Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20 of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources. We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the salience and relative authority of entities, and the novelty of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93 in the article-entity suggestion stage and upto 84 for the article-section placement. Finally, we compare our approach against competitive baselines and show significant improvements.
In this task, given specific information extraction templates, a given corpus is analyzed in order to find worthwhile mentions of an entity or snippets that match the templates. Balog @cite_5 @cite_0 recommend news citations for an entity. Prior to that, the news articles are classified for their appropriateness for an entity, where as features for the classification task they use entity, document, entity-document and temporal features. The best performing features are those that measure similarity between an entity and the news document. @cite_10 consider the problem of knowledge base completion, through question answering and complete missing facts in Freebase based on templates, i.e. bornIn .
@cite_0 @cite_5 @cite_10
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The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resources. Further, our approach achieves performance of up to 95 on entities mentioned from newswire and 80 on a public test set that was designed to include challenging queries. Knowledge bases have become indispensable sources of information. It is therefore critical that they rely on the latest information available and get updated every time new facts surface. Knowledge base acceleration (KBA) systems seek to help humans expand knowledge bases like Wikipedia by automatically recommending edits based on incoming content streams. A core step in this process is that of identifying relevant content, i.e., filtering documents that would imply modifications to the attributes or relations of a given target entity. We propose two multi-step classification approaches for this task that consist of two and three binary classification steps, respectively. Both methods share the same initial component, which is concerned with the identification of entity mentions in documents, while subsequent steps involve identification of documents being relevant and or central to a given entity. Using the evaluation platform of the TREC 2012 KBA track and a rich feature set developed for this particular task, we show that both approaches deliver state-of-the-art performance. Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases. In this paper, we introduce a complete information extraction framework that harvests temporal facts and events from semi-structured data and free text of Wikipedia articles to create a temporal ontology. First, we extend a temporal data representation model by making it aware of events. Second, we develop an information extraction method which harvests temporal facts and events from Wikipedia infoboxes, categories, lists, and article titles in order to build a temporal knowledge base. Third, we show how the system can use its extracted knowledge for further growing the knowledge base. We demonstrate the effectiveness of our proposed methods through several experiments. We extracted more than one million temporal facts with precision over 90 for extraction from semi-structured data and almost 70 for extraction from text. Capturing knowledge from free-form evaluative texts about an entity is a challenging task. New techniques of feature extraction, polarity determination and strength evaluation have been proposed. Feature extraction is particularly important to the task as it provides the underpinnings of the extracted knowledge. The work in this paper introduces an improved method for feature extraction that draws on an existing unsupervised method. By including user-specific prior knowledge of the evaluated entity, we turn the task of feature extraction into one of term similarity by mapping crude (learned) features into a user-defined taxonomy of the entity's features. Results show promise both in terms of the accuracy of the mapping as well as the reduction in the semantic redundancy of crude features.