This paper presents a novel approach for semantic query extension using a probabilistic description logic. Concepts that are related to a keyword-based query are used for finding other concepts and relations through the use of a relational Bayesian network built using the probabilistic description logic crALC. Furthermore, probabilistic assessments allow us to rank the information returned by search. Examples and issues of importance in real world applications are discussed.

The representation of uncertainty in the semantic web can be eased by the use of learning techniques. To completely induce a probabilistic ontology (that is, an ontology encoded through a probabilistic description logic) from data, two basic tasks must be solved: (1) learning concept definitions and (2) learning probabilistic inclusions. In this paper we propose and test an algorithm that learns concept definitions using an inductive logic programming approach and then learns probabilistic inclusions using relational data.

Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last 20 years. It is well- known that the interpretation domain of classical DLs is a classical set. However, in Science and in the ordinary life the situation is not at all like this. In order to handle these types of knowledge in DLs, in this paper we present a DL framework based on multiset theory. Concretely, we present the DL over multisets ALCmsets which is a semantic extension of the classical DL ALC. The syntax and semantics of ALCmsets are presented. Moreover, we investigate the logical properties of ALCmsets and provide a sound and terminating reasoning algorithm for satisfiability problem of ALCmsets.

The web of linked data represents a globally distributed dataspace which can be queried using the SPARQL query language. However, with the growth in size and complexity of the web of linked data, it becomes impractical for the user to know enough about its structure and semantics for the user queries to produce enough answers. This problem is addressed in the paper by making use of ontologies available on the web of linked data to produce approximate results. The existing approach, which generates multiple relaxed queries and executes them sequentially one by one, is improved by integrating the approximation steps with the query execution itself. Thus, by performing query relaxation on-the-fly at runtime, the shared data between relaxed queries are not fetched repeatedly, resulting in significant performance benefits. Further opportunities for optimization during query execution are identified and are used to prune relaxation steps which do not produce results. The implementation of our approach demonstrates its efficacy.

In this paper, we present a satisfiability preserving transformation of the fuzzy Description Logic ALCFL into the classical Description Logic ALCH. We can use the already existing DL systems to do the reasoning of ALCFL by applying the result of this paper. This work is inspired by Straccia, who has transformed the fuzzy Description Logic fALCH into the classical Description Logic ALCH.

Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. In the setting of fuzzy DLs, restricting to a finite set of degrees of truth has proved to be useful. In this paper, we propose finite fuzzy DLs as a generalization of existing approaches. We assume a finite totally ordered set of linguistic terms or labels, which is very useful in practice since expert knowledge is usually expressed using linguistic terms. Then, we consider any smooth t-norm defined over this set of degrees of truth. In particular, we focus on the finite fuzzy DL ALCH, studying some logical properties, and showing the decidability of the logic by presenting a reasoning preserving reduction to the non-fuzzy case.

Enhancing Semantic Web technologies with an ability to express uncertainty and imprecision is widely discussed topic. While SWRL can provide additional expressivity to OWL-based ontologies, it does not provide any way to handle uncertainty or imprecision. We introduce an extension of SWRL called SWRL-F that is based on SWRL rule language and uses SWRL’s strong semantic foundation as its formal underpinning. We extend it with a SWRL-F ontology to enable fuzzy reasoning in the rule base. The resulting language provides small but powerful set of fuzzy operations that do not introduce inconsistencies in the host ontology.

In this paper, we introduce the tractable pf-EL++ logic, a paraconsistent version of the fuzzy logic f-EL++. Within pf-EL++, it is possible to tolerate contradictions under incomplete and vague knowledge. pf-EL++ extends the f-EL++ language with a paraconsistent negation in order to represent contradictions. This paraconsistent negation is defined under Belnap’s bilattices. It is important to observe that pf -EL++ is a conservative extension of f-EL++, thus assuring that the polynomial-time reasoning algorithm used in f-EL++ can also be used in pf -EL++.

An important line of research within the field of fuzzy DLs is the computation of an equivalent crisp representation of a fuzzy ontology. In this short paper, we discuss the relation between tractable fuzzy DLs and tractable crisp representations. This relation heavily depends on the family of fuzzy operators considered.

Using a variant of Lehmann’s Default Logics and Probabilistic Description Logics we recently presented a framework that invalidates those unwanted inferences that cause concept unsatisfiability without the need to remove explicitly stated axioms. The solutions of this methods were shown to outperform classical ontology repair w.r.t. the number of inferences invalidated. However, conflicts may still exist in the knowledge base and can make reasoning ambiguous. Furthermore, solutions with a minimal number of inferences invalidated do not necessarily minimize the number of conflicts. In this paper we provide an overview over finding solutions that have a minimal number of conflicts while invalidating as few inferences as possible. Specifically, we propose to evaluate solutions w.r.t. the quantity of information they convey by recurring to the notion of entropy and discuss a possible approach towards computing the entropy w.r.t. an ABox.

The past few years have witnessed an increasingly mature body of research on the Semantic Web, with new standards being developed and more complex use cases being proposed and explored. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty inherent to real world SW applications. Not surprisingly, several approaches addressing uncertainty representation and reasoning on the Semantic Web have emerged [3, 4, 6, 7, 10, 11, 13, 14]. For example, PR-OWL [3] provides OWL constructs for representing Multi-Entity Bayesian Network (MEBN) [8] theories. This paper reviews some shortcomings of PR-OWL 1 [2] and describes how they will be addressed in PR-OWL 2. A method is presented for mapping back and forth from triples into random variables (RV). The method applies to triples representing both predicates and functions. A complex example is given for mapping an n-ary relation using the proposed schematic.

Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches.

This is going to be a very interesting debate, in which we would explore the future of uncertainty reasoning in the Semantic Web and the best ways to make our community stronger, more active, and collaborative. Your ideas are enthusiastically welcomed.