http://dl.gi.de/handle/20.500.12116/11562 2026-07-23T03:51:55Z 2026-07-23T03:51:55Z Boden, Christoph Löser, Alexander Nagel, Christoph Pieper, Stephan http://dl.gi.de/handle/20.500.12116/11649 2018-03-29T12:36:13Z 2012-01-01T00:00:00Z

Fact-Aware Document Retrieval for Information Extraction Boden, Christoph; Löser, Alexander; Nagel, Christoph; Pieper, Stephan Exploiting textual information from large document collections such as the Web with structured queries is an often requested, but still unsolved requirement of many users. We present BlueFact, a framework for efficiently retrieving documents containing structured, factual information from a full-text index. This is an essential building block for information extraction systems that enable ad-hoc analytical queries on unstructured text data as well as knowledge harvesting in a digital archive scenario.Our approach is based on the observation that documents share a set of common grammatical structures and words for expressing facts. Our system observes these keyword phrases using structural, syntactic, lexical and semantic features in an iterative, cost effective training process and systematically queries the search engine index with these automatically generated phrases. Next, BlueFact retrieves a list of document identifiers, combines observed keywords as evidence for a factual information and infers the relevance for each document identifier. Finally, we forward the documents in the order of their estimated relevance to an information extraction service. That way BlueFact can efficiently retrieve all the structured, factual information contained in an indexed collection of text documents.We report results of a comprehensive experimental evaluation over 20 different fact types on the Reuters News Corpus Volume I (RCV1). BlueFact’s scoring model and feature generation methods significantly outperform existing approaches in terms of fact retrieval performance. BlueFact fires significantly fewer queries against the index, requires significantly less execution time and achieves very high fact recall across different domains.

2012-01-01T00:00:00Z Braunschweig, Katrin Eberius, Julian Thiele, Maik Lehner, Wolfgang http://dl.gi.de/handle/20.500.12116/11651 2018-03-29T12:36:13Z 2012-01-01T00:00:00Z

OPEN—Enabling Non-expert Users to Extract, Integrate, and Analyze Open Data Braunschweig, Katrin; Eberius, Julian; Thiele, Maik; Lehner, Wolfgang Government initiatives for more transparency and participation have lead to an increasing amount of structured data on the web in recent years. Many of these datasets have great potential. For example, a situational analysis and meaningful visualization of the data can assist in pointing out social or economic issues and raising people’s awareness. Unfortunately, the ad-hoc analysis of this so-called Open Data can prove very complex and time-consuming, partly due to a lack of efficient system support.On the one hand, search functionality is required to identify relevant datasets. Common document retrieval techniques used in web search, however, are not optimized for Open Data and do not address the semantic ambiguity inherent in it. On the other hand, semantic integration is necessary to perform analysis tasks across multiple datasets. To do so in an ad-hoc fashion, however, requires more flexibility and easier integration than most data integration systems provide. It is apparent that an optimal management system for Open Data must combine aspects from both classic approaches.In this article, we propose OPEN, a novel concept for the management and situational analysis of Open Data within a single system. In our approach, we extend a classic database management system, adding support for the identification and dynamic integration of public datasets. As most web users lack the experience and training required to formulate structured queries in a DBMS, we add support for non-expert users to our system, for example though keyword queries. Furthermore, we address the challenge of indexing Open Data.

2012-01-01T00:00:00Z Maier, Moritz Schulz, Jan Thoben, Klaus-Dieter http://dl.gi.de/handle/20.500.12116/11652 2018-03-29T12:36:13Z 2012-01-01T00:00:00Z

Verfahren zur funktionalen Ähnlichkeitssuche technischer Bauteile in 3D-Datenbanken Maier, Moritz; Schulz, Jan; Thoben, Klaus-Dieter In diesem Artikel wird die funktionale Ähnlichkeitssuche technischer Bauteile behandelt. Ziel ist es, ein Verfahren aufzuzeigen, welches eine Ähnlichkeitssuche innerhalb einer bionischen 3D-Datenbank mit verschiedensten Strukturen ermöglicht, ohne direkt auf die Geometriedaten der einzelnen Strukturen zurückzugreifen. Vielmehr werden mechanische Funktionen der Strukturen als Ähnlichkeitskriterium genutzt. Das Verfahren stützt sich hierbei auf Wirkflächen und Punkte, welche Ein- oder Austrittspunkte von Kräften in einer Struktur darstellen. Mit Hilfe verschiedener Algorithmen wird eine automatische Ähnlichkeitssuche durchgeführt. Das Ergebnis der funktionalen Ähnlichkeitssuche ist eine visuell aufbereitete Landkarte, auf der sich funktional ähnliche Strukturen gruppiert und funktional unähnliche entfernt darstellen.

2012-01-01T00:00:00Z Balke, Wolf-Tilo http://dl.gi.de/handle/20.500.12116/11646 2018-03-29T12:36:13Z 2012-01-01T00:00:00Z

Editorial Balke, Wolf-Tilo

2012-01-01T00:00:00Z