Tereza Iofciu

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Tereza Iofciu is an author.

Publications

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
Exploiting click-through data for entity retrieval Entity retrieval
Evaluation
Query log analysis
User session
Wikipedia
SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval English 2010 We present an approach for answering Entity Retrieval queries using click-through information in query log data from a commercial Web search engine. We compare results using click graphs and session graphs and present an evaluation test set making use of Wikipedia "List of" pages. 0 0
Overview of the INEX 2009 entity ranking track Lecture Notes in Computer Science English 2010 In some situations search engine users would prefer to retrieve entities instead of just documents. Example queries include "Italian Nobel prize winners", "Formula 1 drivers that won the Monaco Grand Prix", or "German spoken Swiss cantons". The XML Entity Ranking (XER) track at INEX creates a discussion forum aimed at standardizing evaluation procedures for entity retrieval. This paper describes the XER tasks and the evaluation procedure used at the XER track in 2009, where a new version of Wikipedia was used as underlying collection; and summarizes the approaches adopted by the participants. 0 0
Why finding entities in Wikipedia is difficult, sometimes Information retrieval English 2010 Entity Retrieval (ER)—in comparison to classical search—aims at finding individual entities instead of relevant documents. Finding a list of entities requires therefore techniques different to classical search engines. In this paper, we present a model to describe entities more formally and how an ER system can be build on top of it. We compare different approaches designed for finding entities in Wikipedia and report on results using standard test collections. An analysis of entity-centric queries reveals different aspects and problems related to ER and shows limitations of current systems performing ER with Wikipedia. It also indicates which approaches are suitable for which kinds of queries. 0 0
L3S at INEX 2008: Retrieving entities using structured information Lecture Notes in Computer Science English 2009 Entity Ranking is a recently emerging search task in Information Retrieval. In Entity Ranking the goal is not finding documents matching the query words, but instead finding entities which match those requested in the query. In this paper we focus on the Wikipedia corpus, interpreting it as a set of entities and propose algorithms for finding entities based on their structured representation for three different search tasks: entity ranking, list completion, and entity relation search. The main contribution is a methodology for indexing entities using a structured representation. Our approach focuses on creating an index of facts about entities for the different search tasks. More, we use the category structure information for improving the effectiveness of the List Completion task. 0 0
Time based tag recommendation using direct and extended users sets CEUR Workshop Proceedings English 2009 Tagging resources on the Web is a popular activity of standard users. Tag recommendations can help such users assign proper tags and automatically extend the number of annotations available in order to improve, for example, retrieval effectiveness for annotated resources. In this paper we focus on the application of an algorithm designed for Entity Retrieval in the Wikipedia setting. We show how it is possible to map the hyperlink and category structure of Wikipedia to the social tagging setting. The main contribution is a time-based methodology for recommending tags exploiting the structure in the dataset without knowledge about the content of the resources. 0 0
A model for Ranking entities and its application to Wikipedia Proceedings of the Latin American Web Conference, LA-WEB 2008 English 2008 Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper we propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and Wikipedia scenarios. Since searching for entities on Web scale repositories is an open challenge as the effectiveness of ranking is usually not satisfactory, we present a set of algorithms based on our model and evaluate their retrieval effectiveness. The results show that combining simple Link Analysis, Natural Language Processing, and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@ 10 and 35% for MAP. 0 0
L3S at INEX 2007: Query expansion for entity ranking using a highly accurate ontology Lecture Notes in Computer Science English 2008 Entity ranking on Web scale datasets is still an open challenge. Several resources, as for example Wikipedia-based ontologies, can be used to improve the quality of the entity ranking produced by a system. In this paper we focus on the Wikipedia corpus and propose algorithms for finding entities based on query relaxation using category information. The main contribution is a methodology for expanding the user query by exploiting the semantic structure of the dataset. Our approach focuses on constructing queries using not only keywords from the topic, but also information about relevant categories. This is done leveraging on a highly accurate ontology which is matched to the character strings of the topic. The evaluation is performed using the INEX 2007 Wikipedia collection and entity ranking topics. The results show that our approach performs effectively, especially for early precision metrics. 0 0
Semantically enhanced entity ranking Lecture Notes in Computer Science English 2008 Users often want to find entities instead of just documents, i.e., finding documents entirely about specific real-world entities rather than general documents where the entities are merely mentioned. Searching for entities on Web scale repositories is still an open challenge as the effectiveness of ranking is usually not satisfactory. Semantics can be used in this context to improve the results leveraging on entity-driven ontologies. In this paper we propose three categories of algorithms for query adaptation, using (1) semantic information, (2) NLP techniques, and (3) link structure, to rank entities in Wikipedia. Our approaches focus on constructing queries using not only keywords but also additional syntactic information, while semantically relaxing the query relying on a highly accurate ontology. The results show that our approaches perform effectively, and that the combination of simple NLP, Link Analysis and semantic techniques improves the retrieval performance of entity search. 0 0
Extracting Semantic Relationships between Wikipedia Categories Semantic wikipedia 1st Workshop on Semantic Wikis:, 2006. 2006 The Wikipedia is the largest online collaborative knowledge sharing system, a free encyclopedia. Built upon traditional wiki architectures, its search capabilities are limited to title and full-text search. We suggest that semantic information can be extracted from Wikipedia by analyzing the links between categories. The results can be used for building a semantic schema for Wikipedia which could improve its search capabilities and provide contributors with meaningful suggestions for editing theWikipedia pages.We analyze relevant measures for inferring the semantic relationships between page categories of Wikipedia. Experimental results show that Connectivity Ratio positively correlates with the semantic connection strength. 0 0
Extracting semantic relationships between wikipedia categories CEUR Workshop Proceedings English 2006 The Wikipedia is the largest online collaborative knowledge sharing system, a free encyclopedia. Built upon traditional wiki architectures, its search capabilities are limited to title and full-text search. We suggest that semantic information can be extracted from Wikipedia by analyzing the links between categories. The results can be used for building a semantic schema for Wikipedia which could improve its search capabilities and provide contributors with meaningful suggestions for editing theWikipedia pages.We analyze relevant measures for inferring the semantic relationships between page categories of Wikipedia. Experimental results show that Connectivity Ratio positively correlates with the semantic connection strength. 0 0