Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. Unlike data models, the fundamental asset of ontologies is their relative independence of particular applications, i.e. an ontology consists of relatively generic knowledge that can be reused by different kinds of applications/tasks. The first part of this paper concerns some aspects that help to understand the differences and similarities between ontologies and data models. In the second part we present an ontology engineering framework that supports and favours the genericity of an ontology. We introduce the DOGMA ontology engineering approach that separates "atomic" conceptual relations from "predicative" domain rules. A DOGMA ontology consists of an ontology base that holds sets of intuitive context-specific conceptual relations and a layer of "relatively generic" ontological commitments that hold the domain rules. This constitutes what we shall call the double articulation of a DOGMA ontology 1 .

Abstract. This paper tackles two main disparities between conceptual data schemes and ontologies, which should be taken into account when (re)using conceptual data modeling techniques for building ontologies. Firstly, conceptual schemes are intended to be used during design phases and not at the runtime of applications, while ontologies are typically used and accessed at runtime. To handle this first difference, we define a conceptual markup language (ORM-ML) that allows to represent ORM conceptual diagrams in an open, textual syntax, so that ORM schemes can be shared, exchanged, and processed at the run-time of autonomous applications. Secondly, unlike ontologies that are supposed to hold application-independent domain knowledge, conceptual schemes were developed only for the use of an enterprise application(s), i.e. "in-house" usage. Hence, we present an ontology engineering-framework that enables reusing conceptual modeling approaches in modeling and representing ontologies. In this approach we prevent application-specific knowledge to enter or to be mixed with domain knowledge. To end, we present DogmaModeler: an ontology-engineering tool that implements the ideas presented in the paper.

This paper presents preliminary results in building an annotated corpus of the Palestinian Arabic dialect. The corpus consists of about 43K words, stemming from diverse resources. The paper discusses some linguistic facts about the Palestinian dialect, compared with the Modern Standard Arabic, especially in terms of morphological, orthographic, and lexical variations, and suggests some directions to resolve the challenges these differences pose to the annotation goal. Furthermore, we present two pilot studies that investigate whether existing tools for processing Modern Standard Arabic and Egyptian Arabic can be used to speed up the annotation process of our Palestinian Arabic corpus.

Abstract. This paper presents a specifically database-inspired approach (called DOGMA) for engineering formal ontologies, implemented as shared resources used to express agreed formal semantics for a real world domain. We address several related key issues, such as knowledge reusability and shareability, scalability of the ontology engineering process and methodology, efficient and effective ontology storage and management, and coexistence of heterogeneous rule systems that surround an ontology mediating between it and application agents. Ontologies should represent a domain's semantics independently from "language", while any process that creates elements of such an ontology must be entirely rooted in some (natural) language, and any use of it will necessarily be through a (in general an agent's computer) language. To achieve the claims stated, we explicitly decompose ontological resources into ontology bases in the form of simple binary facts called lexons and into socalled ontological commitments in the form of description rules and constraints. Ontology bases in a logic sense, become "representationless" mathematical objects which constitute the range of a classical interpretation mapping from a first order language, assumed to lexically represent the commitment or binding of an application or task to such an ontology base. Implementations of ontologies become database-like on-line resources in the model-theoretic sense. The resulting architecture allows to materialize the (crucial) notion of commitment as a separate layer of (software agent) services, mediating between the ontology base and those application instances that commit to the ontology. We claim it also leads to methodological approaches that naturally extend key aspects of database modeling theory and practice. We discuss examples of the prototype DOGMA implementation of the ontology base server and commitment server.

Abstract. This chapter presents a methodological framework for ontology engineering (called DOGMA), which is aimed to guide ontology builders towards building ontologies that are both highly reusable and usable, easier to build and to maintain. We survey the main foundational challenges in ontology engineering and analyse to what extent one can build an ontology independently of application requirements at hand. We discuss ontology reusability verses ontology usability and present the DOGMA approach, its philosophy and formalization, which prescribe that an ontology be built as separate domain axiomatization and application axiomatizations. While a domain axiomatization focuses on the characterization of the intended meaning (i.e. intended models) of a vocabulary at the domain level, application axiomatizations focus on the usability of this vocabulary according to certain application/usability perspectives and specify the legal models (a subset of the intended models) of the application(s)' interest. We show how specification languages (such as ORM, UML, EER, and OWL) can be effectively (re)used in ontology engineering.

In this article we present Curras, the first morphologically annotated corpus of the Palestinian Arabic dialect. Palestinian Arabic is one of the many primarily spoken dialects of the Arabic language. Arabic dialects are generally under-resourced compared to Modern Standard Arabic, the primarily written and official form of Arabic. We start in the article with a background description that situates Palestinian Arabic linguistically and historically and compares it to Modern Standard Arabic and Egyptian Arabic in terms of phonological, morphological, orthographic, and lexical variations. We then describe the methodology we developed to collect Palestinian Arabic text to guarantee a variety of representative domains and genres. We also discuss the annotation process we used, which extended previous efforts for annotation guideline development, and utilized existing automatic annotation solutions for Standard Arabic and Egyptian Arabic. The annotation guidelines and annotation meta-data are described in detail. The Curras Palestinian Arabic corpus consists of more than 56 K tokens, which are annotated with rich morphological and lexical features. The inter-annotator agreement results indicate a high degree of consistency.

Big Data technology has discarded traditional data modelling approaches as no longer applicable to distributed data processing. It is, however, largely recognised that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g. data analytics and reporting. In this paper, the third of its kind co-authored by mem-

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