combinations of constrained random walks
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Learning Relationships Defined by Linear Combinations of Constrained Random Walks. William W. Cohen Machine Learning Department and Language Technologies Institute School of Computer Science Carnegie Mellon University joint work with: Ni Lao Language Technologies Institute Tom Mitchell - PowerPoint PPT PresentationTRANSCRIPT
Learning Relationships Defined by Linear Combinations of
Constrained Random Walks
William W. CohenMachine Learning Department and Language Technologies Institute
School of Computer ScienceCarnegie Mellon University
joint work with: Ni Lao
Language Technologies InstituteTom Mitchell
Machine Learning Department
Motivation: The simple and the complex
• In computer science there is a tension between– The elegant, simple and general– The messy, complex and problem-specific
• Graphs are:– Simple: so they are easy to analyze and store– General: so
• They appear in many contexts• They are often a natural representation of
important aspects of information– Well-understood: for instance,
• Standard techniques like PPR/RWR exist for estimating similarity of two nodes in a graph
Motivation: The simple and the complex
• The real world is complex…• … learning is a way to incorporate that complexity in our
models without sacrificing elegance and generality
Motivation: The simple and the complex
• Graphs are:– Simple: so they are easy to analyze and store– General– Well-understood: for instance,
• Standard techniques like PPR/RWR exist for estimating similarity of two nodes in a graph
• In this talk:– Learning similarity-like relationships in
graphs, based on RWR/PPR– Several applications
Similarity Queries on Graphs
1) Given type t* and node x in G, find y:T(y)=t* and y~x.2) Given type t* and node set X, find y:T(y)=t* and y~X.
• Nearest-neighbor classification:– G contains feature nodes and instance nodes– A link (x,f) means feature f is true for instance x– x* is a query instance, y~x* means y likely of same class as x*
• Information retrieval: – G contains word nodes and document nodes– A link (w,d) means word w is in document d– X is a set of keywords, y~X means y likely to be relevant to X
• Database retrieval:– G encodes a database– … ?
BANKS: Browsing and Keyword Search
• Database is modeled as a graph– Nodes = tuples– Edges = references between tuples
• edges are directed and indicate foreign key, inclusion dependencies, ..
[Aditya et al, VLDB 2002]
MultiQuery Optimization
S. Sudarshan
Prasan Roy
writes writes
author author
paper
Query: {“sudarshan”, “roy”} Answer: subtree from graph
MultiQuery Optimization
S. Sudarshan
Prasan Roy
writes writes
author author
paper
Query: “sudarshan”, “roy” Answer: subtree from graph
y: paper(y) & ~“sudarshan” w: paper(y) & w~“roy”AND
Similarity Queries on Graphs
1) Given type t* and node x in G, find y:T(y)=t* and y~x.2) Given type t* and node set X, find y:T(y)=t* and y~X.
• Nearest-neighbor classification• Information retrieval• Database retrieval• Evaluation: specific families of tasks for scientific publications:
– Citation recommendation for a paper: (given title, year, …, of paper p, what papers should be cited by p?)
– Expert-finding: (given keywords, genes, … suggest a possible author)– “Entity recommendation”: (given title, author, year, … predict entities
mentioned in a paper, e.g. gene-protein entities) – can improve NER– Literature recommendation: given researcher and year, suggest papers
to read that year
• Inference in a DB of automatically-extracted facts
Core tasks in CS
Outline
• Motivation for Learning Similarity in Graphs
• A Baseline Similarity Metric
• Some Literature-related Tasks• The Path Ranking Algorithm (Learning Method)
– Motivation– Details
• Results: BioLiterature tasks
• Results: KB Inference tasks
Defining Similarity on Graphs: PPR/RWR
Given type t* and node x, find y:T(y)=t* and y~x.
• Similarity defined by “damped” version of PageRank• Similarity between nodes x and y:
– “Random surfer model”: from a node z,• with probability α, teleport back to x (“reset”)• Else pick a y uniformly from { y’ : z y’ }• repeat from node y ....
– Similarity x~y = Pr( surfer is at y | reset is always to x )
• Intuitively, x~y is sum of weight of all paths from x to y, where weight of path decreases exponentially with length.
• Can easily extend to a “query” set X={x1,…,xk}
[Personalized PageRank 1999]
Some BioLiterature Retrieval Tasks
• Data used in this study– Yeast: 0.2M nodes, 5.5M links– Fly: 0.8M nodes, 3.5M links– E.g. the fly graph
Publication126,813
Author233,229
Write679,903 Gene
516,416Protein414,824
689,812
Cite 1,267,531
Bioentity5,823,376
1,785,626
Physical/Geneticinteractions1,352,820
Downstream/Uptream
Year58
Journal1,801
Transcribe293,285
before
Title Terms102,223
2,060,275
Learning Proximity Measures for BioLiterature Retrieval Tasks
• Tasks:– Gene recommendation: author, yeargene– Reference recommendation: words,yearpaper– Expert-finding: words, genesauthor– Literature-recommendation: author, [papers read in past]
• Baseline method:– Typed RWR proximity methods
• Baseline learning method:– parameterize Prob(walk edge|edge label=L) and tune the
parameters for each label L (somehow…)
Publication126,813
Author233,229
Write679,903 Gene
516,416Protein414,824
689,812
Cite 1,267,531
Bioentity5,823,376
1,785,626
Physical/Geneticinteractions1,352,820
Downstream/Uptream
Year58
Journal1,801
Transcribe293,285
before
Title Terms102,223
2,060,275
P(write)=b
P(L=cite) = a
P(NE) = c
P(bindTo) = dP(express) = d
Path-based vs Edge-label based learning
• Learning one-parameter-per-edge label is limited because the context in which an edge label appears is ignored– E.g. (observed from real data – task, find papers to read)
• Instead, we will learn path-specific parameters
Path Comments
Don't read about genes I’ve already read about
Do read papers from my favorite authors
-1Read Contain Containauthor paper gene paper -1Read Write Writeauthor paper author paper
• Paths will be interpreted as constrained random walks that give a similarity-like weight to every reachable node• Step 0: D0 = {a} Start at author a• Step 1: D1: Uniform over all papers p read by a• Step 2: D2: Author a’ of papers in D1 weighted by number of papers
in D1 published by a’• Step 3: D3 Papers p’ published by a’ weighted by ....• …
A Limitation of RWR Learning Methods• Learning one-parameter-per-edge label is limited because the context
in which an edge label appears is ignored– E.g. (observed from real data – task, find papers to read)
• Instead, we will learn path-specific parameters
Path Comments
Don't read about genes I’ve already read about
Do read papers from my favorite authors
Path Comments
Do read about the genes I’m working on
Don't read papers from my own lab
-1Read Contain Containauthor paper gene paper -1Read Write Writeauthor paper author paper
-1Write Contain Containauthor paper gene paper -1Write publish publishauthor paper institute paper
Path Constrained Random Walksas Basis of a Proximity Measure
• Our work (Lao & Cohen, ECML 2010) – learn a weighted combination of simple “path experts”, each of
which corresponds to a particular labeled path through the graph
• Citation recommendation--an example – In the TREC-CHEM Prior Art Search Task, researchers found
that it is more effective to first find patents about the topic, then aggregate their citations
– Our proposed model can discover this kind of retrieval schemes and assign proper weights to combine them. E.g.
Weighted Paths
18
Definitions• An graph G=(T,R,X,E), is
– a set of entity types T={T} and a set of relations R={R}– a set of entities (nodes) X={x}, where each node x has a type from T– a set of edges e=(x,y), where each edge has a relation label from R
• A path P=(R1, …,Rn) is a sequence of relations
• Path Constrained Random Walk– Given a query set S of “source” nodes– Distribution D0 at time 0 is uniform over s in S– Distribution Dt at time t>0 is formed by
• Pick x from Dt-1• Pick y uniformly from all things related to x
– by an edge labeled Rt
– Notation: fP(s,t) = Prob(st; P)
– In our examples type of t will be determined by Rn
Paper
Paper
Author
Paper
Paper
Paper
Author
Paper
WrittenBy
Write
Cite
Cite
CiteBy
CiteBy
WrittenBy
Path Ranking Algorithm (PRA)
• A PRA model scores a source-target node pair by a linear function of their path features
where P is the set of all relation paths with length ≤ L (with support on data, in some cases – see [Lao and Cohen EMNLP 2011])
• For a relation R and a set of node pairs {(si, ti)}, we construct a training dataset D ={(xi, yi)}, where xi is a vector of all the path features for (si, ti), and yi indicates whether R(si, ti) is true or not
• θ is estimated using L1,L2-regularized logistic regression
( , ) ( , )P PP
score s t f s t
P
[Lao & Cohen, ECML 2010]
( , ) Prob( ; )Pf s t s t P
26
Extension 1: Query Independent Paths
• PageRank (and other query-independent rankings):– assign an importance score (query independent) to each web page– later combined with relevance score (query dependent)
• We generalize pagerank to heterogeneous graphs:– We include to each query a special entity e0 of special type T0 – T0 is related to all other entity types, and each type is related to all
instances of that type– This defines a set of PageRank-like query independent relation paths– Compute f(*t;P) offline for efficiency
• Example
Paper
Paper
AuthorT0
AuthorPaper
Paper
Wrote
WrittenBy
CiteBy
Citewell cited papers
productive authors
all papers
all authors
Extension 2: Entity-specific rankings
• There are entity-specific characteristics which cannot be captured by a general model– Some items are interesting to the users because of features not
captured in the data– To model this, assume the identity of the entity matters
– Introduce new features f(st; Ps,t) to account for jumping from s to t and new features f(*t; P*,t)
– At each gradient step, add a few new features of this sort with highest gradient, count on regularization to avoid overfitting
Extension 3: Speeding up random walks
• Prior work on speeding up personalized PageRank/RWR– Pre-computing components (eg Jeh & Widom 2003)– Sampling-based approaches (eg Fogaras et al, 2005)– Pre-clustering data (eg Tong et al 2006)– Pruning approaches (eg Andersen et al, 2006)
• We use hybrid sample/pruning based approach (“Weighted particle filtering” + “low variance sampling”)– Same approximation used at training and test time– Speedups up to 10-100x w/ little loss (sometimes some gain!) in
performance
[Lao and Cohen, KDD 2010]
30
Experiment Setup for BioLiterature
• Data sources for bio-informatics– PubMed on-line archive of over 18 million biological abstracts– PubMed Central (PMC) full-text copies of over 1 million of these papers– Saccharomyces Genome Database (SGD) a database for yeast– Flymine a database for fruit flies
• Tasks– Gene recommendation: author, yeargene– Venue recommendation: genes, title wordsjournal– Reference recommendation: title words,yearpaper– Expert-finding: title words, genesauthor
• Data split– 2000 training, 2000 tuning, 2000 test
• Time variant graph – each edge is tagged with a time stamp (year)– only consider edges that are earlier than the query, during random walk
BioLiterature: Some Results
• Compare the MAP of PRA to– RWR model– query independent paths (qip) – popular entity biases (pop)
Except these† , all improvements are statistically significant at p<0.05 using paired t-test
Example Path Features and their Weights
• A PRA+qip+pop model trained for the citation recommendation task on the yeast data
6) approx. standard IR retrieval
1) papers co-cited with on-topic papers
7,8) papers cited during the past two years
9) well cited papers
12,13) papers published during the past two years
10,11) key early papers about specific genes
14) old papers
Outline
• Motivation for Learning Similarity in Graphs
• A Baseline Similarity Metric
• Some Literature-related Tasks• The Path Ranking Algorithm (Learning Method)
– Motivation– Details
• Results: BioLiterature tasks
• Results: KB Inference tasks [Lao, Mitchell, Cohen, EMNLP 2011]
Large Scale Knowledge-Bases
• Large-Scale Collections of Automatically Extracted Knowledge
– KnowItAll (Univ. Washington)
• 0.5B facts extracted from 0.1B web pages
– DBpedia (Univ. Leipzig)
• 3.5M entities 0.7B facts extracted from wikipedia
– YAGO (Max-Planck-Institute)
• 2M entities 20M facts extracted from Wikipedia and wordNet
– FreeBase
• 20M entities 0.3B links, integrated from different data sources and human judgments
– NELL (Never-Ending Language Learning, CMU)
• 0.85M facts extracted from 0.5B webpages
Inference in Noisy Knowledge Bases
• Challenges– Robustness: extracted knowledge is incomplete and noisy– Scalability: the size of knowledge base is large
American
IsA
PlaysIn
AthletePlaysInLeagueHinesWard
SteelersAthletePlaysForTeam
NFL
TeamPlaysInLeague
?
isa-1
The NELL Case Study
• Never-Ending Language Learning: “a never-ending learning system that operates 24 hours per day, for years, to continuously improve its ability to read (extract structured facts from) the web” (Carlson et al., 2010)
• Closed domain, semi-supervised extraction• Combines multiple strategies: morphological patterns,
textual context, html patterns, logical inference
• Example beliefs
A Link Prediction Task
• We consider 48 relations for which NELL database has more than 100 instances
• We create two link prediction tasks for each relation– AthletePlaysInLeague(HinesWard,?)– AthletePlaysInLeague(?, NFL)
• The actual nodes y known to satisfy R(x; ?) are treated as labeled positive examples, and all other nodes are treated as negative examples
Current NELL method (baseline)
• FOIL (Quinlan and Cameron-Jones, 1993) is a learning algorithm similar to decision trees, but in relational domains
• NELL implements two assumptions for efficient learning– The predicates are functional --e.g. an athlete plays in at
most one league– Only find clauses that correspond to bounded-length paths
of binary relations -- relational pathfinding (Richards & Mooney, 1992)
04/19/23 38
• FOL not great for handling uncertainty– FOIL can only combine rules with disjunctions, therefore cannot
leverage low accuracy rules– E.g. rules for teamPlaysSports
High
accuracy
but low
recall
Current NELL method (baseline)
Experiments - Cross Validation on KB data(for parameter setting, etc)
RWR: Random Walk with Restart (PPR)
†Paired t-test give p-values 7x10-3, 9x10-4, 9x10-8, 4x10-4
†
†
†
†
Evaluation by Mechanical Turk
• There are many test queries per predicate– All entities of a predicate’s domain/range, e.g.
• WorksFor(person, organization)– On average 7,000 test queries for each functional predicate, and
13,000 for each non-functional predicate
• Sampled evaluation– We only evaluate the top ranked result for each query– We sort the queries for each predicate according to the scores
of their top ranked results, and then evaluate precisions at top 10, 100 and 1000 queries
• Each belief is voted by 5 workers– Workers are given assertions like “Hines Ward plays for the
team Steelers”, as well as Google search links for each entity
Evaluation by Mechanical Turk• On 8 functional predicates where N-FOIL can successfully learn
– PRA is comparable to N-FOIL for p@10, but has significantly better p@100
• On 8 randomly sampled non-functional (one-many) predicates – Slightly lower accuracy than functional predicates
Task #Rules
N-FOILp@10 p@10
0#Path
s
PRAp@10 p@10
0
Functional Predicates 2.1(+37) 0.76 0.380 43 0.79 0.668Non-functional
Predicates ---- ---- ---- 92 0.65 0.620
PRA: Path Ranking Algorithm
Outline
• Motivation for Learning Similarity in Graphs
• A Baseline Similarity Metric
• Some Literature-related Tasks• The Path Ranking Algorithm (Learning Method)
– Motivation– Details
• Results: BioLiterature tasks
• Results: KB Inference tasks [Lao, Mitchell, Cohen, EMNLP 2011]
Outline
• Motivation for Learning Similarity in Graphs• A Baseline Similarity Metric• Some Literature-related Tasks• The Path Ranking Algorithm (Learning Method)
– Motivation– Details
• Results: BioLiterature tasks• Results: KB Inference tasks • Conclusions
Summary/Conclusion
• Learning is the way to make a clean, elegant formulation of a task work in the messy, complicated real world
• Learning how to navigate graphs is a significant, core task that models– Recommendation, expert-finding, …– Information retrieval– Inference in KBs– …
• It includes significant, core learning problems– Regularization/search of huge feature space– Discovery: long paths, lexicalized paths, …– Incorporating knowledge of graph structure …– ….