A Technical Introduction to the Semantic Search Engine SeMedico

Size: px

Start display at page:

Download "A Technical Introduction to the Semantic Search Engine SeMedico"

Hortense Arnold
6 years ago
Views:

Talk in the Semesterprojekt Entwicklung einer Suchmaschine für Alternativmethoden zu Tierversuchen January 12, 2018 Humboldt-Universität zu Berlin A Technical Introduction to the Semantic Search

1 Talk in the Semesterprojekt Entwicklung einer Suchmaschine für Alternativmethoden zu Tierversuchen January 12, 2018 Humboldt-Universität zu Berlin A Technical Introduction to the Semantic Search Engine SeMedico Erik Fäßler Jena University Language & Information Engineering (JULIE) Lab Friedrich Schiller University Jena, Jena, Germany Erik Fäßler Technical Introduction to Semedico 1

2 SeMedico Front Page Erik Fäßler Technical Introduction to Semedico 2

3 SeMedico Auto Completion Erik Fäßler Technical Introduction to Semedico 3

4 SeMedico Result View I Erik Fäßler Technical Introduction to Semedico 4

5 SeMedico Result View II Erik Fäßler Technical Introduction to Semedico 5

6 SeMedico System Overview MEDLINE Doc Doc Doc JULIE Lab Server PostgreSQL NCBI Gene ElasticSearch Concept Database C R A E A E A E C O Frontend (Tapestry / JavaScript) SeMedico Web Application Java Servlet Erik Fäßler Technical Introduction to Semedico 6

7 MEDLINE Document Storage I MEDLINE comes in (G)ZIPed XML 30K documents per file <PubmedArticleSet> <PubmedArticle><MedlineCitation> <PMID> </PMID> <Article> <Journal>...</Journal> <ArticleTitle>...</ArticleTitle> <Abstract>...</Abstract> <AuthorList>...</AuthorList> <MeshHeadings>...</MeshHeadings> </Article> </MedlineCitation> <MedlineCitation> <PMID>...</PMID>... </MedlineCitation></PubmedArticle> </PubmedArticleSet> Erik Fäßler Technical Introduction to Semedico 7

8 MEDLINE Document Storage II Import of MEDLINE citations into database table Size of MEDLINE: 27M abstracts pmid xml <MedlineCitation><PMID> </PMID>...</MedlineCitation> <MedlineCitation><PMID> </PMID>...</MedlineCitation> <MedlineCitation><PMID> </PMID>...</MedlineCitation> <MedlineCitation><PMID> </PMID>...</MedlineCitation> Erik Fäßler Technical Introduction to Semedico 8

From the Database into the Pipeline I pmid xml 1 1234567 <MedlineCitation><PMID>1234567</PMID>...</MedlineCitation> 2 1729454 <MedlineCitation><PMID>1729454</PMID>.

9 From the Database into the Pipeline I pmid xml <MedlineCitation><PMID> </PMID>...</MedlineCitation> <MedlineCitation><PMID> </PMID>...</MedlineCitation> <MedlineCitation><PMID> </PMID>...</MedlineCitation> <MedlineCitation><PMID> </PMID>...</MedlineCitation> MEDLINE Doc Doc Doc JULIE Lab Server PostgreSQL NCBI Gene ElasticSearch Concept Database C R A E A E A E C O Frontend (Tapestry / JavaScript) SeMedico Web Application Java Servlet Erik Fäßler Technical Introduction to Semedico 9

10 From the Database into the Pipeline II JULIE Lab Server PostgreSQL UIMA Medline DB Reader DB concurrency handling Parsing of XML Populating UIMA CAS instance Title / Abstract Authors Journal Info etc. Common Analysis System CAS to text analysis components Erik Fäßler Technical Introduction to Semedico 10

11 SeMedico System Overview MEDLINE Doc Doc Doc JULIE Lab Server PostgreSQL NCBI Gene ElasticSearch Concept Database C R A E A E A E C O Frontend (Tapestry / JavaScript) SeMedico Web Application Java Servlet Erik Fäßler Technical Introduction to Semedico 11

12 SeMedico UIMA JCoRe Pipeline I from reader Sentences Tokens Abbreviations Parts of Speech Semantic layer Species (LINNAEUS) GeNo: Genes/Proteins Recognition Normalization (NCBI Gene) Molecular Event Extraction (BioSem) Event Certainty Assessment Scale 1 to 6 1: Negation 6: No doubt MeSH Terms (Dictionary) Ontology classes (GO, GRO; Dictionary) to consumer Hahn & Matthies et al., LREC 2016 Erik Fäßler Technical Introduction to Semedico 12

http ElasticSearch CAS title abstract species genes events transformation API { } preanalyzed

13 SeMedico UIMA JCoRe Pipeline II from analysis pipeline ElasticSearch CAS Consumer Transforms CAS into preanalyzed JSON document Transformation configurable via API JULIE Lab ES plugin required http ElasticSearch CAS title abstract species genes events transformation API { } preanalyzed JSON title : { }, abstract : { }, authors : { }, : { } Erik Fäßler Technical Introduction to Semedico 13

14 Full texts from Pubmed Central SeMedico integrates the open access subset of PMC Use a specific reader from JCoRe: jcore-pmc-reader The rest of the analysis is basically the same But: Matthies, Franz, & Hahn, Udo (2017). Scholarly information extraction is going to make a quantum leap with PubMed Central (PMC) But moving from abstracts to full texts seems harder than expected. in: MedInfo 2017: Precision Healthcare through Informatics Proceedings of the 16th World Congress on Medical and Health Informatics. Hangzhou, China, August 2017, Erik Fäßler Technical Introduction to Semedico 14

15 SeMedico System Overview MEDLINE Doc Doc Doc JULIE Lab Server PostgreSQL NCBI Gene ElasticSearch Concept Database C R A E A E A E C O Frontend (Tapestry / JavaScript) SeMedico Web Application Java Servlet Erik Fäßler Technical Introduction to Semedico 15

16 Concept Database I Name Description Number of Concepts Medical Subject Headings (MeSH) Biomedical vocabulary, multihierarchy 26K MeSH Supplementary Concepts Chemicals, proteins etc. connected to MeSH 150K NCBI Gene Gene Database 650K (in SeMedico) NCBI Taxonomy Taxonomical classification of species 1.1M Gene Ontology (GO) Ontology about gene products and related processes Gene Regulation Ontology (GRO) Ontology about gene regulation processes 50K 507 Erik Fäßler Technical Introduction to Semedico 16

17 Concept Database II Concepts are arranged taxonomically Squamous Cell Carcinoma IS-A Carcinoma Neo4j is a graph database Terminologies and arbitrary relations between concepts can be modeled explicitly Appropiate query language: Get descendants of concept Compute shortest path between two concepts Erik Fäßler Technical Introduction to Semedico 17

18 Neo4j Example Graph Tauopathies type 1 type 4 type 2 type 3 Erik Fäßler Technical Introduction to Semedico 18

19 Neo4j Concept Node Properties Erik Fäßler Technical Introduction to Semedico 19

20 Zooming Out Erik Fäßler Technical Introduction to Semedico 20

Concept IDs Concept Database tid2341 tid42

query: match: tid914 facet tid42 : { name :

abstract species ncbitax:9606 genes mtor

abstract : {[ human, tid914, mtor, tid42 ]}

21 Concept IDs Concept Database tid2341 tid42 tid914 SeMedico Web Application Java Servlet query: match: tid914 facet tid42 : { name : mtor, synonym : FRAP, description : } CAS abstract species ncbitax:9606 genes mtor ncbigene:2475 transformation API { } JSON abstract : {[ human, tid914, mtor, tid42 ]} ElasticSearch Erik Fäßler Technical Introduction to Semedico 21

22 ElasticSearch I Manages Lucene index Seamless index updates, no downtime Easy to use index distribution model Full text search Faceting Highlighting Erik Fäßler Technical Introduction to Semedico 22

the same on sent document text How to integrate UIMA? First idea: Create a Lucene UIMA analyzer, but Moves (a lot!

23 ElasticSearch II? Lucene generates index terms via text analysis Tokenization, case folding, synonym enrichment, stemming ElasticSearch does the same on sent document text How to integrate UIMA? First idea: Create a Lucene UIMA analyzer, but Moves (a lot!) processing requirements into the ElasticSearch cluster Requires to load dictionaries, machine learning models Memory that is lost to Lucene and ElasticSearch Overall: Diminishes search performance Erik Fäßler Technical Introduction to Semedico 23

24 ElasticSearch III JULIE Lab ElasticSearch plugin to exactly specify index terms without ES-internal analysis Employs the JSON format created for the Solr JsonPreAnalyzedParser JsonPreAnalyzedParser Created by JULIE Lab internal (currently) CAS consumer Erik Fäßler Technical Introduction to Semedico 24

25 ElasticSearch IV Preanalyzed Format {"v":"1", "str":"immunohistochemistry performed to evaluate the expression of phosphorylated mtor (p-mtor), phosphorylated p70s6k (p-p70s6k), phosphorylated 4E-binding protein 1 (p-4e-bp1), and Ki-67 using 105 surgically resected ESCC correlated with treatment outcome.", "tokens":[ {"t": immunohistochemistry","s :0,"e :20,"i":1}, {"t": tid94702","s :0,"e :20,"i :0}, {"t": perform","s :21,"e :30,"i":1}, {"t": evaluat","s :34,"e :42,"i":1}, ] } {"t": event","s :34,"e :42,"i :0}, Erik Fäßler Technical Introduction to Semedico 25

26 ElasticSearch V Simple Query { "query": { "bool": { "must": [ { "match": { "abstracttext": { "query": cancer }}}, { "nested": { "path": "events", "inner_hits": {}, "query": { "bool": { "must": [{ "match": { "events.allarguments": "mtor" }}], "filter": { "range": { "events.likelihood": { "lte": 5}}}}}}}]}}, "fields": [ "abstracttext", "title" ] } Erik Fäßler Technical Introduction to Semedico 26

27 ElasticSearch VI Concept Query { "query": { "bool": { "must": [ { "match": { "abstracttext": { "query": tid52310 }}}, { "nested": { "path": "events", "inner_hits": {}, "query": { "bool": { "must": [{ "match": { "events.allarguments": tid42" }}], "filter": { "range": { "events.likelihood": { "lte": 5}}}}}}}]}}, "fields": [ "abstracttext", "title" ] } Erik Fäßler Technical Introduction to Semedico 27

28 ElasticSearch VII Highlighting Erik Fäßler Technical Introduction to Semedico 28

29 References Semedico Faessler, Erik, & Hahn, Udo (2017). SEMEDICO: A comprehensive semantic search engine for the life sciences. in: ACL 2017 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Vancouver, British Columbia, Canada, August 1, 2017, GeNo Wermter, Joachim, & Tomanek, Katrin, & Hahn, Udo (2009). High-performance gene name normalization with GeNo. in: Bioinformatics, 25, BioSem Bui, Q., Mulligen, E. van, Campos, D., & Kors, J. (2013). A Fast Rule-based Approach for Biomedical Event Extraction. In Proceedings of the BioNLP 2013 Shared Task Workshop (pp ). Sofia, Bulgaria: Association for Computational Linguistics. Certainty Assessment Engelmann, Christine, & Hahn, Udo (2014). An empirically grounded approach to extend the linguistic coverage and lexical diversity of verbal probabilities. in: CogSci Proceedings of the 36th Annual Cognitive Science Conference. Cognitive Science Meets Artificial Intelligence: Human and Artificial Agents in Interactive Contexts. Québec City, Québec, Canada, July 23-26, 2014., JCoRe Hahn, Udo, & Matthies, Franz, & Faessler, Erik, & Hellrich, Johannes (2016). UIMA-based JCoRe 2.0 goes GitHub and Maven Central: State-of-the-art software resource engineering and distribution of NLP pipelines. in: LREC 2016 Proceedings of the 10th International Conference on Language Resources and Evaluation. Portorož, Slovenia, May 2016, Erik Fäßler Technical Introduction to Semedico 29

30 Conclusion MEDLINE Doc Doc Doc JULIE Lab Server PostgreSQL NCBI Gene ElasticSearch Concept Database C R A E A E A E C O Frontend (Tapestry / JavaScript) SeMedico Web Application Java Servlet Erik Fäßler Technical Introduction to Semedico 30

31 Talk in the Semesterprojekt Entwicklung einer Suchmaschine für Alternativmethoden zu Tierversuchen January 12, 2018 Humboldt-Universität zu Berlin A Technical Introduction to the Semantic Search Engine SeMedico Erik Fäßler Jena University Language & Information Engineering (JULIE) Lab Friedrich Schiller University Jena, Jena, Germany Erik Fäßler Technical Introduction to Semedico 31

Visualizing Semantic Metadata from Biological Publications

Visualizing Semantic Metadata from Biological Publications Johannes Hellrich, Erik Faessler, Ekaterina Buyko and Udo Hahn Jena University Language and Information Engineering (JULIE) Lab Friedrich-Schiller-Universität