Primjena grafova znanja temeljena na Wikidata repozitoriju

undergraduate thesis

• Title (english): Application of knowledge graphs based on Wikidata repository
• Author: Vlatko Novak
• Mentor: Tomislav Stipančić (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2020-02-28, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing; Artificial Intelligence
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mechanical Engineering

Abstract

Grafovi znanja postaju neizostavne komponente sustava strojne inteligencije, a njihovu važnost i potencijal u budućnosti najbolje se očituju činjenicom da Microsoft, Google, Facebook i ostali tehnološki giganti ulažu sve više novca u njihovo korištenje i razvoj. Grafovi znanja predstavljaju skup međusobno povezanih objekata i događaja iz stvarnog svijeta ili apstraktnih koncepata uz formalnu strukturu koja omogućava i ljudima i strojevima razumijevanje. Grafovi znanja glavni su alat semantičkog weba koji je nastavak na World Wide Web. Na semantičkom webu podaci su međusobno povezani tako da su strojno razumljivi. Za ostvarivanje grafova znanja i semantičkog weba potrebne su tehnologije: URI, XML, RDF, RDFS, OWL i SPARQL koje daju potrebne opise entiteta i znanje kako bi računalo razumjelo podatke što bliže kao ljudi.
Kako su podaci na Wikipediji bili razumljivi samo za ljude, tako je na principu grafova znanja nastala Wikidata, baza znanja namijenjena da informacije koje sadrži budu čitljive i ljudima i strojevima. To omogućava da se pomoću strukturiranih računalnih upita definiranim nekim od upitnih jezika (npr. SPARQL) kreiraju odgovori (grafovi znanja) u stvarnom vremenu u trenutku kada do baze znanja stigne pojedini upit te stoga predstavljaju trenutno znanje koje tehnički sustav može iskoristiti prilikom zaključivanja.

Abstract (english)

Knowledge graphs are becoming an irreplacable components of machine intelligence. Their importance and potential for the future is the most obvious as Microsoft, Google, Facebook and other tech giants are investing more and more money in their use and development. Knowledge graphs are a sets of interconnected objects and events from real world or abstract concepts with a formal structure that allows both humans and machines to understand. Knowledge graphs are the main tool of the semantic web, which is an extension of the World Wide Web. On the semantic web, data is interconnected so that is machine-readable. Knowledge graphs and semantic web require technologije like: URI, XML, RDF, RDFS, OWL and SPARQL that provide the necessary description of entities and knowledge to help the machine to understand the data as closely as humans.
As Wikipedia data was understandable to humans only, so on the principle of knowledge graphs Wikidata was created, a knowledge base designed to make information it contains legible to both humans and machines. This technology, with the help of structured computer queries defined by some of the query languages (eg SPARQL), allows Wikidata to create real-time answers (graphs of knowledge) at the very time when a query arrives at the knowledge base and therefore represent current knowledge that the technical system can use to conclude.

• Keywords: grafovi znanja; semantički web; strojna inteligencija; wikidata
• Keywords (english): knowledge graphs; semantic web; machine intelligence; wikidata
• Language: croatian
• URN:NBN: urn:nbn:hr:235:810750
• Study programme: Title: Mechanical Engineering; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2020-02-22 18:20:39