@conference {16025,
	title = {The Metacognitive Loop: An Architecture for Building Robust Intelligent Systems},
	booktitle = {2010 AAAI Fall Symposium Series},
	year = {2010},
	month = {2010/03/11/},
	abstract = {The Metacognitive Loop: An Architecture for Building Robust Intelligent Systems},
	keywords = {commonsense, ontologies, robust intelligent systems},
	url = {http://www.aaai.org/ocs/index.php/FSS/FSS10/paper/view/2161},
	author = {Shahri,Hamid Haidarian and Dinalankara,Wikum and Fults,Scott and Wilson,Shomir and Perlis, Don and Schmill,Matt and Oates,Tim and Josyula,Darsana and Anderson,Michael}
}
@conference {19633,
	title = {Configuration Reasoning and Ontology For Web},
	booktitle = {IEEE International Conference on Services Computing, 2007. SCC 2007},
	year = {2007},
	month = {2007/07//},
	pages = {387 - 394},
	abstract = {Configuration plays a central role in the deployment and management of Web infrastructures and applications. A configuration often consists of assigning "values" to a pre-defined set of parameters defined in one or more files. Although the task of assigning values to (configuration) parameters looks simple, configuring infrastructures and applications is a very complex process. In this paper we present a framework for defining and analyzing configuration of an Apache server. We define the notion of "configuration space" of an Apache server as a set of possible values that can be assigned to configuration parameters. We then define the notion of an "obstacle" and "forbidden region" in the configuration space that should be avoided. We model configuration space using a logical framework based on OWL (Web ontology language). The obstacles and forbidden regions in the configuration space are modeled as constraints in the logical framework. These obstacles and forbidden regions are essentially "anti-patterns" that a typical installation should avoid. Given an instance of a configuration (that is, a "point" in the configuration space) we then check if the instance is "obstacle free" using logical reasoning.},
	keywords = {Apache server, Application software, configuration management, configuration reasoning, configuration space, File servers, Information security, Internet, knowledge representation languages, logical framework, logical reasoning, ontologies, ontologies (artificial intelligence), Orbital robotics, OWL, path planning, Robot kinematics, Runtime environment, Taxonomy, Web infrastructures management, Web ontology language, Web server},
	author = {Dana Dachman-Soled and Sreedhar, V.C.}
}
@conference {17801,
	title = {TOSS: an extension of TAX with ontologies and similarity queries},
	booktitle = {Proceedings of the 2004 ACM SIGMOD international conference on Management of data},
	series = {SIGMOD {\textquoteright}04},
	year = {2004},
	month = {2004///},
	pages = {719 - 730},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {TAX is perhaps the best known extension of the relational algebra to handle queries to XML databases. One problem with TAX (as with many existing relational DBMSs) is that the semantics of terms in a TAX DB are not taken into account when answering queries. Thus, even though TAX answers queries with 100\% precision, the recall of TAX is relatively low. Our TOSS system improves the recall of TAX via the concept of a similarity enhanced ontology (SEO). Intuitively, an ontology is a set of graphs describing relationships (such as isa, partof, etc.) between terms in a DB. An SEO also evaluates how similarities between terms (e.g. "J. Ullman", "Jeff Ullman", and "Jeffrey Ullman") affect ontologies. Finally, we show how the algebra proposed in TAX can be extended to take SEOs into account. The result is a system that provides a much higher answer quality than TAX does alone (quality is defined as the square root of the product of precision and recall). We experimentally evaluate the TOSS system on the DBLP and SIGMOD bibliographic databases and show that TOSS has acceptable performance.},
	keywords = {ontologies, semantic integration of heterogeneous data, similarity enhancement, XML databases},
	isbn = {1-58113-859-8},
	doi = {10.1145/1007568.1007649},
	url = {http://doi.acm.org/10.1145/1007568.1007649},
	author = {Hung,Edward and Deng,Yu and V.S. Subrahmanian}
}
