Wolfgang Nejdl

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Wolfgang Nejdl 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 the wisdom of the crowds for characterizing and connecting heterogeneous resources Classification
Comparison
Domain independent
Fingerprints
Twikime
Wikipedia
HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media English 2014 Heterogeneous content is an inherent problem for cross-system search, recommendation and personalization. In this paper we investigate differences in topic coverage and the impact of topics in different kinds of Web services. We use entity extraction and categorization to create fingerprints that allow for meaningful comparison. As a basis taxonomy, we use the 23 main categories of Wikipedia Category Graph, which has been assembled over the years by the wisdom of the crowds. Following a proof of concept of our approach, we analyze differences in topic coverage and topic impact. The results show many differences between Web services like Twitter, Flickr and Delicious, which reflect users' behavior and the usage of each system. The paper concludes with a user study that demonstrates the benefits of fingerprints over traditional textual methods for recommendations of heterogeneous resources. 0 0
Extracting event-related information from article updates in Wikipedia Lecture Notes in Computer Science English 2013 Wikipedia is widely considered the largest and most up-to-date online encyclopedia, with its content being continuously maintained by a supporting community. In many cases, real-life events like new scientific findings, resignations, deaths, or catastrophes serve as triggers for collaborative editing of articles about affected entities such as persons or countries. In this paper, we conduct an in-depth analysis of event-related updates in Wikipedia by examining different indicators for events including language, meta annotations, and update bursts. We then study how these indicators can be employed for automatically detecting event-related updates. Our experiments on event extraction, clustering, and summarization show promising results towards generating entity-specific news tickers and timelines. 0 0
Temporal summarization of event-related updates in wikipedia Entity timeline
Event Detection
Tem- poral summarization
Wikipedia updates
WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web English 2013 Wikipedia is a free multilingual online encyclopedia cover- ing a wide range of general and specific knowledge. Its con- tent is continuously maintained up-to-date and extended by a supporting community. In many cases, real-world events inuence the collaborative editing of Wikipedia articles of the involved or affected entities. In this paper, we present Wikipedia Event Reporter, a web-based system that sup- ports the entity-centric, temporal analytics of event-related information in Wikipedia by analyzing the whole history of article updates. For a given entity, the system first identifies peaks of update activities for the entity using burst detec- tion and automatically extracts event-related updates using a machine-learning approach. Further, the system deter- mines distinct events through the clustering of updates by exploiting different types of information such as update time, textual similarity, and the position of the updates within an article. Finally, the system generates the meaningful tem- poral summarization of event-related updates and automat- ically annotates the identified events in a timeline. 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
How to trace and revise identities Lecture Notes in Computer Science English 2009 The Entity Name System (ENS) is a service aiming at providing globally unique URIs for all kinds of real-world entities such as persons, locations and products, based on descriptions of such entities. Because entity descriptions available to the ENS for deciding on entity identity-Do two entity descriptions refer to the same real-world entity?-are changing over time, the system has to revise its past decisions: One entity has been given two different URIs or two entities have been attributed the same URI. The question we have to investigate in this context is then: How do we propagate entity decision revisions to the clients which make use of the URIs provided by the ENS? In this paper we propose a solution which relies on labelling the IDs with additional history information. These labels allow clients to locally detect deprecated URIs they are using and also merge IDs referring to the same real-world entity without needing to consult the ENS. Making update requests to the ENS only for the IDs detected as deprecated considerably reduces the number of update requests, at the cost of a decrease in uniqueness quality. We investigate how much the number of update requests decreases using ID history labelling, as well as how this impacts the uniqueness of the IDs on the client. For the experiments we use both artificially generated entity revision histories as well as a real case study based on the revision history of the Dutch and Simple English Wikipedia. 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
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