knowledge acquisition


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2006
	Brazhnik, Olga	Databases and the geometry of knowledge read more Abstract: Based on a geometrical interpretation of knowledge space, this work defines relationships between data, concepts and models, and establishes a framework for their integration. Concepts encapsulate our knowledge and provide a basis for data acquisition. They change as we learn more. Every discipline operates with a specific set of concepts organized in models. In order to co-process data collected against different concepts, we need to map the underlying concepts. Modal Intentional Actual (MIA) structure, derived from knowledge representation theory, enables the separation of data from hypotheses, and provides a consistent approach for building data models, concept mapping and defining complex relationships, which are represented by morphisms in category theory. Essential data elements from enterprise modeling techniques provide specifications for storing concepts and morphisms in a database. ontology; knowledge acquisition; Knowledge Representation; data models; concept mapping; data integration; Checkout More Details \| Available Online	2006
	Duckham, M.	Qualitative reasoning about consistency in geographic information read more Abstract: Sorry no abstract available for this article knowledge acquisition; error; Checkout More Details	2006
2005
	Boschee, Elizabeth	Automatic Information Extraction read more Abstract: Sorry no abstract available for this article knowledge acquisition; intelligence analysis; Checkout More Details	2005
2004
	OSullivan, David	Too much of the wrong kind of data: Implications for the practice of micro-scale modeling read more Abstract: Sorry no abstract available for this article knowledge acquisition; Checkout More Details	2004
1997
		Using MineSet for knowledge discovery read more Abstract: Sorry no abstract available for this article Data mining; knowledge discovery; Visualization; data visualisation; client-server systems; decision support systems; deductive databases; knowledge acquisition; relational databases; MineSet; computationally intensive tasks; database visualization; decision making; relational data; Checkout More Details	1997
1993
	Chin, David N.	Acquiring user models read more Abstract: Existing machine techniques for acquiring user models are characterized along five orthogonal dimensions: passive/active, user-initiated/automatic, logical/plausible, direct/indirect, and on-line/off-line. Passive techniques observe users whereas active techniques query users. User-initiated techniques require that users volunteer information; automatic techniques do not. The logical/plausible dimension measures the accuracy of derived user model data. Indirect techniques build upon data gathered by more direct methods. On-line techniques acquire user models in real-time during user interaction, while off-line techniques work after the user interaction is finished. Commonalities and differences in capabilities and features of different user model acquisition techniques are analyzed along the above dimensions, and the relationship of these techniques to similar techniques in other areas of artificial intelligence are discussed. knowledge acquisition; user modelling; machine learning; Checkout More Details \| Available Online	1993