We address the problem of equivalence of count-distinct aggregate queries, prove that the problem is decidable, and can be decided in the third level of Polynomial hierarchy. We introduce the notion of core for conjunctive queries with comparisons as an extension of the classical notion for relational queries, and prove that the existence of isomorphism among cores of queries is a sufficient and necessary condition for equivalence of conjunctive queries with comparisons similar to the classical relational setting. However, it is not a necessary condition for equivalence of count-distinct queries. We introduce a relaxation of this condition based on a new notion, which is a potentially new query equivalent to the initial query, introduced to capture the behavior of count-distinct operator.

We study the foundations of human driven, data-aware business processes, by leveraging on the recently proposed framework of Data-Centric Dynamic Systems (DCDSs). The processes we consider simultaneously capture the control-flow of activities, the manipulation of data maintained in a full-fledged relational database, and the interaction with human users. In particular, users can input new data into the system by relying on different interaction modalities: deterministic input, nondeterministic input, and value selection, considering both finite or countably infinite domains for the input data. With this extended framework, we reassessed the decidability results obtained for the formal verification of DCDSs against rich temporal properties, specified using first-order variants of the mu-calculus.

Verification of dynamic systems that manipulate data, stored in a database or ontology, has lately received increasing attention. A plethora of recent works has shown that verification of systems working over unboundedly many data is decidable even for very rich temporal properties, provided that the system is state-bounded. This condition requires the existence of an overall bound on the amount of data stored in each single state along the system evolution. In general, checking state-boundedness is undecidable. An open question is whether it is possible to isolate significant classes of dynamic systems for which state-boundedness is decidable. In this paper we provide a strong negative answer, by resorting to a novel connection with variants of Petri nets. In particular, we show undecidability for systems whose data component contains unary relations only, and whose action component queries and updates such relations in a very limited way. To contrast this result, we propose interesting relaxations of the sufficient conditions proposed in the concrete setting of Data-Centric Dynamic Systems, building on recent results on chase termination for tuple-generating dependencies.

Description logic Knowledge and Action Bases (KAB) are a mechanism for providing both a semantically rich representation of the information on the domain of interest in terms of a description logic knowledge base and actions to change such information over time, possibly introducing new objects. We resort to a variant of DL-Lite where the unique name assumption is not enforced and where equality between objects may be asserted and inferred. Actions are specified as sets of conditional effects, where conditions are based on epistemic queries over the knowledge base (TBox and ABox), and effects are expressed in terms of new ABoxes. In this setting, we address verification of temporal properties expressed in a variant of first-order mu-calculus with quantification across states. Notably, we show decidability of verification, under a suitable restriction inspired by the notion of weak acyclicity in data exchange.

Artifact-Centric systems have emerged in the last years as a suitable framework to model business-relevant entities, by combining their static and dynamic aspects. In particular, the Guard-Stage-Milestone (GSM) approach has been recently proposed to model artifacts and their lifecycle in a declarative way. In this paper, we enhance GSM with a Semantic Layer, constituted by a full-fledged OWL 2 QL ontology linked to the artifact information models through mapping specifications. The ontology provides a conceptual view of the domain under study, and allows one to understand the evolution of the artifact system at a higher level of abstraction. In this setting, we present a technique to specify temporal properties expressed over the Semantic Layer, and verify them according to the evolution in the underlying GSM model. This technique has been implemented in a tool that exploits state-of-the-art ontology-based data access technologies to manipulate the temporal properties according to the ontology and the mappings, and that relies on the GSMC model checker for verification.

We introduce description logic (DL) Knowledge and Action Bases (KAB), a mechanism that provides both a semantically rich representation of the information on the domain of interest in terms of a DL KB and a set of actions to change such information over time, possibly introducing new objects. We resort to a variant of DL-Lite where UNA is not enforced and where equality between objects may be asserted and inferred. Actions are specified as sets of conditional effects, where conditions are based on epistemic queries over the KB (TBox and ABox), and effects are expressed in terms of new ABoxes. We address the verification of temporal properties expressed in a variant of first-order μ-calculus where a controlled form of quantification across states is allowed. Notably, we show decidability of verification, under a suitable restriction inspired by the notion of weak acyclicity in data exchange.

Data-centric dynamic systems are systems where both the process controlling the dynamics and the manipulation of data are equally central. In this paper we study verification of (first-order) mu-calculus variants over relational data-centric dynamic systems, where data are represented by a full-fledged relational database, and the process is described in terms of atomic actions that evolve the database. The execution of such actions may involve calls to external services, providing fresh data inserted into the system. As a result such systems are typically infinite-state. We show that verification is undecidable in general, and we isolate notable cases, where decidability is achieved. Specifically we start by considering service calls that return values deterministically (depending only on passed parameters). We show that in a mu-calculus variant that preserves knowledge of objects appeared along a run we get decidability under the assumption that the fresh data introduced along a run are bounded, though they might not be bounded in the overall system. In fact we tie such a result to a notion related to weak acyclicity studied in data exchange. Then, we move to nondeterministic services where the assumption of data bounded run would result in a bound on the service calls that can be invoked during the execution and hence would be too restrictive. So we investigate decidability under the assumption that knowledge of objects is preserved only if they are continuously present. We show that if infinitely many values occur in a run but do not accumulate in the same state, then we get again decidability. We give syntactic conditions to avoid this accumulation through the novel notion of "generate-recall acyclicity", which takes into consideration that every service call activation generates new values that cannot be accumulated indefinitely.

We introduce semantic artifacts, which are a mechanism that provides both a semantically rich representation of the information on the domain of interest in terms of an ontology, including the underlying data, and a set of actions to change such information over time. In this paper, the ontology is specified as a DL-Lite TBox together with an ABox that may contain both (known) constants and unknown individuals (labeled nulls, represented as Skolem terms). Actions are specified as sets of conditional effects, where conditions are based on conjunctive queries over the ontology (TBox and ABox), and effects are expressed in terms of new ABoxes. In this setting, which is obviously not finite state, we address the verification of temporal/dynamic properties expressed in μ-calculus. Notably, we show decidability of verification, under a suitable restriction inspired by the notion of acyclicity in data exchange.

Artifacts are entities characterized by data of interest (constituting the state of the artifact) in a given business application, and a lifecycle, which constrains the artifact’s possible evolutions. In this paper we study relational artifacts, where data are represented by a full fledged relational database, and the lifecycle is described by a temporal/dynamic formula expressed in μ-calculus. We then consider business processes, modeled as a set of condition/action rules, in which the execution of actions (aka tasks, or atomic services) results in new artifact states. We study conformance of such processes wrt the artifact lifecycle as well as verification of temporal/dynamic properties expressed in μ-calculus. Notice that such systems are infinite-state in general, hence undecidable. However, inspired by recent literature on database dependencies developed for data exchange, we present a natural restriction that makes such systems finite-state, and the above problems decidable.

Abstract: Ontology matching is a process for selection of a good alignment across entities of two (or more) ontologies. This can be viewed as a two‐phase process of (1) applying a similarity measure to find the correspondence of each pair of entities from two ontologies, and (2) extraction of an optimal or near optimal mapping. This paper is focused on the second phase and introduces our evolutionary approach for that. To be able to do so, we need a mechanism to score different possible mappings. Our solution is a weighting mechanism named coincidence‐based weighting. A genetic algorithm is then introduced to create better mappings in successive iterations. We will explain how we code a mapping as well as our crossover and mutation functions. Evaluation of the algorithm is shown and discussed.

Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By interpreting ontologies as Typed Graphs embedded in a Metric Space, coincidence of the structures of the two ontologies is formulated. Having such a formulation, we define a mechanism to score mappings. This scoring can then be used to extract a good alignment among a number of candidates. To do this, this paper introduces three approaches: The first one, straightforward and capable of finding the optimum alignment, investigates all possible alignments, but its runtime complexity limits its use to small ontologies only. To overcome this shortcoming, we introduce a second solution as well which employs a Genetic Algorithm (GA) and shows a good effectiveness for some certain test collections. Based on approximative approaches, a third solution is also provided which, for the same purpose, measures random walks in each ontology versus the other.

Ontology Alignment is a process for finding related entities of different ontologies. This paper discusses the results of our research in this area. One of them is a formulation for a new structural measure which extends famous related works. In this measure with a special attention to the transitive properties, it is tried to increase recall with less harm on precision. Second contribution is a new method for compound measure creation without any need to the mapping extraction phase. Effectiveness of these ideas is discussed and quantitative evaluations are explained in this paper.

Various methods using different measures have been proposed for ontology alignment. Therefore, it is necessary to evaluate the effectiveness of such measures to select better ones for more quality alignment. Current approaches for comparing these measures, are highly dependent on alignment frameworks, which may cause unreal results. In this paper, we propose a framework independent evaluation method, and discuss results of applying it to famous existing string measures.

Search is one of the main motivations behind semantic web. A lot of proposals on semantic web search engines have been appeared in recent years but most of them are restricted to a limited context of web. Here we propose a scalable Multi Ontological Semantic Search Engine to conquer this problem. In addition some components of this architecture have been implemented and their results are reported.

Ontologies are key elements in the Semantic Web for providing formal definitions of concepts and relationships. Such definitions are needed to have data that could be understood and reasoned upon by machines as well as humans. However, because of the possibility of having many Ontologies in the web, alignment – which aims providing mappings across them – is a necessary operation. Many metrics have been defined for ontology alignment. The so-called simple metrics use linguistic or structural features of Ontological concepts to create mappings. Compound metrics, on the other hand, combine some of the simple metrics to have a better results. This paper reports our new method for compound metric creation. It is based on a supervised learning approach in data mining where a training set is used to create a neural network model, performs sensitivity analysis on it to select appropriate metrics among a set of existing ones, and finally constructs a neural network model to combine the result metrics into a compound one. Empirical results of applying it on a set of Ontologies is also shown in this paper.

Several metrics have been proposed for recognition of relationships between elements of two Ontologies. Many of these methods select a number of such metrics and combine them to extract existing mappings. In this article, we present a method for selection of more effective metrics – based on data mining techniques. Furthermore, by having a set of metrics, we suggest a data-mining-like means for combining them into a better ontology alignment.

several methods have been suggested for Ontology Alignment problem, each exploits some measures and uses of their combinations in order to come up with higher quality of alignment. A group of such measures are especially based on structural position of the entities in ontologies, the corresponding measures of which are named Structural Measures. This paper is about to present a new method for computing Structural Similarity, based on the notion of Information Content. The method employs various aspects of the structure of ontologies to recognize related entities. We evaluate the proposed measure using standard methods of Precision and Recall, as well as a new one based on Sensitivity Analysis from Data Mining. Evaluation results show superiority of proposed structural measure over the rest of the methods taking part in the presented tests.

The rapid growth of the World-Wide Web poses unprecedented scaling challenges for general-purpose crawlers and search engines, A focused crawler aims at selectively seek out pages that are relevant to a pre-defined set of topics. Besides specifying topics by some keywords, it is customary also to use some exemplary documents to compute the similarity of a given Web document to the topic, in this paper we introduce a new hybride focused crawler, which uses link structure of documents as well as similarity of pages to the topic to crawl the Web