We present DISPONTE, a semantics for probabilistic on- tologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE the axioms of a probabilistic ontology can be annotated with an epistemic or a statistical probability. The epistemic probability represents a degree of condence in the axiom, while the statistical probability considers the populations to which the axiom is applied.

In previous work, we presented an approach for link prediction using a probabilistic description logic, named crALC. Inference in crALC, considering all the social network individuals, was used for suggesting or not a link. Despite the preliminary experiments have shown the potential of the approach, it seems unsuitable for real world scenarios, since in the presence of a social network with many individuals and evidences about them, the inference was unfeasible. Therefore, we extended our approach through the consideration of graph-based features to reduce the space of individuals used in inference. In this paper, we evaluate empirically this modication comparing it with standard proposals. It was possible to verify that this strategy does not decrease the quality of the results and makes the approach scalable.

We describe a package that performs inferences for the probabilistic description logic crALC: given a terminology consisting of a set of sentences in crALC, and a set of assertions, the package computes the probability of additional assertions using an approximate variational method. We brie y review the essentials of crALC, mention some recent applications, and describe the package. We then describe our current efforts to incorporate lifted inference into the package.

Trust is a broad concept which, in many systems, is reduced to reputation estimation. However, reputation is just one way of determining trust. The estimation of trust can be tackled from other perspectives as well, including by looking at provenance. In this work, we look at the combination of reputation and provenance to determine trust values. Concretely, the first contribution of this paper is a standard procedure for computing reputation-based trust assessments. The second is a procedure for computing trust values based on provenance information, represented by means of the W3C standard model PROV. Finally, we demonstrate how merging the results of these two procedures can be beneficial for the reliability of the estimated trust value.

We evaluate our procedures and hypothesis by estimating and verifying the trustworthiness of the tags created within the Waisda? video tagging game, launched by the Netherlands Institute for Sound and Vision. Within Waisda?, tag trustworthiness is estimated on the basis of user consensus. Hence, we first provide a means to represent user consensus in terms of trust values, and then we predict the trustworthiness of tags based on reputation, provenance and a combination of the two. Through a quantitative analysis of the results, we demonstrate that using provenance information is beneficial for the accuracy of trust assessments.

Subjective logic is a powerful probabilistic logic which is useful to handle data in case of uncertainty. Subjective logic and the Semantic Web can mutually benefit from each other, since subjective logic is useful to handle the inner noisiness of the Semantic Web data, while the Semantic Web offers a mean to obtain evidence useful for performing evidential reasoning based on subjective logic. In this paper we propose three extensions and applications of subjective logic in the Semantic Web, namely: the use of semantic similarity measures for weighing subjective opinions, a way for accounting for partial observations, and the new concept of “open world opinion”, i.e. subjective opinions based on Dirichlet Processes, which extend multinomial opinions. For each of these extensions, we provide examples and applications to prove their validity.

The co-existence of heterogeneous but complementary data sources, such as ontologies and databases describing the same domain, is the reality of the Web today. In this paper we argue that this complementarity could be exploited both for discovering the knowledge not captured in the ontology but learnable from the data, and for enhancing the process of ontological reasoning by relying on the combination of formal domain models and evidence coming from data. We build upon our previous work on knowledge discovery from heterogeneous sources of information via association rules mining, and propose a method for automated reasoning on grounded knowledge bases (i.e. knowledge bases linked to data) based on the standard Tableaux algorithm. The proposed approach combines logical reasoning and statistical inference thus making sense of heterogeneous data sources.

Annotation graphs, made available through the Linked Data initiative and Semantic Web, have signicant scientic value. However, their increasing complexity makes it dicult to fully exploit this value. Graph summaries, which group similar entities and relations for a more abstract view on the data, can help alleviate this problem, but new meth- ods for graph summarization are needed that handle uncertainty present within and across these sources. Here, we propose the use of probabilistic soft logic (PSL) [1] as a general framework for reasoning about annota- tion graphs, similarities, and the possibly confounding evidence arising from these. We show preliminary results using two simple graph summa- rization heuristics in PSL for a plant biology domain.

Preference representation and reasoning is a key issue in many real-world scenarios. Currently, there are many approaches allowing preferences to be assessed in a qualitative or quantitative way. The most prominent qualitative approach for representing preferences are CP-nets. Their clear graphical structure unifies an easy representation of user desires with nice computational properties when computing the best outcome. Here, we introduce ontological CP-nets, which allow the representation of preferences using a CP-net over an ontological domain, i.e., variable values are logical formulas constrained relative to a background domain ontology.