approach for querying Wikipedia content that supports a simple, yet expressive query interfaces that allow both keyword and structured queries. A unique feature of our approach is that, besides returning documents that match the queries, it also exploits relationships among documents to return richer

This paper describes a query-based multi-document summarizer that was built to participate in the update summarization task of TAC10. The system relies on a thesaurus extracted from Wikipedia and uses it as its underlying ontology. The concepts which are detected within the documents are used

always being about a particular query (e.g., the Wikipedia page on earthquakes is about the query earthquake) and others being about the query only at a particular point in time (e.g., the New York Times is only about earthquakes following a major seismic activity). The relationship between documents

. More recently, there have been extensions to allow entity-relationship queries, which allow specification of multiple sets of entities as well as relationships between them. In this paper we address the problem of entity ranking (“near”) queries and entity-relationship queries on the Wikipedia corpus

Query expansion of named entities can be employed in order to increase the retrieval effectiveness. A peculiarity of named entities compared to other vocabulary terms is that they are very dynamic in appearance, and synonym relationships between terms change with time. In this paper, we present

Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship

Deriving knowledge from information stored in unstructured doc-uments is a major challenge. The proliferation of knowledge shar-ing communities such as Wikipedia urge for automatic methods to construct a knowledge base consisting of entities and their re-lationships for advanced querying. More

that encompasses Web pages, entities, and ontological concepts and relationships. We exploit this combination of pages and entities for a novel kind of search-result ranking, coined EntityAuthority, in order to improve the quality of keyword queries that return either pages or entities. To this end, we utilize

applications. In addition to labor saving, the ben-efits delivered by these advances are many and include reliable prediction of outcome, minimization of downtime, system-produced documentation, and support for archiving, historical queries, and provenance. The PRISM/PRISM++ system delivers these benefits

Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents. They are often used as queries for retrieving records and also as key information for linking documents from multiple sources