1. Julia Stoyanovich, William Mee, Kenneth A. Ross New England DB Summit 2010 Semantic Ranking and Result Visualization for Life Sciences Publications

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3. 3 Data and Query Processing PubMed corpus –over 19 million articles and growing –articles annotated with MeSH terms –annotators are instructed to annotate with the most specific term possible Medical Subject Headings (MeSH) annotations –over 25K term descriptors –organized into a polyhierarchy –17 trees, almost no cycles Entrez search engine –query translation, synonym & ontology expansions mosquito -> "culicidae"[MeSH Terms] OR "culicidae"[All Fields] OR "mosquito"[All Fields]

4. 4 Connective Tissue DiseasesAutoimmune Diseases Rheumatic Diseases Diseases Skin & Connective Tissue Diseases RA Felty’s Sy Arthritis, Juvenile Rheumatoid Nodule Still’s Disease, Adult-Onset Skin Diseases …. … Immune System Diseases …. RASLE …. Lupus Nephritis Lupus Vasculitis Lupus Nephritis Lupus Vasculitis Felty’s Sy Sjögren’s Sy Still’s Disease, Adult-Onset Arthritis, Juvenile Rheumatoid Caplan Sy Sjögren’s Sy Scleroderma, Systemic …. MeSH: A Scoped Polyhierarchy

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6. 6 CC T A D B E GHF G F Q = { C }D = { G, E } term-scope(D) F E G C term-scope(Q) G H G C H E F term-similarity (Q, D) = | term-scope(Q)  term-scope(D) | Semantics of Query Relevance

7. 7 Q = { E, B } D = { F, G } CC T A D B E GHF G F term-similarity (Q, D) = 2 term-scope(Q) G C E term-scope(D) F B But F contributes to both query terms, while G only contributes to one! Idea: count occurrences of document terms within the context of query terms. Semantics of Query Relevance

8. 8 conditional-similarity (Q, D): count the # of ancestor-descendant pairs balanced-similarity (Q, D): normalize the contribution of each query term CC T A D B E GHF G F BCEFGFG Semantics of Query Relevance Q = { E, B } D = { F, G }

9. 9 Q = { q 1, …, q n }D = { d 1, …, d m } 1.term-scope (Q) = term-scope(q 1 )  …  term-scope( q n ) 2.term-scope (D) = term-scope(d 1 )  …  term-scope(d m ) 3.term-similarity (Q, D) = | term-scope(Q)  term-scope(D) | Can be expensive for queries, documents with large term scopes! | (A  B)  (Y  Z) | = | (A  Y)  (A  Z)  (B  Y)  (B  Z) | < |A  Y| + |A  Z| + |B  Y| + |B  Z| Pre-compute term-similarity (s,t) for all (s,t) –Practical, since 160K pairs have term-similarity(s,t) > 0, out of over 600M At query time – Compute score upper-bounds for all documents – Compute term-similarity only for the promising documents Useful upper-bounds also hold for conditional and balanced-similarity Computation of term-similarity abc abcc

10. 10 System Architecture batch 3 Query Manager 1 query eUtils API batch 2 batch 1 Java RMI 2 3 4 In-memory DB PubMed

11. 11 Performance: Ranked Retrieval * results for 150 queries in our workload

12. 12 Performance: Ranked Retrieval * results are cumulative over 150 queries

13. 13 Performance: Skyline for term-similarity * large queries > 20K results; 30% of the workload, 75% of the time

14. 14 User study –8 users, researchers in medicine, biology, bioinformatics 1 query per user, total 670 individual, 335 pair-wise relevance judgments conducted free-form interviews with some users –2 baselines distance-based information-theoretic Quantitative analysis of results –We appear to outperform baselines for queries with polyhierarchy features –Baselines appear to outperform our measures for several other queries –For some queries no measure correlated with user’s perception of quality Qualitative analysis of results –Many aspects inform a user’s judgment, ontology is one of them –Both general and specific concepts are important Plan to scale up the evaluation by making our system available to the scientific community at large Evaluation of Effectiveness

15. 15 Related Work Hierarchy-based similarity measures –[Ganesan et al, 2003] compare sets / multisets of terms, leaf nodes, hierarchy is a tree –[Rada & Bicknell, 1989] distance is a mean-path length between pairs of query & document terms –[Lin & Kim, 1993; Resnik, 1995] information-theoretic measures, typically distance via ancestor Weighted set similarity [Hadjieleftheriou, 2007] Bibliographic search in life sciences –Entrez, GoPubMed, NextBio Efficient computation of skylines –[Bentley 1980; Borzsonyi et al 2001; ….]

16. 16 Contributions Similarity measures for scoped polyhierarchies –Distance is via descendants, not via ancestors –Scoping is exploited –Alternative semantics of combining contributions of individual terms to the score Efficient computation of similarity using score upper-bounds Efficient computation of a 2D skyline using score upper-bounds, with lazy evaluation of coordinates Experimental evaluation –Efficiency –User study

17. 17 Thank you!

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