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AI Reading, or Automatic Semantic Decomposition into Knowledge Graphs and Symbolic reasoning through Marker Passing

dc.contributor.authorFähndrich, Johannes
dc.contributor.authorTrollmann, Frank
dc.date.accessioned2021-12-14T10:57:46Z
dc.date.available2021-12-14T10:57:46Z
dc.date.issued2021
dc.description.abstractMarker passing algorithms have been applied to solve problems in artificial intelligence related to the semantic of written words. Such approaches could a also prove to be useful in digital forensics, e.g., to reduce the effort of extracting evidence from confiscated data. We call creating semantic or even pragmatic understanding of text: AI reading. With this, we show that the aggregation of knowledge out of heterogeneous information sources can be a combination of symbolic and connectionist approaches. With that, the extraction of knowledge graphs can be automated. This approach has the benefit, when used correctly, that both the creation and the use of the knowledge graph through Marker Passing stay explainable. In this paper we describe a tool chain of a Marker Passing approach from the point of view of digital forensics and discuss challenges and opportunities arising from the application of such an approach.en
dc.identifier.doi10.18420/informatik2021-076
dc.identifier.isbn978-3-88579-708-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37744
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-314
dc.subjectSemantic Decomposition
dc.subjectDigital Investigation
dc.subjectMarker Passing
dc.titleAI Reading, or Automatic Semantic Decomposition into Knowledge Graphs and Symbolic reasoning through Marker Passingen
gi.citation.endPage902
gi.citation.startPage891
gi.conference.date27. September - 1. Oktober 2021
gi.conference.locationBerlin
gi.conference.sessiontitleWorkshop: International Workshop on Digital Forensics (WDF)

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