The most widely accepted defining feature of the semantic web is machine-usable content. By this definition, the semantic web is already manifest in shopping agents that automatically access and use web content to find the lowest air fares or book prices. However, where are the semantics? Most people regard the semantic web as a vision, not a reality -- so shopping agents should not "count." To use web content, machines need to know what to do when they encounter it, which, in turn, requires the machine to know what the content means (that is, its semantics). The challenge of developing the semantic web is how to put this knowledge into the machine. The manner in which it is done is at the heart of the confusion about the semantic web. The goal of this article is to clear up some of this confusion. I explain that shopping agents work in the complete absence of any explicit account of the semantics of web content because the meaning of the web content that the agents are expected to encounter can be determined by the human programmers who hardwire it into the web application software. I therefore regard shopping agents as a degenerate case of the semantic web. I note various shortcomings of this approach. I conclude by presenting some ideas about how the semantic web will likely evolve.
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.
It has been argued that it is much easier to convey logical statements using rules rather than OWL (or description logic (DL)) axioms. Based on recent theoretical developments on transformations between rules and DLs, we have developed ROWLTab, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL 2 DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule. In this paper, we present ROWLTab, together with a user evaluation of its effectiveness compared to entering axioms using the standard Protege interface. Our evaluation shows that modeling with ROWLTab is much quicker than the standard interface, while at the same time, also less prone to errors for hard modeling tasks.