UniDL on Tuesday, July 20th

09:00‑10:00 Opening
Location: IF 1.15
09:00 Welcoming introduction
09:05 Ralf Möller (University of Hamburg-Harburg)
Invited talk: A Probabilistic Abduction Engine for Media Interpretation based on Ontologies

For multimedia interpretation, and in particular for the combined interpretation of information coming from different modalities, a semantically well-founded formalization is required in the context of an agent-based scenario. Low-level percepts, which are represented symbolically, define the observations of an agent, and interpretations of content are defined as explanations for the observations.

We propose an abduction-based formalism that uses description logics for the ontology and Horn rules for defining the space of hypotheses for explanations (i.e., the space of possible interpretations of media content), and we use Markov logic to define the motivation for the agent to generate explanations on the one hand, and for ranking different explanations on the other. This work has been funded by the European Community with the project CASAM (Contract FP7-217061 CASAM) and by the German Science Foundation with the project PRESINT (DFG MO 801/1-1).

10:30‑12:30 Technical Papers
Location: IF 1.15
10:30 Pavel Klinov and Bijan Parsia
Relationships between Probabilistic Description and First-Order Logics

This paper analyzes the probabilistic description logic P-SHIQ as a fragment of first-order probabilistic logic (FOPL). P-SHIQ was suggested as a language that is capable of representing and reasoning with different kinds of uncertainty in ontologies, namely generic probabilistic relationships between concepts and probabilistic facts about individuals. The logic also provides a non-monotonic mechanism for combining these kinds of probabilities which allows for probabilistic inheritance with overriding. Despite all these attractive features some semantic properties of P-SHIQ have been unclear which raised concerns regarding whether it could be used as a basis for a probabilistic extension of an ontological language, e.g., OWL. In this paper we provide a clear insight into the logic and its semantics by translating it into FOPL which modeling capabilities have been properly analyzed. We show that P-SHIQ can be viewed as sublanguage of FOPL with a specific semantics based on possible worlds. From that reduction, we show that some of the restrictions of P-SHIQ are fundamental and discuss alternative semantic foundations for a probabilistic description logic.

10:55 Paulo Santos, Fabio Cozman, Valquiria Fenelon Pereira and Britta Hummel
Probabilistic Logic Encoding of Spatial Domains

This paper presents a formalisation of a spatial domain in terms of a qualitative spatial reasoning formalism, encoded in a probabilistic description logic. The QSR formalism chosen is a subset of a cardinal direction calculus and the probabilistic description logic used has the relational structures of the well- know ALC language, allied with the inference methods of Bayesian Networks. We consider a scenario that is composed of a road that is navigated by an exper- imental vehicle equipped with three on-board sensors: a digital map, a GPS and a video camera. This paper presents results of using the proposed formalism to answer queries about (for instance) which lane is the vehicle driving on; or, which driving direction each lane permits.

11:20 Oliver Gries and Ralf Möller
Gibbs Sampling in Probabilistic Description Logics with Deterministic Dependencies

In many applications there is interest in representing both probabilistic and deterministic dependencies. This is especially the case in applications using Description Logics (DLs), where ontology engineering usually is based on strict knowledge, while there is also the need to represent uncertainty. We introduce a Markovian style of probabilistic reasoning in first-order logic known as Markov logic and investigate the opportunities for restricting this formalism to DLs. In particular, we show that Gibbs sampling with deterministic dependencies specified in an appropriate fragment remains correct, i.e., probability estimates approximate the correct probabilities. We propose a Gibbs sampling method incorporating deterministic dependencies and conclude that this incorporation can speed up Gibbs sampling significantly.

11:45 Anni-Yasmin Turhan and Rafael Peñaloza
Towards Approximative Most Specific Concepts by Completion for EL with Subjective Probabilities

The most specific concept (msc) w.r.t general EL-TBoxes does not need to exists in general due to cyclic axioms. In this paper we present an algorithm for computing role-depth bounded EL-msc based on the completion algorithm for \el. We extend this computation algorithm to a recently introduced probabilistic variant of EL: Prob-EL^01.

12:10 Rommel Carvalho, Kathryn Laskey and Paulo Costa
Compatibility Formalization Between PR-OWL and OWL

As stated in [5], a major design goal for PR-OWL was to attain compatibility with OWL. However, this goal has been only partially achieved as yet, primarily due to several key issues not fully addressed in the original work. This paper describes these main issues of compatibility between PR-OWL probabilistic ontology language and OWL ontology language and presents possible approaches to deal with these issues. To illustrate them and how they can be addressed, we use procurement fraud as an example application domain [2]. First we describe the lack of mapping between PR-OWL random variables (RVs) and the concepts defined in OWL, and then show how this mapping can be done. Second we describe PR-OWL's lack of compatibility with existing types already present in OWL, and then show how every type defined in PR-OWL can be directly mapped to concepts already present in OWL.

14:00‑15:00 System Descriptions
Location: IF 1.15
14:00 Pavel Klinov and Bijan Parsia
Pronto: A Practical Probabilistic Description Logic Reasoner

This paper presents a system description of Pronto — the first probabilistic Description Logic reasoner capable of processing knowledge bases containing over a thousand probabilistic axioms. We describe the design and architecture of the reasoner with an emphasis on the components that implement algorithms which are crucial for achieving such level of scalability. Finally, we present the results of the experimental evaluation of Pronto’s performance on series of propositional and non-propositional probabilistic knowledge bases.

14:25 Nicola Vitucci, Mario Arrigoni Neri and Giuseppina Gini
Using f-SHIN to Represent Objects: An Aid to Visual Grasping

Description Logics (DLs) are nowadays used to face a variety of problems. When dealing with numerical data coming from the real world, however, the use of traditional logics results in a loss of useful information that can be otherwise exploited using more expressive logics. Fuzzy extensions of traditional DLs, being able to represent vague concepts, are well suited to reason on such objects. In this paper we present an architecture for the automatic building and querying of a fuzzy ontology related to the representation of objects in terms of their composing parts. Our approach mainly aims to face the problem of visual grasping, which is of wide interest in the robotics field.