Literatur zur Informationserschließung

Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.

© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)

Suchergebnisse

• 1
  Calegari, S. ; Pasi, G.: Personal ontologies : generation of user profiles based on the YAGO ontology.
  In: Information processing and management. 49(2013) no.3, S.640-658.
  
  Abstract: Personalized search is aimed at tailoring the search outcome to users; to this aim user profiles play an important role: the more faithfully a user profile represents the user interests and preferences, the higher is the probability to improve the search process. In the approaches proposed in the literature, user profiles are formally represented as bags of words, as vectors, or as conceptual taxonomies, generally defined based on external knowledge resources (such as the WordNet and the ODP - Open Directory Project). Ontologies have been more recently considered as a powerful expressive means for knowledge representation. The advantage offered by ontological languages is that they allow a more structured and expressive knowledge representation with respect to the above mentioned approaches. A challenging research activity consists in defining user profiles by a knowledge extraction process from an existing ontology, with the main aim of producing a semantically rich representation of the user interests. In this paper a method to automatically define a personal ontology via a knowledge extraction process from the general purpose ontology YAGO is presented; starting from a set of keywords, which are representatives of the user interests, the process is aimed to define a structured and semantically coherent representation of the user topical interests. In the paper the proposed method is described, as well as some evaluations that show its effectiveness.
  
  Inhalt: Vgl.: doi: 10.1016/j.ipm.2012.07.010.
  
  Anmerkung: Beitrag in einem Themenschwerpunkt "Personalization and recommendation in information access".
  
  Themenfeld: Wissensrepräsentation
  Objekt: YAGO
  
  
• 2
  Bast, H. ; Bäurle, F. ; Buchhold, B. ; Haussmann, E.: Broccoli: semantic full-text search at your fingertips.
  In: http://arxiv.org/pdf/1207.2615v2.pdf.
  
  Abstract: We present Broccoli, a fast and easy-to-use search engine forwhat we call semantic full-text search. Semantic full-textsearch combines the capabilities of standard full-text searchand ontology search. The search operates on four kinds ofobjects: ordinary words (e.g., edible), classes (e.g., plants), instances (e.g.,Broccoli), and relations (e.g., occurs-with or native-to). Queries are trees, where nodes are arbitrary bags of these objects, and arcs are relations. The user interface guides the user in incrementally constructing such trees by instant (search-as-you-type) suggestions of words, classes, instances, or relations that lead to good hits. Both standard full-text search and pure ontology search are included as special cases. In this paper, we describe the query language of Broccoli, a new kind of index that enables fast processing of queries from that language as well as fast query suggestion, the natural language processing required, and the user interface. We evaluated query times and result quality on the full version of the English Wikipedia (32 GB XML dump) combined with the YAGO ontology (26 million facts). We have implemented a fully functional prototype based on our ideas, see: http://broccoli.informatik.uni-freiburg.de.
  
  Themenfeld: Wissensrepräsentation
  Objekt: Broccoli ; YAGO ; Wikipedia
  
  
• 3
  Suchanek, F.M. ; Kasneci, G. ; Weikum, G.: YAGO: a large ontology from Wikipedia and WordNet.
  In: Web semantics: science, services and agents on the World Wide Web. 6(2008) no.3, S.203-217.
  
  Abstract: This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO's precision at 95%-as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO's data.
  
  Inhalt: Vgl.: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B758F-4TDJGCF-1&_user=10&_coverDate=09%2F30%2F2008&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1352884236&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=3285f9101276b6f41185a30c1b39d1d0.
  
  Themenfeld: Semantic Web ; Wissensrepräsentation
  Objekt: Yago ; Wikipedia ; WordNet
  
  
• 4
  Suchanek, F.M. ; Kasneci, G. ; Weikum, G.: YAGO: a core of semantic knowledge unifying WordNet and Wikipedia.
  In: http://www2007.org/papers/paper391.pdf.
  
  Abstract: We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as hasWonPrize). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.
  
  Inhalt: Beitrag für WWW 2007.
  
  Themenfeld: Semantic Web ; Wissensrepräsentation
  Objekt: Yago ; Wikipedia ; WordNet