Document (#39134)

Author

Mandalka, M.

Title

Open semantic search zum unabhängigen und datenschutzfreundlichen Erschliessen von Dokumenten

Issue

[07.07.2015].

Source

http://www.linux-community.de/Internal/Nachrichten/Open-Semantic-Search-zum-unabhaengigen-und-datenschutzfreundlichen-Erschliessen-von-Dokumenten

Year

2015

Abstract

Ob grösserer Leak oder Zusammenwürfeln oder (wieder) Erschliessen umfangreicherer (kollaborativer) Recherche(n) oder Archive: Immer öfter müssen im Journalismus größere Datenberge und Dokumentenberge erschlossen werden. In eine Suchmaschine integrierte Analyse-Tools helfen (halb)automatisch.

Content

"Open Semantic Desktop Search Zur Tagung des Netzwerk Recherche ist die Desktop Suchmaschine Open Semantic Desktop Search zum unabhängigen und datenschutzfreundlichen Erschliessen und Analysieren von Dokumentenbergen nun erstmals auch als deutschsprachige Version verfügbar. Dank mächtiger Open Source Basis kann die auf Debian GNU/Linux und Apache Solr basierende freie Software als unter Linux, Windows oder Mac lauffähige virtuelle Maschine kostenlos heruntergeladen, genutzt, weitergegeben und weiterentwickelt werden. Dokumentenberge erschliessen Ob grösserer Leak oder Zusammenwürfeln oder (wieder) Erschliessen umfangreicherer (kollaborativer) Recherche(n) oder Archive: Hin und wieder müssen größere Datenberge bzw. Dokumentenberge erschlossen werden, die so viele Dokumente enthalten, dass Mensch diese Masse an Dokumenten nicht mehr alle nacheinander durchschauen und einordnen kann. Auch bei kontinuierlicher Recherche zu Fachthemen sammeln sich mit der Zeit größere Mengen digitalisierter oder digitaler Dokumente zu grösseren Datenbergen an, die immer weiter wachsen und deren Informationen mit einer Suchmaschine für das Archiv leichter auffindbar bleiben. Moderne Tools zur Datenanalyse in Verbindung mit Enterprise Search Suchlösungen und darauf aufbauender Recherche-Tools helfen (halb)automatisch.
Unabhängiges Durchsuchen und Analysieren grosser Datenmengen Damit können investigativ arbeitende Journalisten selbstständig und auf eigener Hardware datenschutzfreundlich hunderte, tausende, hunderttausende oder gar Millionen von Dokumenten oder hunderte Megabyte, Gigabytes oder gar einige Terabytes an Daten mit Volltextsuche durchsuchbar machen. Automatische Datenanreicherung und Erschliessung mittels Hintergrundwissen Zudem wird anhand von konfigurierbaren Hintergrundwissen automatisch eine interaktive Navigation zu in Dokumenten enthaltenen Namen von Bundestagsabgeordneten oder Orten in Deutschland generiert oder anhand Textmustern strukturierte Informationen wie Geldbeträge extrahiert. Mittels Named Entities Manager für Personen, Organisationen, Begriffe und Orte können eigene Rechercheschwerpunkte konfiguriert werden, aus denen dann automatisch eine interaktive Navigation (Facettensuche) und aggregierte Übersichten generiert werden. Automatische Datenvisualisierung Diese lassen sich auch visualisieren: So z.B. die zeitliche Verteilung von Suchergebnissen als Trand Diagramm oder durch gleichzeitige Nennung in Dokumenten abgeleitete Verbindungen als Netzwerk bzw. Graph.
Automatische Texterkennung (OCR) Dokumente, die nicht im Textformat, sondern als Grafiken vorliegen, wie z.B. Scans werden automatisch durch automatische Texterkennung (OCR) angereichert und damit auch der extrahierte Text durchsuchbar. Auch für eingebettete Bilddateien bzw. Scans innerhalb von PDF-Dateien. Unscharfe Suche mit Listen Ansonsten ist auch das Recherche-Tool bzw. die Such-Applikation "Suche mit Listen" integriert, mit denen sich schnell und komfortabel abgleichen lässt, ob es zu den einzelnen Einträgen in Listen jeweils Treffer in der durchsuchbaren Dokumentensammlung gibt. Mittels unscharfer Suche findet das Tool auch Ergebnisse, die in fehlerhaften oder unterschiedlichen Schreibweisen vorliegen. Semantische Suche und Textmining Im Recherche, Textanalyse und Document Mining Tutorial zu den enthaltenen Recherche-Tools und verschiedenen kombinierten Methoden zur Datenanalyse, Anreicherung und Suche wird ausführlicher beschrieben, wie auch eine große heterogene und unstrukturierte Dokumentensammlung bzw. eine grosse Anzahl von Dokumenten in verschiedenen Formaten leicht durchsucht und analysiert werden kann.
Virtuelle Maschine für mehr Plattformunabhängigkeit Die nun auch deutschsprachig verfügbare und mit deutschen Daten wie Ortsnamen oder Bundestagsabgeordneten vorkonfigurierte virtuelle Maschine Open Semantic Desktop Search ermöglicht nun auch auf einzelnen Desktop Computern oder Notebooks mit Windows oder iOS (Mac) die Suche und Analyse von Dokumenten mit der Suchmaschine Open Semantic Search. Als virtuelle Maschine (VM) lässt sich die Suchmaschine Open Semantic Search nicht nur für besonders sensible Dokumente mit dem verschlüsselten Live-System InvestigateIX als abgeschottetes System auf verschlüsselten externen Datenträgern installieren, sondern als virtuelle Maschine für den Desktop auch einfach unter Windows oder auf einem Mac in eine bzgl. weiterer Software und Daten bereits existierende Systemumgebung integrieren, ohne hierzu auf einen (für gemeinsame Recherchen im Team oder für die Redaktion auch möglichen) Suchmaschinen Server angewiesen zu sein. Datenschutz & Unabhängigkeit: Grössere Unabhängigkeit von zentralen IT-Infrastrukturen für unabhängigen investigativen Datenjournalismus Damit ist investigative Recherche weitmöglichst unabhängig möglich: ohne teure, zentrale und von Administratoren abhängige Server, ohne von der Dokumentenanzahl abhängige teure Software-Lizenzen, ohne Internet und ohne spionierende Cloud-Dienste. Datenanalyse und Suche finden auf dem eigenen Computer statt, nicht wie bei vielen anderen Lösungen in der sogenannten Cloud."

Footnote

Vgl. auch: http://www.opensemanticsearch.org/de/.

Theme

Suchmaschinen
Semantisches Umfeld in Indexierung u. Retrieval

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5. Hänger, C.; Krätzsch, C.; Niemann, C.: Was vom Tagging übrig blieb : Erkenntnisse und Einsichten aus zwei Jahren Projektarbeit (2011) 0.11

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