Semantic Search Algorithms and Applications Michael Schroeder Bio

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Semantic Search: Algorithms and Applications Michael Schroeder Bio. Technological Center TU Dresden Biotec

Semantic Search: Algorithms and Applications Michael Schroeder Bio. Technological Center TU Dresden Biotec

Syllabus The module deals with practical applications of logic and reasoning. The course "semantic

Syllabus The module deals with practical applications of logic and reasoning. The course "semantic search" deals with a novel search paradigm that uses background knowledge in the form of ontologies. The course introduces the necessary cocnepts from information retrieval and text-mining to realize ontology learning and alignments and ontologybased search. By Michael Schroeder, Biotec, 2006 2

Practicals n Two types: n Pen and paper exercises n Programming tasks By Michael

Practicals n Two types: n Pen and paper exercises n Programming tasks By Michael Schroeder, Biotec, 2006 3

Assessment n Written exam counting 80% of final mark n Practicals: Labs and Programming

Assessment n Written exam counting 80% of final mark n Practicals: Labs and Programming task counting 20% n Do labs n Build a semantic search engine (group work) n Strategy for preparation n Follow slides n Do all labs and homework By Michael Schroeder, Biotec, 2006 4

Getting in touch Email: {ms, george. tsatasronis}@biotec. dresden. de Web site: http: //www. biotec.

Getting in touch Email: {ms, george. tsatasronis}@biotec. dresden. de Web site: http: //www. biotec. tu dresden. de Click Schroeder->Group->Teaching Includes web site for ILS module Bioinformatics group: Structural protein interactions and functional annotation with ontologies, textmining, rules Example: Ontology-based literature search at ww. gopubmed. org By Michael Schroeder, Biotec, 2006 5

Part I: Books and Papers n Natual language processing n Manning and Schütze. Foundations

Part I: Books and Papers n Natual language processing n Manning and Schütze. Foundations of statsitical natural language processing n Gene mention normalization and interaction extraction with context models and sentence motifs. Hakenberg et al. , Genome Biology, 2008 n Information retrieval n Manning, Raghavan, Schütze. Introduction to information retrieval. Book is online at http: //nlp. stanford. edu/IR-book/information-retrievalbook. html n Ontology learning and alignment: n P Cimiano. Ontology learning and population from text. Springer n Bio ontologies: n The Gene Ontology (GO) project in 2006. Gene Ontology Consortium. Nucleic Acids Res. 2006 Jan 1; 34(Database issue): D 322 -6 By Michael Schroeder, Biotec, 2006 6