Towards a systematic benchmarking of ontology-based query rewriting systems

Query rewriting is one of the fundamental steps in ontologybased data access (OBDA) approaches. It takes as inputs an ontology and a query written according to that ontology, and produces as an output a set of queries that should be evaluated to account for the inferences that should be considered for that query and ontology. Different query rewriting systems give support to different ontology languages with varying expressiveness, and the rewritten queries obtained as an output do also vary in expressiveness. This heterogeneity has traditionally made it difficult to compare different approaches, and the area lacks in general commonly agreed benchmarks that could be used not only for such comparisons but also for improving OBDA support. In this paper we compile data, dimensions and measurements that have been used to evaluate some of the most recent systems, we analyse and characterise these assets, and provide a unified set of them that could be used as a starting point towards a more systematic benchmarking process for such systems. Finally, we apply this initial benchmark with some of the most relevant OBDA approaches in the state of the art.

Towards a Systematic benchmarking of ontology-based query rewriting systems

Query rewriting is one of the fundamental steps in ontologybased data access (OBDA) approaches. It takes as inputs an ontology and a query written according to that ontology, and produces as an output a set of queries that should be evaluated to account for the inferences that should be considered for that query and ontology. Different query rewriting systems give support to different ontology languages with varying expressiveness, and the rewritten queries obtained as an output do also vary in expressiveness. This heterogeneity has traditionally made it difficult to compare different approaches, and the area lacks in general commonly agreed benchmarks that could be used not only for such comparisons but also for improving OBDA support. In this paper we compile data, dimensions and measurements that have been used to evaluate some of the most recent systems, we analyse and characterise these assets, and provide a unified set of them that could be used as a starting point towards a more systematic benchmarking process for such systems. Finally, we apply this initial benchmark with some of the most relevant OBDA approaches in the state of the art.

AutoWebS: um Ambiente para Modelagem e Geração Automática de ServiçosWeb Semânticos

Typically Web services contain only syntactic information that describes their interfaces. Due to the lack of semantic descriptions of the Web services, service composition becomes a difficult task. To solve this problem, Web services can exploit the use of ontologies for the semantic definition of service s interface, thus facilitating the automation of discovering, publication, mediation, invocation, and composition of services. However, ontology languages, such as OWL-S, have constructs that are not easy to understand, even for Web developers, and the existing tools that support their use contains many details that make them difficult to manipulate. This paper presents a MDD tool called AutoWebS (Automatic Generation of Semantic Web Services) to develop OWL-S semantic Web services. AutoWebS uses an approach based on UML profiles and model transformations for automatic generation of Web services and their semantic description. AutoWebS offers an environment that provides many features required to model, implement, compile, and deploy semantic Web services

Ontological evaluation of enterprise systems interoperability using ebXML

Enterprise systems interoperability (ESI) is an important topic for business currently. This situation is evidenced, at least in part, by the number and extent of potential candidate protocols for such process interoperation, viz., ebXML, BPML, BPEL, and WSCI. Wide-ranging support for each of these candidate standards already exists. However, despite broad acceptance, a sound theoretical evaluation of these approaches has not yet been provided. We use the Bunge-Wand-Weber (BWW) models, in particular, the representation model, to provide the basis for such a theoretical evaluation. We, and other researchers, have shown the usefulness of the representation model for analyzing, evaluating, and engineering techniques in the areas of traditional and structured systems analysis, object-oriented modeling, and process modeling. In this work, we address the question, what are the potential semantic weaknesses of using ebXML alone for process interoperation between enterprise systems? We find that users will lack important implementation information because of representational deficiencies; due to ontological redundancy, the complexity of the specification is unnecessarily increased; and, users of the specification will have to bring in extra-model knowledge to understand constructs in the specification due to instances of ontological excess.

Universal ontology: attentive tracking of objects and substances across languages and over development

Previous research has demonstrated that adults are successful at visually tracking rigidly moving items, but experience great difficulties when tracking substance-like ‘‘pouring’’ items. Using a comparative approach, we investigated whether the presence/absence of the grammatical count–mass distinction influences adults and children’s ability to attentively track objects versus substances. More specifically, we aimed to explore whether the higher success at tracking rigid over substance-like items appears universally or whether speakers of classifier languages (like Japanese, not marking the object–substance distinction) are advantaged at tracking substances as compared to speakers of non-classifier languages (like Swiss German, marking the object–substance distinction). Our results supported the idea that language has no effect on low-level cognitive processes such as the attentive visual processing of objects and substances. We concluded arguing that the tendency to prioritize objects is universal and independent of specific characteristics of the language spoken.

Essentials in Ontology Engineering: Methodologies, Languages, and Tools

In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering. Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in (a) Knowledge Engineering, Artificial Intelligence and Computer Science, (b) applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and (c) the Semantic Web, the Semantic Grid, and the Linked Data initiative. In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies. In addition, we include some words on how all these elements can be used in the Linked Data initiative.

OntoMap: an ontology-based architecture to perform the semantic mapping between an interlingua and software components

This paper is about the use of natural language to communicate with computers. Most researches that have pursued this goal consider only requests expressed in English. A way to facilitate the use of several languages in natural language systems is by using an interlingua. An interlingua is an intermediary representation for natural language information that can be processed by machines. We propose to convert natural language requests into an interlingua [universal networking language (UNL)] and to execute these requests using software components. In order to achieve this goal, we propose OntoMap, an ontology-based architecture to perform the semantic mapping between UNL sentences and software components. OntoMap also performs component search and retrieval based on semantic information formalized in ontologies and rules.

Ontology learning from text: a soft computing paradigm

Text-based information accounts for more than 80% of today’s Web content. They consist of Web pages written in different natural languages. As the semantic Web aims at turning the current Web into a machine-understandable knowledge repository, availability of multilingual ontology thus becomes an issue at the core of a multilingual semantic Web. However, multilingual ontology is too complex and resource intensive to be constructed manually. In this paper, we propose a three-layer model built on top of a soft computing framework to automatically acquire a multilingual ontology from domain specific parallel texts. The objective is to enable semantic smart information access regardless of language over the Semantic Web.

The Art of Aidagara: Ethics, Aesthetics, and the Quest for an Ontology of Social Existence in Watsuji Tetsurō’s Rinrigaku

This paper provides an analysis of the key term aidagara (“betweenness”) in the philosophical ethics of Watsuji Tetsurō (1889-1960), in response to and in light of the recent movement in Japanese Buddhist studies known as “Critical Buddhism.” The Critical Buddhist call for a turn away from “topical” or intuitionist thinking and towards (properly Buddhist) “critical” thinking, while problematic in its bipolarity, raises the important issue of the place of “reason” versus “intuition” in Japanese Buddhist ethics. In this paper, a comparison of Watsuji’s “ontological quest” with that of Martin Heidegger (1889-1976), Watsuji’s primary Western source and foil, is followed by an evaluation of a corresponding search for an “ontology of social existence” undertaken by Tanabe Hajime (1885-1962). Ultimately, the philosophico-religious writings of Watsuji Tetsurō allow for the “return” of aesthesis as a modality of social being that is truly dimensionalized, and thus falls prey neither to the verticality of topicalism nor the limiting objectivity of criticalism.

Cross-lingual ontology matching as a challenge for the Multilingual Semantic Web

Recently, the Semantic Web has experienced significant advancements in standards and techniques, as well as in the amount of semantic information available online. Nevertheless, mechanisms are still needed to automatically reconcile information when it is expressed in different natural languages on the Web of Data, in order to improve the access to semantic information across language barriers. In this context several challenges arise [1], such as: (i) ontology translation/localization, (ii) cross-lingual ontology mappings, (iii) representation of multilingual lexical information, and (iv) cross-lingual access and querying of linked data. In the following we will focus on the second challenge, which is the necessity of establishing, representing and storing cross-lingual links among semantic information on the Web. In fact, in a “truly” multilingual Semantic Web, semantic data with lexical representations in one natural language would be mapped to equivalent or related information in other languages, thus making navigation across multilingual information possible for software agents.

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

Ontology Localization

In the context of the Semantic Web, resources on the net can be enriched by well-defined, machine-understandable metadata describing their associated conceptual meaning. These metadata consisting of natural language descriptions of concepts are the focus of the activity we describe in this chapter, namely, ontology localization. In the framework of the NeOn Methodology, ontology localization is defined as the activity of adapting an ontology to a particular language and culture. This adaptation mainly involves the translation of the natural language descriptions of the ontology from a source natural language to a target natural language, with the final objective of obtaining a multilingual ontology, that is, an ontology documented in several natural languages. The purpose of this chapter is to provide detailed and prescriptive methodological guidelines to support the performance of this activity.

The NeOn Methodology for Ontology Engineering

In contrast to other approaches that provide methodological guidance for ontology engineering, the NeOn Methodology does not prescribe a rigid workflow, but instead it suggests a variety of pathways for developing ontologies. The nine scenarios proposed in the methodology cover commonly occurring situations, for example, when available ontologies need to be re-engineered, aligned, modularized, localized to support different languages and cultures, and integrated with ontology design patterns and non-ontological resources, such as folksonomies or thesauri. In addition, the NeOn Methodology framework provides (a) a glossary of processes and activities involved in the development of ontologies, (b) two ontology life cycle models, and (c) a set of methodological guidelines for different processes and activities, which are described (a) functionally, in terms of goals, inputs, outputs, and relevant constraints; (b) procedurally, by means of workflow specifications; and (c) empirically, through a set of illustrative examples.

A Mathematical Apparatus for Ontology Simulation. Specialized Extensions of the Extendable Language of Applied Logic

* This paper was made according to the program of fundamental scientific research of the Presidium of the Russian Academy of Sciences «Mathematical simulation and intellectual systems», the project "Theoretical foundation of the intellectual systems based on ontologies for intellectual support of scientific researches".

Ontology-Based Model of Representation of Knowledge about Language Mappings

The paper presents a short review of some systems for program transformations performed on the basis of the internal intermediate representations of these programs. Many systems try to support several languages of representation of the source texts of programs and solve the task of their translation into the internal representation. This task is still a challenge as it is effort-consuming. To reduce the effort, different systems of translator construction, ready compilers with ready grammars of outside designers are used. Though this approach saves the effort, it has its drawbacks and constraints. The paper presents the general idea of using the mapping approach to solve the task within the framework of program transformations and overcome the disadvantages of the existing systems. The paper demonstrates a fragment of the ontology model of high-level languages mappings onto the single representation and gives the example of how the description of (a fragment) a particular mapping is represented in accordance with the ontology model.