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Ontology learning and population from text - algorithms, evaluation and applications
- P. Cimiano
- Computer Science
- 12 October 2006
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications discusses ontologies for the semantic web, aswell as knowledge management, information retrieval, text clustering and classification, as well as natural language processing.
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis
A novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus based on Formal Concept Analysis, which model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser.
A Framework for Ontology Learning and Data-driven Change Discovery
Text2Onto remains independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism, and allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.
Template-based question answering over RDF data
- Christina Unger, Lorenz Bühmann, Jens Lehmann, A. N. Ngomo, D. Gerber, P. Cimiano
- Computer ScienceWWW
- 16 April 2012
A novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of theQuestion answering system, which is then instantiated using statistical entity identification and predicate detection.
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data
- Thanh Tran, Haofen Wang, S. Rudolph, P. Cimiano
- Computer ScienceIEEE 25th International Conference on Data…
- 29 March 2009
A novel keyword search paradigm for graph-structured data, focusing in particular on the RDF data model, which first compute queries from the keywords, allowing the user to choose the appropriate query, and finally, process the query using the underlying database engine.
Towards the self-annotating web
PANKOW (Pattern-based Annotation through Knowledge on theWeb), a method which employs an unsupervised, pattern-based approach to categorize instances with regard to an ontology, is proposed.
Semantic annotation for knowledge management: Requirements and a survey of the state of the art
This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.
Linking Lexical Resources and Ontologies on the Semantic Web with Lemon
It is shown that the adoption of Semantic Web standards can provide added value for lexicon models by supporting a rich axiomatization of linguistic categories that can be used to constrain the usage of the model and to perform consistency checks.
Learning by googling
A novel methodology that acquires collective knowledge from the World Wide Web using the GoogleTM API is presented, PANKOW, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one withThe aim of learning sub-/superconcept relations.
Ontology Learning from Text: Methods, Evaluation and Applications
This volume presents current research in ontology learning, addressing three perspectives, including methodologies that have been proposed to automatically extract information from texts and to give a structured organization to such knowledge, including approaches based on machine learning techniques.