Abstract
We present onto2problog, a tool that supports ontology-mediated querying of probabilistic data via probabilistic logic programming engines. Our tool supports conjunctive queries on probabilistic data under ontologies encoded in the description logic mathcal{ELH}^{dr}, thus capturing a large part of the OWL 2 EL profile.
Highlights
The amount of data collected has grown considerably in recent years, but with this so has the uncertainty in this data
Jung and Lutz introduced the framework of ontology-mediated querying of probabilistic data (OMQPD): given a set of independent probabilistic facts, an ontology, and a query, evaluate the query on the facts taking into account the supplementary knowledge from the
A probabilistic logic program (PLP) is a triple (F, p, Π) where F is a set of facts, p ∶ F → [0, 1] assigns a probability to every fact, and Π is a stratified logic program consisting of rules of the form: H ← B1, ... , Bm, ¬Bm+1, ... , ¬Bn where H and all Bi are relational atoms over terms
Summary
The amount of data collected has grown considerably in recent years, but with this so has the uncertainty in this data. Suppose we have two probabilistic facts: 0.9 ↦ DepartmentHead(alice) This expresses the knowledge that Alice is a department head with probability 0.9, and, independently, Alice is a mentor of Charlie with probability 0.4. To the best of our knowledge there are so far only preliminary implementations realizing this framework in practice, such as the one proposed by Schoenfisch and Stuckenschmidt [18] This system is incomplete in the sense that it only works for certain safe combinations of query and ontology, and only for ontologies in DL-Lite [2]. It reduces OMQPD in ELHdr to the task of marginal inference in a probabilistic logic program, which has an extensive literature surrounding it with many practical techniques available. For the technical details of our approach, we refer the reader to our earlier conference paper [20]
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