1. Linking and Summarizing Information on Entities Presented by Min-Yen Kan Web IR / NLP Group (WING) Department of Computer Science National University of Singapore, Singapore This talk archived as http://wing.comp.nus.edu.sg/~kanmy/talks/080407-nihLMC.htmhttp://wing.comp.nus.edu.sg/~kanmy/talks/080407-nihLMC.htm

2. Entity Linkage using the Web and Graph-based Update Summarization 2NIH Lister Hill Medical Center Singapore, the garden city 4M+ people, sandwiched between Malaysia and Indonesia 50 km from the equator: hot and humid year-long Known for: urban planning, fondness for acronyms and aversion to bubble gum litterers :-D WING @ NUS http://wing.comp.nus.edu.sg 1 postdoc, 6 Ph.D. students, 5 undergraduates Projects of in natural language processing, digital libraries, and information retrieval.

3. Entity Linkage using the Web and Graph-based Update Summarization 3NIH Lister Hill Medical Center Entity Centric Information Management “Collate all studies on SBP2 that new findings in the last year.” “Oh, I meant the PROTEIN SBP2, not the gene.” “What other proteins does SBP2 bind to?” “Tell me more about the contradiction from previous results.” “Which Miller did the study on SBP2 in 2002?”

4. Entity Linkage using the Web and Graph-based Update Summarization 4NIH Lister Hill Medical Center Entity Centric Information Management Two consequences to discuss today: Linkage Joint work with Yee Fan TAN, Dongwon LEE (PSU) et al. Summarization Joint work with Ziheng LIN et al.

5. Entity Linkage using the Web and Graph-based Update Summarization 5NIH Lister Hill Medical Center What’s Entity Linkage? Aggregating data on an object together from heterogeneous resources Problem: Entity names are ambiguous! –Medical terms –Person names –Products –Customer records These problems exist even when we have controlled vocabulary and lexicons (Specialist, UMLS, MeSH) By UV cross-linking and immunoprecipitation, we show that SBP2 specifically binds selenoprotein mRNAs both in vitro and in vivo. The SBP2 clone used in this study generates a 3173 nt transcript (2541 nt of coding sequence plus a 632 nt 3’ UTR truncated at the polyadenylation site). Gene Protein

6. Entity Linkage using the Web and Graph-based Update Summarization 6NIH Lister Hill Medical Center Examples of Split Records Dongwon Lee, 110 E. Foster Ave. #410, State College, PA, 16802 Honda Fix Joint Conf. on Digital Libraries Apple iPod Nano 4GB Entity Linkage LEE Dong, 110 East Foster Avenue Apartment 410, University Park, PA 16802-2343 Honda Jazz JCDL 4GB iPod nano 4GB De-duplication Ironic, isn’t it?

7. Entity Linkage using the Web and Graph-based Update Summarization 7NIH Lister Hill Medical Center All over the web! Jeffrey D. Ullman (Stanford University)

8. Entity Linkage using the Web and Graph-based Update Summarization 8NIH Lister Hill Medical Center Record linkage, formally defined Input – Two lists of records, A and B Output – For each record a in A and for each record b in B, does a and b refer to the same entity? Note: – Entities do not come with unique identifiers – To disambiguate (deduplicate) items in a single list L, we set A = B = L

9. Entity Linkage using the Web and Graph-based Update Summarization 9NIH Lister Hill Medical Center Talk Outline Linkage using the Web – Introduction >> Record linkage using internal knowledge String matching Classification or clustering Graphical formalisms Blocking – Record linkage using search engines Update Summarization

10. Entity Linkage using the Web and Graph-based Update Summarization 10NIH Lister Hill Medical Center Fellegi-Sunter model * true matches ○ true non-matches false matchesfalse non-matches no-decision region (hold for human review) designate as definite match designate as definite non-match Similarity (a, b) Frequency of Similarity

11. Entity Linkage using the Web and Graph-based Update Summarization 11NIH Lister Hill Medical Center String matching String similarity –Strings as ordered sequences Edit distance Jaro and Jaro-Winkler –Strings as unordered sets Jaccard similarity Cosine similarity Abbreviation matching – Pattern detection: e.g. “National Institute of Health (NIH)” ([a], [b], [c]) ≠ ([c], [b], [a]) {[a], [b], [c]} = {[c], [b], [a]}

12. Entity Linkage using the Web and Graph-based Update Summarization 12NIH Lister Hill Medical Center Machine Learning – Create features String similarity, relationships (e.g. collaborators) – Then learn a model Naïve Bayes, Support Vector Machine, K-means, Agglomerative Clustering, … Yoojin Hong, Byung-Won On and Dongwon Lee. System Support for Name Authority Control Problem in Digital Libraries: OpenDBLP Approach. ECDL 2004. Sudha Ram, Jinsoo Park and Dongwon Lee. Digital Libraries for the Next Millennium: Challenges and Research Directions. Information Systems Frontiers 1999. Same Person?

13. Entity Linkage using the Web and Graph-based Update Summarization 13NIH Lister Hill Medical Center Graphical Methods: Social network analysis Nodes: entities Edges: relationships Y. Wang M.-Y. Kan D. Hsu J. C. Latombe A. Dhanik Y. F. TanL. Qiu T.-S. Chua T.-H. Chiang H. Cui Analysis Connected components Distance between nodes Node/edge centrality Cliques Bipartite subgraphs …

14. Entity Linkage using the Web and Graph-based Update Summarization 14NIH Lister Hill Medical Center Talk Outline Linkage using the Web – Introduction – Record linkage using internal knowledge >> Record linkage using search engines Search Engine Features Adaptive Queries Query Probing Update Summarization

15. Entity Linkage using the Web and Graph-based Update Summarization 15NIH Lister Hill Medical Center Record linkage using search engines Previously… – We assumed input data records contain sufficient information to perform linkage What if… – There is insufficient or only noisy information? – e.g., linking short forms to long forms Ask other people! – I.e., consult external (vs. internal) sources of knowledge – Use web as collective knowledge base

16. Entity Linkage using the Web and Graph-based Update Summarization 16NIH Lister Hill Medical Center Anatomy of Search Engine Results Number of results Ranked list Snippet URL Title Web page Programmatically accessible through APIs

17. Entity Linkage using the Web and Graph-based Update Summarization 17NIH Lister Hill Medical Center Derivable Features Counts – Co-occurrence measure between count(q1), count(q2) and count(q1 and q2) Hyperlinkage – Count of web pages of q1 point to pages of q2, and vice versa? – Incorporate additional indirect links with less weight (e.g., q 1  p  q2) Snippets or web pages – (Cosine) similarity using tokens – Counts of specific terms e.g. number of snippets for q1 containing the string q2 – Further natural language processing

18. Entity Linkage using the Web and Graph-based Update Summarization 18NIH Lister Hill Medical Center Web page features Named entities (NE) –We consider people, organizations, locations –Each NE token a feature NE-targeted (NE-T) –Motivation: middle names and titles –For NEs having a token of target name Extract tokens that are not in target name as features Born Edward Charles Morrice Fox in Chelsea, London… Charles, Chelsea, Morrice, Edward, Fox, London, … Charles, Morrice, …

19. Entity Linkage using the Web and Graph-based Update Summarization 19NIH Lister Hill Medical Center Using URLs Where web pages are located is also useful Hypothesis: If web pages of q1 and web pages of q2 overlap a lot, q1 and q2 are the same entity Measure this using URL / Host information Caveat: Not all hosts are equally telling – citeseer vs. harvard.edu for author names – pubmed vs. diabetes- info.com for diabetic terms Solution: Weight by Inverse Host Frequency

20. Entity Linkage using the Web and Graph-based Update Summarization 20NIH Lister Hill Medical Center URL Features (cont.) Page URLs Hypothesis: URL itself tells quite a lot Home page of “lindek” CS department, University of Alberta, Canada –MeURLin (Kan and Nguyen Thi, 2005) Tokens (http, www, cs, ualberta, ca, lindek) URI parts (scheme:http, hostname:cs, user:lindek, …) N-grams (ca ualberta, uaberta cs, cs www, www lindek) Length of tokens … http://www.cs.ualberta.ca/~lindek/

21. Entity Linkage using the Web and Graph-based Update Summarization 21NIH Lister Hill Medical Center Web search engine linkage Test whether q1 and q2 should be linked Hypothesis: Web pages of q1 and web pages of q2 share some representative data I Similar to disconnected triples: “Jeffrey D. Ullman” = 384K pgs “Jeffrey D. Ullman” + “aho” = 174K pgs “J. Ullman” = 124K pgs “J. Ullman” + “aho” = 41K pgs “Shimon Ullman” = 27.3K pgs “Shimon Ullman” + “aho”= 66 pgs q2q2 q1q1

22. Entity Linkage using the Web and Graph-based Update Summarization 22NIH Lister Hill Medical Center Evaluation - Full web pages in WEPS Goal –To compare the usefulness of various features for the Web People Search Task Architecture Input web pages Feature vectors Clusters Cosine similarity + Single link hierarchical agglomerative clustering + Minimum similarity threshold

23. Entity Linkage using the Web and Graph-based Update Summarization 23NIH Lister Hill Medical Center Evaluation F(α = 0.5) and similarity threshold 0.2

24. Entity Linkage using the Web and Graph-based Update Summarization 24NIH Lister Hill Medical Center Evaluation - Author Disambiguation Dataset –Manually-disambiguated dataset of 24 ambiguous names in computer science domain –Each ambiguous name represented 2 unique authors (k = 2) except for one where it represented 3 –Each name is attributed to 30 citations on average –Proportion of largest class ranges from 50% to 97% Search engine –Google (http://www.google.com/)

25. Entity Linkage using the Web and Graph-based Update Summarization 25NIH Lister Hill Medical Center Evaluation Single link performs best –Good for clustering citations from different publication pages together (some pages list only selected publications) –Some authors have disparate research areas, not well represented by a centroid vector Resolving hostnames to IP addresses give best accuracy Classification accuracy averaged over all names

26. Entity Linkage using the Web and Graph-based Update Summarization 26NIH Lister Hill Medical Center Discussion Per-name accuracies using single linkPer-name average number of URLs returned per citation

27. Entity Linkage using the Web and Graph-based Update Summarization 27NIH Lister Hill Medical Center Discussion Apparent correlation between accuracy and average number of URLs returned per citation –Author names with few URLs tend to fare poorly since results are mainly aggregator web sites What’s the cost? – Lots of queries needed – Web page downloads are expensive – Hence, slow Can we speed this up? Sure thing…

28. Entity Linkage using the Web and Graph-based Update Summarization 28NIH Lister Hill Medical Center Query probing Consider some publication venues: –Joint Conference on Digital Libraries –European Conference on Digital Libraries –Digital Libraries Query probing – Use common n-gram “digital libraries” as query probe – If we can obtain information on all three conferences, we save two queries

29. Entity Linkage using the Web and Graph-based Update Summarization 29NIH Lister Hill Medical Center Adaptive querying Combine two methods when needed Methods – M s : stronger method but very slow (e.g. web page similarity) – M w : weaker method but fast (e.g. host overlap) Aim – Accuracy close to M s – Significantly reduced running time than M s Algorithm – Execute M w – If heuristic suggests that M w results are likely incorrect Execute M s

30. Entity Linkage using the Web and Graph-based Update Summarization 30NIH Lister Hill Medical Center Entity Linkage - Conclusion Important problem with a rich history New external methods poll contextual evidence for judgment Need to combine methods to obtain best aspect of each

31. Entity Linkage using the Web and Graph-based Update Summarization 31NIH Lister Hill Medical Center Talk Outline Linkage using the Web >> Graph-based Update Summarization – Introduction – Timestamped Graphs – Evaluation and Conclusions “Now that all this data is linked, how do we process it?’’

32. Entity Linkage using the Web and Graph-based Update Summarization 32NIH Lister Hill Medical Center Applications of Summarization Decision Support Doing Less Work

33. Entity Linkage using the Web and Graph-based Update Summarization 33NIH Lister Hill Medical Center More seriously: an exciting challenge......put a book on the scanner, turn the dial to ‘2 pages’, and read the result......download 1000 documents from the web, send them to the summarizer, and select the best ones by reading the summaries of the clusters......forward the Japanese email to the summarizer, select ‘1 par’, and skim the translated summary. …get a weekly digest of new treatments and therapies for pressure ulcers An update task

34. Entity Linkage using the Web and Graph-based Update Summarization 34NIH Lister Hill Medical Center Simplifying summarization Select important sentences verbatim from the input text to form a summary – Input: A text document with k sentences – Output: Top n (n << k) sentences with the highest numeric scores (each sentence in the input document is assigned a numeric score) Extractive Summarization

35. Entity Linkage using the Web and Graph-based Update Summarization 35NIH Lister Hill Medical Center Summarization Heuristics for extractive summarization – Cue/stigma phrases – Sentence position (relative to document, section, paragraph) – Sentence length – TF×IDF, TF scores – Similarity (with title, context, query) Machine learning to tune weights by supervised learning Recently, graphical representations of text have shed new light on the summarization problem

36. Entity Linkage using the Web and Graph-based Update Summarization 36NIH Lister Hill Medical Center Revisiting Social Networks: Prestige One motivation was to model the problem as finding prestige of nodes in a social network PageRank: random walk In summarization, lead to TextRank and LexRank Did we leave anything out of our representation for summarization? Yes, the notion of an evolving network

37. Entity Linkage using the Web and Graph-based Update Summarization 37NIH Lister Hill Medical Center Social networks change! Natural evolving networks (Dorogovtsev and Mendes, 2001) – Citation networks: New papers can cite old ones, but the old network is static – The Web: new pages are added with an old page connecting it to the web graph, old pages may update links

38. Entity Linkage using the Web and Graph-based Update Summarization 38NIH Lister Hill Medical Center Talk Outline Linkage using the Web Graph-based Update Summarization – Introduction >> Timestamped Graphs – Evaluation and Conclusion

39. Entity Linkage using the Web and Graph-based Update Summarization 39NIH Lister Hill Medical Center Evolutionary models for summarization Writers and readers often follow conventional rhetorical styles - articles are not written or read in an arbitrary way Consider the evolution of texts using a very simplistic model – Writers write from the first sentence onwards in a text – Readers read from the first sentence onwards of a text A simple model: sentences get added incrementally to the graph

40. Entity Linkage using the Web and Graph-based Update Summarization 40NIH Lister Hill Medical Center Timestamped Graph Construction These assumptions suggest us to iteratively add sentences into the graph in chronological order. At each iteration, consider which edges to add to the graph. – For single document: simple and straightforward: add 1 st sentence, followed by the 2 nd, and so forth, until the last sentence is added – For multi-document: treat it as multiple instances of single documents, which evolve in parallel; i.e., add 1 st sentences of all documents, followed by all 2 nd sentences, and so forth NB: Doesn’t really model chronological ordering between articles, fix later

41. Entity Linkage using the Web and Graph-based Update Summarization 41NIH Lister Hill Medical Center Timestamped Graph Construction Model: Documents as columns – d i = document i Sentences as rows –s j = j th sentence of document

42. Entity Linkage using the Web and Graph-based Update Summarization 42NIH Lister Hill Medical Center Timestamped Graph Construction A multi document example doc1 doc2 doc3 sent1 sent2 sent3

43. Entity Linkage using the Web and Graph-based Update Summarization 43NIH Lister Hill Medical Center An example TSG: DUC 2007 D0703A-A

44. Entity Linkage using the Web and Graph-based Update Summarization 44NIH Lister Hill Medical Center These are just one instance of TSGs Let’s generalize and formalize them Def: A timestamped graph algorithm tsg(M) is a 9-tuple (d, e, u, f,σ, t, i, s, τ) that specifies a resulting algorithm that takes as input the set of texts M and outputs a graph G Properties of nodes Timestamped Graph Construction Properties of edges Input text transformation function

45. Entity Linkage using the Web and Graph-based Update Summarization 45NIH Lister Hill Medical Center Edge properties (d, e, u, f) Edge Direction (d) – Forward, backward, or undirected Edge Number (e) – number of edges to instantiate per timestep Edge Weight (u) – weighted or unweighted edges Inter-document factor (f) – penalty factor for links between documents in multi-document sets.

46. Entity Linkage using the Web and Graph-based Update Summarization 46NIH Lister Hill Medical Center Node properties ( σ, t, i, s) Vertex selection function σ(u, G) – One strategy: among those nodes not yet connected to u in G, choose the one with highest similarity according to u – Similarity functions: Jaccard, cosine, concept links (Ye et al.. 2005) Text unit type (t) – Most extractive algorithms use sentences as elementary units Node increment factor (i) – How many nodes get added at each timestep Skew degree (s) – Models how nodes in multi-document graphs are added – Skew degree = how many iterations to wait before adding the 1 st sentence of the next document – Skip for today…

47. Entity Linkage using the Web and Graph-based Update Summarization 47NIH Lister Hill Medical Center Timestamped Graph Construction Representations – We can model a number of different algorithms using this 9-tuple formalism: (d, e, u, f,σ, t, i, s,τ) – The given toy example: (f, 1, 0, 1, max-cosine-based, sentence, 1, 0, null) – LexRank graphs: (u, N, 1, 1, cosine-based, sentence, L max, 0, null) N = total number of sentences in the cluster; L max = the max document length i.e., all sentences are added into the graph in one timestep, each connected to all others, and cosine scores are given to edge weights

48. Entity Linkage using the Web and Graph-based Update Summarization 48NIH Lister Hill Medical Center System Overview Sentence splitting –Detect and mark sentence boundaries –Annotate each sentence with the doc ID and the sentence number –E.g., XIE19980304.0061: 4 March 1998 from Xinhua News; XIE19980304.0061-14: the 14 th sentence of this document Graph construction –Construct TSG in this phase

49. Entity Linkage using the Web and Graph-based Update Summarization 49NIH Lister Hill Medical Center System Overview Sentence Ranking – Apply topic-sensitive random walk on the graph to redistribute the weights of the nodes Sentence extraction – Extract the top-ranked sentences – Two different modified MMR re- rankers are used, depending on whether it is main or update task

50. Entity Linkage using the Web and Graph-based Update Summarization 50NIH Lister Hill Medical Center Talk Outline Linkage using the Web Graph-based Update Summarization – Introduction – Timestamped Graphs >> Evaluation and Conclusion

51. Entity Linkage using the Web and Graph-based Update Summarization 51NIH Lister Hill Medical Center Evaluation Dataset: DUC 2005, 2006 and 2007. Evaluation tool: ROUGE: n-gram based automatic evaluation Each dataset contains 50 or 45 clusters, each cluster contains a query and 25 documents Evaluate on some parameters – Do different e values affect the summarization process? e = 2 works best for DUC dataset – How do topic-sensitivity and edge weighting perform in running PageRank? Applying both seems to have best effect – How does skewing the graph affect the information flow in the graph? Skew of 1 works best, but need to try other possibilities

52. Entity Linkage using the Web and Graph-based Update Summarization 52NIH Lister Hill Medical Center Holistic Evaluation in DUC 2007 Extractive-based TSG system Used modified maximal marginal relevance for update tasks – Penalize links in previously read articles – Extension of inter- document factor (f) Cluster 1Cluster 2Cluster 3

53. Entity Linkage using the Web and Graph-based Update Summarization 53NIH Lister Hill Medical Center Evaluation Results Main task: 10th of 32 systems Update task: 3rd of 24 systems Conclusion TSG formalism better tailored to deal with update / incremental text tasks New method that may be competitive with current approaches – Other top scoring systems may do sentence compression (abstractive), not just extraction

54. Entity Linkage using the Web and Graph-based Update Summarization 54NIH Lister Hill Medical Center Graph-based Update Summary - Conclusion Proposed a timestamped graph model for text understanding and summarization – Adds sentences in an incremental fashion Future work: – Freely skewed model – Empirical and theoretical properties of TSGs

55. Entity Linkage using the Web and Graph-based Update Summarization 55NIH Lister Hill Medical Center Where do we go from here? Thank you! http://wing.comp.nus.edu.sg/ Organizing data around entities, events How people deal with data anyways Understand objects and their inter/intra-relationship Automation requires domain- expertise within a generic framework “Collate all studies on SBP2 that new findings in the last year.” “Oh, I meant the PROTEIN SBP2, not the gene.” “What other proteins does SBP2 bind to?” “Tell me more about the contradiction from previous results.” “Which Miller did the study on SBP2 in 2002?”

56. Backup Slides – Entity Linkage 50 Minute talk total 7 Apr 2008, 10 – 11 AM

57. Entity Linkage using the Web and Graph-based Update Summarization 57NIH Lister Hill Medical Center Social network analysis Connected triple Random walk Maximum flow Clustering x2x2 x1x1 x1x1 x2x2 x3x3 st

58. Entity Linkage using the Web and Graph-based Update Summarization 58NIH Lister Hill Medical Center Scalability Issues Pairwise comparisons –Requires O(n 2 ) time –Major bottleneck Possible solutions –Blocking techniques –Avoiding pairwise comparisons altogether Input: d 1, d 2, …, d n for i = 1 to n for j = (i + 1) to n compute sim(d i, d j )

59. Entity Linkage using the Web and Graph-based Update Summarization 59NIH Lister Hill Medical Center Cost-utility Framework cost of acquiring f i utility of acquiring f i feature f i known value value that can be acquired

60. Entity Linkage using the Web and Graph-based Update Summarization 60NIH Lister Hill Medical Center Record Matching TITLE_MIN_LEN TITLE_MAX_LEN AUTHOR_MIN_LEN AUTHOR_MAX_LEN VENUE_MIN_LEN VENUE_MAX_LEN TITLE_SIM AUTHOR_SIM VENUE_SIM MATCH/MISMATCH? Header-reference pair (instance) [1] Given information [2] Information that can be acquired at a cost Training data Assume all feature-values and their acquisition costs known Testing data Assume [1] known, but feature-values and their acquisition costs in [2] unknown Costs Set to MIN_LEN * MAX_LEN

61. Entity Linkage using the Web and Graph-based Update Summarization 61NIH Lister Hill Medical Center Costs and Utilities Costs –Trained 3 models (using M5’), treat as regression Utilities –Trained 2^3 = 8 classifiers (each to predict match/mismatch using only known feature-values) –For a test instance with a missing feature-value F Get confidence of appropriate classifier without F Get expected confidence of appropriate classifier with F Utility is difference between the two confidence scores Note –Similar to Saar-Tsechansky et al.

62. Entity Linkage using the Web and Graph-based Update Summarization 62NIH Lister Hill Medical Center Results Increasing proportion of feature-values acquired Increasing proportion of feature-values acquired Without cleaning of header recordsWith manual cleaning of header records

63. Entity Linkage using the Web and Graph-based Update Summarization 63NIH Lister Hill Medical Center Selected Bibliography General and surveys –Ivan P. Fellegi and Alan B. Sunter. A theory for record linkage. Journal of the American Statistical Association, 64(328):1183–1210, December 1969. –William E. Winkler and Yves Thibaudeau. An application of the Fellegi-Sunter Model of record linkage to the 1990 U.S. Decennial Census. Technical Report RR91/09, U.S. Bureau of the Census, 1991. –Ahmed K. Elmagarmid, Panagiotis G. Ipeirotis, and Vassilios S. Verykios. Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering (TKDE), 19(1):1–16, January 2007. –William E. Winkler. Overview of record linkage and current research directions. Technical Report RRS2006/02, U.S. Bureau of the Census, February 2006. –Mikhail Bilenko, Raymond J. Mooney, William W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. Adaptive name matching in information integration. IEEE Intelligent Systems, 18(5):16–23, January/February 2003. –Min-Yen Kan and Yee Fan Tan. Record Matching in Digital Library Metadata. To appear in Communications of the ACM (CACM).

64. Entity Linkage using the Web and Graph-based Update Summarization 64NIH Lister Hill Medical Center Selected Bibliography String matching –Robert A. Wagner and Michael J. Fischer. The string-to-string correction problem. Journal of the Association of Computing Machinery, 21(1):168–173, January 1974. –Saul B. Needleman and Christian D. Wunsch. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 148(3):443–453, March 1970. –Temple F. Smith and Michael S. Waterman. Identification of common molecular subsequences. Journal of Molecular Biology, 147(1):195–197, March 1981. –Andrés Marzal and Enrique Vidal. Computation of normalized edit distance and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):926–932, September 1993. –Alvaro E. Monge and Charles Elkan. The field matching problem: Algorithms and applications. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 267–270, August 1996. –Jie Wei. Markov edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):311–321, March 2004. –Mikhail Bilenko and Raymond J. Mooney. Adaptive duplicate detection using learnable string similarity measures. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 39–48, August 2003. –Andrew McCallum, Kedar Bellare, and Fernando Pereira. A Conditional Random Field For Discriminatively-Trained Finite-State String Edit Distance. In Conference on Uncertainty in Artificial Intelligence (UAI), July 2005. –William. W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. A comparison of string distance metrics for name-matching tasks. In Information Integration on the Web (IIWeb), pages 73–78, August 2003. –Ariel S. Schwartz and Marti A. Hearst. A simple algorithm for identifying abbreviation definitions in biomedical text. In Pacific Symposium on Biocomputing (PSB), pages 451–462, January 2003. –Youngja Park and Roy J. Byrd. Hybrid text mining for finding abbreviations and their definitions. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 126–133, June 2001. –Jeffrey T. Chang, Hinrich Schütze, and Russ B. Altman. Creating an online dictionary of abbreviations from MEDLINE. Journal of the American Medical Informatics Association, 9(6):612–620, November/December 2002. –Hiroko Ao and Toshihisa Takagi. ALICE: An algorithm to extract abbreviations from MEDLINE. Journal of the American Medical Informatics Association, 12(5):576–586, September/October 2005.

65. Entity Linkage using the Web and Graph-based Update Summarization 65NIH Lister Hill Medical Center Selected Bibliography Direct classification or clustering, and blocking –Hui Han, Hongyuan Zha, and C. Lee Giles. A model-based K-means algorithm for name disambiguation. In Workshop on Semantic Web Technologies for Searching and Retrieving Scientific Data, October 2003. –Hui Han, C. Lee Giles, Hongyuan Zha, Cheng Li, and Kostas Tsioutsiouliklis. Two supervised learning approaches for name disambiguation in author citations. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 296–305, June 2004. –Hui Han, Wei Xu, Hongyuan Zha, and C. Lee Giles. A hierarchical naive bayes mixture model for name disambiguation in author citations. In ACM Symposium on Applied Computing (SAC), pages 1065–1069, March 2005. –Hui Han, Hongyuan Zha, and C. Lee Giles. Name disambiguation in author citations using a K-way spectral clustering method. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 334–343, June 2005. –Dongwon Lee, Byung-Won On, Jaewoo Kang, and Sanghyun Park. Effective and scalable solutions for mixed and split citation problems in digital libraries. In ACM SIGMOD Workshop on Information Quality in Information Systems (IQIS), pages 69–76, June 2005. –Byung-Won On, Dongwon Lee, Jaewoo Kang, and Prasenjit Mitra. Comparative study of name disambiguation problem using a scalable blocking-based framework. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 344–353, June 2005. –Andrew McCallum, Kamal Nigam, and Lyle Ungar. Efficient clustering of high-dimensional data sets with application to reference matching. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 169–178, August 2000. –Matthew Michelson and Craig A. Knoblock. Learning blocking schemes for record linkage. In National Conference on Artificial Intelligence (AAAI), July 2006. –Mikhail Bilenko, Beena Kamath, and Raymond J. Mooney. Adaptive Blocking: Learning to Scale Up Record Linkage and Clustering. In IEEE International Conference on Data Mining (ICDM), December 2006.

66. Entity Linkage using the Web and Graph-based Update Summarization 66NIH Lister Hill Medical Center Selected Bibliography Graphical models –Jie Wei. Markov edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):311–321, March 2004. –John Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning (ICML), pages 282–289, June/July 2001. –Andrew McCallum and Ben Wellner. Object consolidation by graph partitioning with a conditionally- trained distance metric. In ACM SIGKDD Workshop on Data Cleaning, Record Linkage, and Object Consolidation, pages 19–24, August 2003. –Ben Wellner, Andrew McCallum, Fuchun Peng, and Michael Hay. An integrated, conditional model of information extraction and coreference with application to citation matching. In Conference on Uncertainty in Artificial Intelligence (UAI), pages 593–601, July 2004. –Andrew McCallum, Kedar Bellare, and Fernando Pereira. A Conditional Random Field For Discriminatively-Trained Finite-State String Edit Distance. In Conference on Uncertainty in Artificial Intelligence (UAI), July 2005. –Xin Dong, Alon Halevy, and Jayant Madhavan. Reference reconciliation in complex information spaces. In ACM SIGMOD International Conference on Management of Data, pages 85–96, June 2005. –Indrajit Bhattacharya and Lise Getoor. A latent dirichlet model for unsupervised entity resolution. In SIAM International Conference on Data Mining, pages 47–58, April 2006.

67. Entity Linkage using the Web and Graph-based Update Summarization 67NIH Lister Hill Medical Center Selected Bibliography Social network analysis –H. A. Kautz, B. Selman, and M. A. Shah. The hidden web. AI Magazine, 18(2):27–36, 1997. –P. Mutschke. Mining networks and central entities in digital libraries. A graph theoretic approach applied to co-author networks. In Intelligent Data Analysis (IDA), pages 155–166, August 2003. –M. E. J. Newman. Who is the best connected scientist? A study of scientific coauthorship networks. In Complex Networks, pages 337– 370, February 2004. –E. Otte and R. Rousseau. Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28(6), December 2002. –T. Krichel and N. Bakkalbasi. A social network analysis of research collaboration in the economics community. In International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, May 2006. –R. Rousseau and M. Thelwall. Escher staircases on the world wide web. First Monday, 9(6), June 2004. –D. G. Feitelson. On identifying name equivalences in digital libraries. Information Research, 9(4), October 2004. –R. Bekkerman and A. McCallum. Disambiguating web appearances of people in a social network. In International conference on World Wide Web (WWW), pages 463–470, May 2005. –R. Holzer, B. Malin, and L. Sweeney. Email alias detection using social network analysis. In Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD), August 2005. –B. Malin, E. Airoldi, and K. M. Carley. A network analysis model for disambiguation of names in lists. Computational and Mathematical Organization Theory, 11(2):119–139, July 2005. –G. Flake, S. Lawrence, and C. L. Giles. Efficient identification of web communities. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 150–160, August 2000. –P. K. Reddy and M. Kitsuregawa. An approach to build a cyber-community hierarchy. In SIAM ICDM Workshop on Web Analysis, April 2002. –Patrick Reuther. Personal name matching: New test collections and a social network based approach. Technical Report Mathematics/Computer Science 06-01, University of Trier, March 2006. –Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Keisuke Ishida, Takuichi Nishimura, Hideaki Takeda, Kôiti Hasida, and Mitsuru Ishizuka. POLYPHONET: an advanced social network extraction system from the web. In International conference on World Wide Web (WWW), pages 397-406, May 2006.

68. Entity Linkage using the Web and Graph-based Update Summarization 68NIH Lister Hill Medical Center Selected Bibliography Web-based methods –Jamie P. Callan, Margie E. Connell, and Aiqun Du. Automatic discovery of language models for text databases. In ACM SIGMOD International Conference on Management of Data, pages 479–490, June 1999. –Jamie P. Callan and Margie E. Connell. Query-based sampling of text databases. ACM Transactions on Information Systems (TOIS), 19(2):97–130, April 2001. –Panagiotis G. Ipeirotis and Luis Gravano. Distributed search over the hidden-web: Hierarchical database sampling and selection. In International Conference on Very Large Databases (VLDB), pages 394–405, August 2002. –Luis Gravano, Panagiotis G. Ipeirotis, and Mehran Sahami. QProber: A system for automatic classification of hidden-web databases. ACM Transactions on Information Systems (TOIS), 21(1):1–41, January 2003. –Aron Culotta, Ron Bekkerman, and Andrew McCallum. Extracting social networks and contact information from email and the web. In Conference on Email and Anti-Spam (CEAS), July 2004. –Philipp Cimiano, Siegfried Handschuh, and Steffen Staab. Towards the self-annotating web. In International conference on World Wide Web (WWW), pages 462–471, May 2004. –Philipp Cimiano, Günter Ladwig, and Steffen Staab. Gimme the context: Context-driven automatic semantic annotation with C-PANKOW. In International conference on World Wide Web (WWW), pages 332–341, May 2005. –Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Keisuke Ishida, Takuichi Nishimura, Hideaki Takeda, Kôiti Hasida, and Mitsuru Ishizuka. POLYPHONET: an advanced social network extraction system from the web. In International conference on World Wide Web (WWW), pages 397- 406, May 2006. –Yee Fan Tan, Min-Yen Kan, and Dongwon Lee. Search engine driven author disambiguation. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), June 2006. –Ergin Elmacioglu, Min-Yen Kan, Dongwon Lee, and Yi Zhang. Googled name linkage. 2007. –Yee Fan Tan, Ergin Elmacioglu, Min-Yen Kan, and Dongwon Lee. Record Linkage of Short Forms to Long Forms: A Case Study of Publication Venues. 2007. –Min-Yen Kan. Web page classification without the web page. In International conference on World Wide Web (WWW), pages 262–263, May 2004. –Min-Yen Kan and Hoang Oanh Nguyen Thi. Fast webpage classification using url features. In International Conference on Information and Knowledge Management (CIKM), pages 325–326, October/November 2005. –Panagiotis G. Ipeirotis, Eugene Agichtein, Pranay Jain, and Luis Gravano. To search or to crawl? Towards a query optimizer for text-centric tasks. In ACM SIGMOD International Conference on Management of Data, pages 265–276, June 2006.

69. Backup Slides - Summarization 50 Minute talk total 7 Apr 2008, 10 – 11 AM

70. Entity Linkage using the Web and Graph-based Update Summarization 70NIH Lister Hill Medical Center A Summarization Machine Summary MULTIDOCS ExtractAbstract Indicative Generic Background Query-oriented Just the news 10% 50% 100% Very Brief Brief Long Headline Informative DOC QUERY Generate a summary given a text document

71. Entity Linkage using the Web and Graph-based Update Summarization 71NIH Lister Hill Medical Center Summarization, defined Definitions Take a text document, extract content from it and present the most important content to the user in a condensed form and in a manner sensitive to the user’s or application’s needs : Summarization requires: – understanding the meaning of a text document – generating fluent text summary Studies of human summarizers –Cremmins (65) & Endres-Niggemeyer (98) showed that professional summarizers used clues to pick summary content.

72. Entity Linkage using the Web and Graph-based Update Summarization 72NIH Lister Hill Medical Center Skew Degree Examples time(d1) < time(d2) < time(d3) < time(d4) d1 d2 d3 d4 Skewed by 1Skewed by 2 Freely skewed d1 d2 d3 d4 Freely skewed = Only add a new document when it would be linked by some node using vertex function σ

73. Entity Linkage using the Web and Graph-based Update Summarization 73NIH Lister Hill Medical Center Input text transformation function (τ) Document Segmentation Function (τ) – Problem observed in some clusters where some documents in a multi-document cluster are very long – Takes many timestamps to introduce all of the sentences, causing too many edges to be drawn –Τ(G) segments long documents into several sub docs Solution is too hacked – hope to investigate more in current and future work d5 d5bd5a

74. Entity Linkage using the Web and Graph-based Update Summarization 74NIH Lister Hill Medical Center Evaluation on number of edges (e) Tried different e values Optimal performance: e = 2 At e = 1, graph is too loosely connected, not suitable for PageRank → very low performance At e = N, a LexRank system N N N e = 2

75. Entity Linkage using the Web and Graph-based Update Summarization 75NIH Lister Hill Medical Center Evaluation (other edge parameters) PageRank: generic vs topic-sensitive Edge weight (u): unweighted vs weighted Optimal performance: topic-sensitive PageRank and weighted edges Topic- sensitive Weighted edges ROUGE-1ROUGE-2 No 0.393580.07690 YesNo0.394430.07838 NoYes0.398230.08072 Yes 0.398450.08282

76. Entity Linkage using the Web and Graph-based Update Summarization 76NIH Lister Hill Medical Center Evaluation on skew degree (s) Different skew degrees: s = 0, 1 and 2 Optimal performance: s = 1 s = 2 introduces a delay interval that is too large Need to try freely skewed graphs Skew degreeROUGE-1ROUGE-2 00.369820.07580 10.372680.07682 20.369980.07489

77. Entity Linkage using the Web and Graph-based Update Summarization 77NIH Lister Hill Medical Center Describing Summaries Aspects of summarization (Sparck-Jones 97, Hovy and Lin 99) Input: – Single-document vs. multi-document Purpose – Situation: embedded in larger system (MT, IR) or not? – Generic vs. query-oriented: author’s view or user’s interest? – Indicative vs. informative: categorization or understanding? – Background vs. just-the-news: does user have prior knowledge? Output – Extract vs. abstract: use text fragments or re-phrase content?

78. Entity Linkage using the Web and Graph-based Update Summarization 78NIH Lister Hill Medical Center Differences for main and update task processing Main task: 1.Construct a TSG for input cluster 2.Run topic-sensitive PageRank on the TSG 3.Apply first modified version of MMR to extract sentences Update task: Cluster A: – Construct a TSG for cluster A – Run topic-sensitive PageRank on the TSG – Apply the second modified version of MMR to extract sentences Cluster B: – Construct a TSG for clusters A and B – Run topic-sensitive PageRank on the TSG; only retain sentences from B – Apply the second modified version of MMR to extract sentences Cluster C: – Construct a TSG for clusters A, B and C – Run topic-sensitive PageRank on the TSG; only retain sentences from C – Apply the second modified version of MMR to extract sentences

79. Entity Linkage using the Web and Graph-based Update Summarization 79NIH Lister Hill Medical Center Sentence Ranking Once a timestamped graph is built, we want to compute an prestige score for each node PageRank: use an iterative method that allows the weights of the nodes to redistribute until stability is reached Similarities as edges → weighted edges; query → topic-sensitive Topic sensitive (Q) portion Standard random walk term

80. Entity Linkage using the Web and Graph-based Update Summarization 80NIH Lister Hill Medical Center Sentence Extraction – Main task Original MMR: integrates a penalty of the maximal similarity of the candidate document and one selected document Ye et al. (2005) introduced a modified MMR: integrates a penalty of the total similarity of the candidate sentence and all selected sentences Score(s) = PageRank score of s; S = selected sentences This is used in the main task Penalty: All previous sentence similarity

81. Entity Linkage using the Web and Graph-based Update Summarization 81NIH Lister Hill Medical Center Sentence Extraction – Update task Update task assumes readers already read previous cluster(s) – implies we should not select sentences that have redundant information with previous cluster(s) Propose a modified MMR for the update task: – consider the total similarity of the candidate sentence with all selected sentences and sentences in previously-read cluster(s) P contains some top-ranked sentences in previous cluster(s) Previous cluster overlap

82. Entity Linkage using the Web and Graph-based Update Summarization 82NIH Lister Hill Medical Center References Günes Erkan and Dragomir R. Radev. 2004. LexRank: Graph-based centrality as salience in text summari-zation. Journal of Artificial Intelligence Research, (22). Rada Mihalcea and Paul Tarau. 2004. TextRank: Bring-ing order into texts. In Proceedings of EMNLP 2004. S.N. Dorogovtsev and J.F.F. Mendes. 2001. Evolution of networks. Submitted to Advances in Physics on 6th March 2001. Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual Web search engine. Com-puter Networks and ISDN Systems, 30(1-7). Jon M. Kleinberg. 1999. Authoritative sources in a hy-perlinked environment. In Proceedings of ACM-SIAM Symposium on Discrete Algorithms, 1999. Shiren Ye, Long Qiu, Tat-Seng Chua, and Min-Yen Kan. 2005. NUS at DUC 2005: Understanding docu-ments via concepts links. In Proceedings of DUC 2005.