Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach
This work addresses the need for efficient probabilistic reasoning in domains with incomplete or uncertain information, such as the Semantic Web, though it is incremental as it builds on existing systems like TRILLP.
The paper tackles the problem of managing uncertainty in the Semantic Web by improving probabilistic reasoning with description logics, resulting in TORNADO, which speeds up inference by directly building Binary Decision Diagrams during tableau construction and outperforms state-of-the-art reasoners.
When modeling real world domains we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using the tableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLP are systems offering a Prolog implementation of the tableau algorithm. TRILLP builds a pinpointing formula, that compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-of-the-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application at http://trill.ml.unife.it/.