Inhalt

[ 921CGELWSMK13 ] KV (*)Web Search and Mining

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Birgit Pröll 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Computer Science 2025W
Lernergebnisse
Kompetenzen
(*)Students are able to analyze, evaluate and employ techniques, tools and frameworks in Web Search and Web Mining.
Fertigkeiten Kenntnisse
(*)Students

  • understand the fundamentals of Web Search, eg, crawling, weighting. (K2)
  • understand the architecture and components of Search Engines and can evaluate them. (K2, K4, K5)
  • are able to analyse, evaluate, configure and employ site search tools and web scrapers. (K3, K4)
  • are capable of developing information extraction and opinion mining applications on web content using current frameworks and libraries. (K6)
  • understand the principles of question answering systems and chatbots. (K2)
(*)1) Web Search

  • Information Retrieval „in a Nutshell“
  • Web Search Challenges
  • Search Engines: Google, et al.
  • Web Crawling
  • Weighting and Ranking: PageRank etc.
  • Search Engines Evaluation
  • Site Search
  • Engine Optimization (SEO)
  • Search Engine User Interfaces
  • Social Media Search
  • Deep Web Search
  • Spam and Defacement Detection
  • Tools and Applications

2) Web Mining

  • Information Extraction „in a Nutshell“
  • Web Information Extraction Challenges
  • Web Scraping
  • Knowledge-based Extraction
  • Social Media Mining (microblogs,, etc.), Sentiment Analysis
  • Tools and Applications

3) Question Answering & Chatbots

  • Approaches and Architectures
  • Tools and Applications
Beurteilungskriterien (*)exercises, exam, in-class contribution
Lehrmethoden (*)slide presentation (slides on Moodle), exercises (group work)
Abhaltungssprache Englisch
Literatur (*)
  • Croft, Donald Metzler, Trevor Strohman: Search Engines – Information Retrieval in Practice, Pearson 2010
  • Daniel Jurafsky, James H. Martin: Speech and Language Processing: An Introduction to Natural Language Processing, Prentice Hall 2000
  • Marti A. Hearst: Search User Interfaces, Cambridge University Press 2009
Lehrinhalte wechselnd? Nein
Äquivalenzen (*)INMAWKVWEIR: KV Web Information Retrieval (3 ECTS) bzw. 921INFWWIRK12: KV Web Information Retrieval and Extraction (3 ECTS)
Präsenzlehrveranstaltung
Teilungsziffer -
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