The Semantic Web has the potential to allow software agents to intelligently process and integrate the Web's wealth of information. These agents must plan how to achieve their goals in light of the information available. However, because the Web is so vast and changes so rapidly, the agent cannot make a closed-world assumption. This condition makes it difficult for an agent to know when it has gathered all relevant information or when additional searches may be redundant. We propose to use local closed world (LCW) information to assist these agents. LCW information can be obtained by accessing sources that are described in a Semantic Web language with LCW extensions, or by executing operators that provide exhaustive information. In this paper, we demonstrate how two Semantic Web languages (DAML OIL and SHOE) can be augmented with the ability to state LCW information. We also show that DAML OIL can represent many kinds of LCW information even without additional language features. Finally, we describe how ordered task decomposition can be used with LCW information to efficiently plan in distributed information environments.
To appear in Theory and Practice of Logic Programming (TPLP), 2008. We are researching the interaction between the rule and the ontology layers of the Semantic Web, by comparing two options: 1) using OWL and its rule extension SWRL to develop an integrated ontology/rule language, and 2) layering rules on top of an ontology with RuleML and OWL. Toward this end, we are developing the SWORIER system, which enables efficient automated reasoning on ontologies and rules, by translating all of them into Prolog and adding a set of general rules that properly capture the semantics of OWL. We have also enabled the user to make dynamic changes on the fly, at run time. This work addresses several of the concerns expressed in previous work, such as negation, complementary classes, disjunctive heads, and cardinality, and it discusses alternative approaches for dealing with inconsistencies in the knowledge base. In addition, for efficiency, we implemented techniques called extensionalization, avoiding reanalysis, and code minimization.
There is a growing need for scalable semantic web repositories which support inference and provide efficient queries. There is also a growing interest in representing uncertain knowledge in semantic web datasets and ontologies. In this paper, I present a bit vector schema specifically designed for RDF (Resource Description Framework) datasets. I propose a system for materializing and storing inferred knowledge using this schema. I show experimental results that demonstrate that this solution simplifies inference queries and drastically improves results. I also propose and describe a solution for materializing and persisting uncertain information and probabilities. Thresholds and bit vectors are used to provide efficient query access to this uncertain knowledge. My goal is to provide a semantic web repository that supports knowledge inference, uncertainty reasoning, and Bayesian networks, without sacrificing performance or scalability.
The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints that can be validated against RDF graphs. Interactions of SHACL with other Semantic Web technologies, such as ontologies or reasoners, is a matter of ongoing research. In this paper we study the interaction of a subset of SHACL with inference rules expressed in datalog. On the one hand, SHACL constraints can be used to define a "schema" for graph datasets. On the other hand, inference rules can lead to the discovery of new facts that do not match the original schema. Given a set of SHACL constraints and a set of datalog rules, we present a method to detect which constraints could be violated by the application of the inference rules on some graph instance of the schema, and update the original schema, i.e, the set of SHACL constraints, in order to capture the new facts that can be inferred. We provide theoretical and experimental results of the various components of our approach.
Beyond its usual industrial fields of application, a current body of research explores the use of constraint based configuration to address general AI problems, like for instance automatic composition of semantically enriched web services (SWS). A configuration request is naturally formulated as a fragment of the desired solution, that the configurator will attempt to complete according to constraints. We address here a case where the design of the configuration request may itself be the result of a configuration phase, that helps the user design the request by formulating it on more abstract grounds. Within this framework, the configurator is first used to complete an abstract request formulated in a specific formalism. Then a translation is performed from the goal model to the final model to yield the actual request sent to the second configuration phase. This research builds on previous experience showing the adequacy of using configuration to compose SWS, that raised further issues regarding the nature of queries.