Semantic Web: – Interoperability,Usability,Applicability

Knowledge representation systems based on the well-founded semantics can offer the degree of scalability required for semantic web applications and make use of expressive semantic features such as Hilog, frame-based reasoning, and defeasibility theories. Such features can be compiled into Prolog tabling engines that have good support for indexing and memory management. However, due both to the power of the semantic features and to the declarative style typical of knowledge representation rules, the resources needed for query evaluation can be unpredictable. In such a situation, users need to understand the overall structure of a computation and examine problematic portions of it. This problem, of profiling a computation, differs from debugging and justification which address why a given answer was or wasn’t derived, and so profiling requires different techniques. In this paper we present a trace-based analysis technique called forest logging which has been used to profile large, heavily tabled computations. In forest logging, critical aspects of a tabled computation are logged; afterwards the log is loaded and analyzed. As implemented in XSB, forest logging slows down execution of practical programs by a constant factor that is often small; and logs containing tens or hundreds of millions of facts can be loaded and analyzed in minutes.

We address the issue of Ontology-Based Data Access, with ontologies represented in the framework of existential rules, also known as Datalog±. A well-known approach involves rewriting the query using ontological knowledge. We focus here on the basic rewriting technique which consists of rewriting the initial query into a union of conjunctive queries. First, we study a generic breadth-first rewriting algorithm, which takes any rewriting operator as a parameter, and define properties of rewriting operators that ensure the correctness of the algorithm. Then, we focus on piece-unifiers, which provide a rewriting operator with the desired properties. Finally, we propose an implementation of this framework and report some experiments.

Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE KBs. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE shows that it can handle probabilistic KBs of realistic size.

This paper presents an ontological approach to the domain of drama. After a description of the drama domain in a cross-cultural and media setting, we introduce the ontology Drammar. Drammar consists of two components, encoding respectively the conceptual model and the SWRL rules. The conceptual model, mainly grounding in AI theories, represents the major concepts of drama, such as agents, actions, plans, units, emotions and values. Then, the paper focuses on the rule component that augments the representation by mapping the intentions of the characters onto the actions actually performed and by appraising the emotion felt by the characters in the drama. To illustrate the functioning of the ontology we introduce a running example from an excerpt of the drama Hamlet. Finally, we carry out an evaluation of the approach on an annotation task that is relevant for drama studies research and teaching. In particular, the emotion appraisal is tested on the main characters of four dramas of different nature, by computing precision and recall results with respect to a human annotator.