Description Logic (DL) ontologies and non-monotonic rules are two prominent Knowledge Representation (KR) formalisms with complementary features that are essential for various applications. Nonmonotonic Description Logic (DL) programs combine these formalisms thus providing support for rule-based reasoning on top of DL ontologies using a well-defined query interface represented by so-called DL-atoms. Unfortunately, interaction of the rules and the ontology may incur inconsistencies such that a DL-program lacks answer sets (i.e., models), and thus yields no information. This issue is addressed by recently defined repair answer sets, for computing which an effective practical algorithm was proposed for DL-Lite A ontologies that reduces a repair computation to constraint matching based on so-called support sets. However, the algorithm exploits particular features of DL-Lite A and can not be readily applied to repairing DL-programs over other prominent DLs like EL. compared to DL-Lite A , in EL support sets may neither be small nor only few support sets might exist, and completeness of the algorithm may need to be given up when the support information is bounded. We thus provide an approach for computing repairs for DL-programs over EL ontologies based on partial (incomplete) support families. The latter are constructed using datalog query rewriting techniques as well as ontology approximation based on logical difference between EL-terminologies. We show how the maximal size and number of support sets for a given DL-atom can be estimated by analyzing the properties of a support hypergraph, which characterizes a relevant set of TBox axioms needed for query derivation. We present a declarative implementation of the repair approach and experimentally evaluate it on a set of benchmark problems; the promising results witness practical feasibility of our repair approach.

Datalog+/- is a family of ontology languages that combine good computational properties with high expressive power. Datalog+/- languages are provably able to capture the most relevant Semantic Web languages. In this paper we consider the class of weakly-sticky (WS) Datalog+/- programs, which allow for certain useful forms of joins in rule bodies as well as extending the well-known class of weakly-acyclic TGDs. So far, only non-deterministic algorithms were known for answering queries on WS Datalog+/- programs. We present novel deterministic query answering algorithms under WS Datalog+/-. In particular, we propose: (1) a bottom-up grounding algorithm based on a query-driven chase, and (2) a hybrid approach based on transforming a WS program into a so-called sticky one, for which query rewriting techniques are known. We discuss how our algorithms can be optimized and effectively applied for query answering in real-world scenarios.

Precision medicine [1] has become a most promising methodology for clinical medicine, which relies heavily on rich biomedical knowledge and information of individual patients such as genetic content, living habits, environmental factors, etc. [2]. US National Academy of Sciences claims in a 2011 research report that a biomedical knowledge network based on biological data and knowledge is necessary for precision medicine [3]. How to compute relatedness between concepts and discover valuable information and implicit knowledge effectively and efficiently from such hybrid knowledge (both structural and non-structural) networks is a key of paramount importance to the realization of precision medicine, and a huge challenge facing the biomedical research community. It is agreeable that the knowledge network should include all the knowledge sources, information systems and repositories in biomedicine available today and in the future, spanning the whole spectrum of structural and non-structural information and knowledge. One type of important knowledge sources is ontology.

The satisfiability problem for SPARQL 1.0 patterns is undecidable in general, since the relational algebra can be emulated using such patterns. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound- constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is decidable by syntactic checks on bound variables and on the use of literals. Although the problem is shown to be NP-complete, it is experimentally shown that the checks can be implemented efficiently in practice. The paper also points out that satisfiability for the so-called ‘well-designed’ patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.

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This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses. This article adopts the solution of using higher-level knowledge to allow this sort of domain knowledge to be used automatically, selecting relevant input attributes, and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

So, again: this is the principle, below is only a discussion of the nature of the signals or representations!

In both semantic model standards Topic Maps and RDF/OWL and in many other NoSQL approaches to solve efficiently the problem of how to represent relations and relationships one major stumbling block is raised beyond all efforts: the namespace. It is a language problem, the babel we have in our civilized world is transferred into our IT systems. But machines do not have to understand our language, we do. The problem here is that from a semantic point of view, similar diagrams are in need from users that want to express business processes but when we reach the implementation stage software engineers have to marry business requirements with the technical constrains of the database system hence the ER diagram you see. Generally speaking this is known as "The Model", a conceptual view of the user on data. The ER version of the model has several limitations, due to the architecture of RDBMS.

The satisfiability problem for SPARQL patterns is undecidable in general, since the expressive power of SPARQL 1.0 is comparable with that of the relational algebra. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound-constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is efficiently decidable by simple checks on bound variables and on the use of literals. The paper also points out that satisfiability for the so-called `well-designed' patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.