Andrew Krizhanovsky

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Andrew Krizhanovsky is an author from Russia.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Multilingual Ontology Matching based on Wiktionary Data Accessible via SPARQL Endpoint Proceedings of the 13th Russian Conference on Digital Libraries RCDL’2011 English 2011 Interoperability is a feature required by the Semantic Web. It is provided by the ontology matching methods and algorithms. But now ontologies are presented not only in English, but in other languages as well. It is important to use an automatic translation for obtaining correct matching pairs in multilingual ontology matching. The translation into many languages could be based on the Google Translate API, the Wiktionary database, etc. From the point of view of the balance of presence of many languages, of manually crafted translations, of a huge size of a dictionary, the most promising resource is the Wiktionary. It is a collaborative project working on the same principles as the Wikipedia. The parser of the Wiktionary was developed and the machine-readable dictionary was designed. The data of the machine-readable Wiktionary are stored in a relational database, but with the help of D2R server the database is presented as an RDF store. Thus, it is possible to get lexicographic information (definitions, translations, synonyms) from web service using SPARQL requests. In the case study, the problem entity is a task of multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint. Ontology matching results obtained using Wiktionary were compared with results based on Google Translate API. 5 0
Index wiki database: design and experiments Corpus linguistics
Inverted index
Zipf's law
Information retrieval
FLINS'08, Corpus Linguistics'08, AIS/CAD'08 2008 With the fantastic growth of Internet usage, information search in documents of a special type called a "wiki page" that is written using a simple markup language, has become an important problem. This paper describes the software architectural model for indexing wiki texts in three languages (Russian, English, and German) and the interaction between the software components (GATE, Lemmatizer, and Synarcher). The inverted file index database was designed using visual tool DBDesigner. The rules for parsing Wikipedia texts are illustrated by examples. Two index databases of Russian Wikipedia (RW) and Simple English Wikipedia (SEW) are built and compared. The size of RW is by order of magnitude higher than SEW (number of words, lexemes), though the growth rate of number of pages in SEW was found to be 12% higher than in Russian, and the rate of acquisition of new words in SEW lexicon was 6% higher during a period of five months (from September 2007 to February 2008). The Zipf's law was tested with both Russian and Simple Wikipedias. The entire source code of the indexing software and the generated index databases are freely available under GPL. 0 0