From Unstructured Web Knowledge to Plan Descriptions

Autonomous on-line knowledge discovery and acquisition play an important role in cognitive informatics, cognitive computing, knowledge engineering, and computational intelligence. On the basis of the latest advances in cognitive informatics and denotational mathematics, this paper develops a web knowledge discovery engine for web document restructuring and comprehension, which decodes on-line knowledge represented in informal documents into cognitive knowledge represented by concept algebra and concept networks. A visualized concept network explorer and a semantic analyzer are implemented to capture and refine queries based on concept algebra. A graphical interface is built using concept and semantic models to refine users’ queries. To enable autonomous information restructuring by machines, a two-level knowledge base that mimics human lexical/syntactical and semantic cognition is introduced. The information restructuring model provides a foundation for automatic concept indexing and knowledge extraction from web documents. The web knowledge discovery engine extends machine learning capability from imperative and adaptive information processing to autonomous and cognitive knowledge processing with unstructured documents in natural languages.

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Disambiguation and Filtering Methods in Using Web Knowledge for Coreference Resolution

Cross-Disciplinary Advances in Applied Natural Language Processing ◽

10.4018/978-1-61350-447-5.ch013 ◽

2012 ◽

pp. 185-201 ◽

Cited By ~ 1

Author(s):

Olga Uryupina ◽

Massimo Poesio ◽

Claudio Giuliano ◽

Kateryna Tymoshenko

Keyword(s):

Semantic Information ◽

Knowledge Bases ◽

Coreference Resolution ◽

Number Of Solutions ◽

Web Resources ◽

Percentage Points ◽

Resolution System ◽

Semantic Compatibility ◽

Additional Constraints ◽

Web Knowledge

The authors investigate two publicly available Web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution engine. They extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a coreference resolution system. The authors show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings (Ponzetto & Poesio, 2009). They propose, therefore, a number of solutions to reduce the amount of noise coming from Web resources: using disambiguation tools for Wikipedia, pruning Yago to eliminate the most generic categories and imposing additional constraints on affected mentions. The evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves the system’s performance by 2-3 percentage points.

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