Authors:
Sven Hartrumpf
1
;
Hermann Helbig
2
and
Ingo Phoenix
1
Affiliations:
1
SEMPRIA GmbH, Germany
;
2
University at Hagen, Germany
Keyword(s):
Semantic Analysis, Knowledge Bases, Text Understanding, Natural Language Processing, Reference Resolution.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Large-scale knowledge acquisition from texts is one of the challenges of the information society that can
only be mastered by technical means. While the syntactic analysis of isolated sentences is relatively well
understood, the problem of automatically parsing on all linguistic levels, starting from the morphological level
through to the semantic level, i.e. real understanding of texts, is far from being solved. This paper explains
the approach taken in this direction by the MultiNet technology in bridging the gap between the syntactic semantic
analysis of single sentences and the creation of knowledge bases representing the content of whole
texts. In particular, it is shown how linguistic text phenomena like inclusion or bridging references can be
dealt with by logical means using the axiomatic apparatus of the MultiNet formalism. The NLP techniques
described are practically applied in transforming large textual corpora like Wikipedia into a knowledge base
and using the la
tter in meaning-oriented search engines.
(More)