Learning Relationships Defined by Linear Combinations of Constrained Random Walks

1. 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 Machine Learning Department

2. 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

3. 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

4. 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

5. 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 • … ?

6. BANKS: Browsing and Keyword Search [Aditya et al, VLDB 2002] • Database is modeled as a graph • Nodes = tuples • Edges = references between tuples • edges are directed and indicate foreign key, inclusion dependencies, .. paper MultiQuery Optimization writes writes author author S. Sudarshan Prasan Roy

7. Query: {“sudarshan”, “roy”} Answer: subtree from graph paper MultiQuery Optimization writes writes author author S. Sudarshan Prasan Roy

8. y: paper(y) & ~“sudarshan” AND w: paper(y) & w~“roy” Query: “sudarshan”, “roy” Answer: subtree from graph

10. 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

11. 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

12. Defining Similarity on Graphs: PPR/RWR [Personalized PageRank 1999] 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}

13. 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

14. Learning Proximity Measures for BioLiterature Retrieval Tasks • Tasks: • Gene recommendation: author, yeargene • Reference recommendation: words,yearpaper • Expert-finding: words, genesauthor • 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…) P(L=cite) = a P(bindTo) = d P(express) = d P(NE) = c P(write)=b

15. 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 • 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 .... • …

16. 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

17. 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 assignproper weights to combine them. E.g. Weighted Paths

18. Definitions • An graphG=(T,R,X,E), is • a set of entitytypesT={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 pathP=(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(st; P) • In our examples type of t will be determined by Rn 18

19. Path Ranking Algorithm (PRA) [Lao & Cohen, ECML 2010] • 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

20. 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 well cited papers all papers productive authors all authors 26

21. 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(st; 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

22. Extension 3: Speeding up random walks [Lao and Cohen, KDD 2010] • 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

23. 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, yeargene • Venue recommendation: genes, title wordsjournal • Reference recommendation: title words,yearpaper • Expert-finding: title words, genesauthor • 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 30

24. 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

25. Example Path Features and their Weights • A PRA+qip+pop model trained for the citation recommendation task on the yeast data 1) papers co-cited with on-topic papers 6) approx. standard IR retrieval 7,8) papers cited during the past two years 9) well cited papers 10,11) key early papers about specific genes 12,13) papers published during the past two years 14) old papers

26. 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]

27. 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

28. Inference in Noisy Knowledge Bases • Challenges • Robustness: extracted knowledge is incomplete and noisy • Scalability: the size of knowledge base is large

29. 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

30. 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

31. 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)

32. Current NELL method (baseline) • 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

33. 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

34. Example Paths Synonyms of the query team

35. 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

36. 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 PRA: Path Ranking Algorithm

37. 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]

38. 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

39. 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 … • ….

40. Thanks to: • The dedicated and persistent • NSF grant IIS-0811562 • NIH grant R01GM081293 • Gifts from Google • MLG Organizers! 47