Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies

A survey of large-scale reasoning on the Web of data

As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

Parallel querying of distributed ontologies with shared vocabulary

Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web

A Prototype Method and Tool to Facilitate Knowledge Sharing in the New Product Development Process

New Product Development (NPD) plays a critical role in the success of manufacturing firms. Activities in the product development process are dependent on the exchange of knowledge among NPD project team members. Increasingly, many organisations consider effective knowledge sharing to be a source of competitive advantage. However, the sharing of knowledge is often inhibited in various ways. This doctoral research presents an exploratory case study conducted at a multinational physical goods manufacturer. This investigation uncovered three, empirically derived and theoretically informed, barriers to knowledge sharing. They have been articulated as the lack of an explicit definition of information about the knowledge used and generated in the product development process, and the absence of mechanisms to make this information accessible in a multilingual environment and to disseminate it to NPD project team members. Collectively, these barriers inhibit a shared understanding of product development process knowledge. Existing knowledge management methodologies have focused on the capture of knowledge, rather than providing information about the knowledge and have not explicitly addressed issues regarding knowledge sharing in a multilingual environment. This thesis reports a prototype method and tool to facilitate knowledge sharing that addresses all three knowledge sharing barriers. Initially the research set out to identify and classify new product development process knowledge and then sought to determine what information about specific knowledge items is required by project teams. Based on the exploratory case findings, an ontology has been developed that formally defines information about this knowledge and allows it to be captured in a knowledge acquisition tool, thereby creating a knowledge base. A mechanism is provided to permit language labels to be attached to concepts and relations in the ontology, making it accessible to speakers of different languages. A dissemination tool allows the ontology and knowledge base to be viewed via a Web browser client. Essentially, the ontology and mechanisms facilitate a knowledge sharing capability. Some initial validation was conducted to better understand implementation issues and future deployment of the prototype method and tool in practice

Cost estimation in initial development stages of products: an ontological approach

Cost estimation in the early stages of a product are fraught with uncertainties. The conceptual design of product development is characterized by the absence of data, the most critical being costs. The costs impact in the initial phases of the project is low, when discovered in later stages represent great risks. As there are no structured alternatives to obtaining costs in the conceptual phase, the reuse of data from past projects is an alternative discussed in the literature. Knowledge management approaches can search for data, nonexistent in the current phases, in successful earlier projects. The use of ontology is discussed as an approach in generating knowledge stored in a database. The proposed solution seeks to estimate costs based on previous projects. A query is formulated to describe the product function and settings. The ontological model searches the classes, instances, and properties in the database and generates a cost estimation. The costs of the previous project are reused to generate a new agile cost estimate without the need to consult other industry sectors. This dissertation project follows the methodological framework Design Science Research to make partial deliveries up to the final artifact, an ontological model. This proposal has great potential in the industry, considering there are no tools attending the initial phases with the same efficiency.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Estimativas de custos nas fases iniciais de um produto são repletas de incertezas. O projeto conceitual do desenvolvimento de produto e caracterizado pela ausência de dados, sendo os mais críticos os custos. O impacto dos custos nas fases iniciais do projeto e baixo, quando descobertos em fases posteriores representam grandes riscos. Como não existem meios estruturados de obtenção dos custos no projeto na fase conceitual, o reuso de dados de projetos passados e uma alternativa discutida na literatura. Abordagens de gerenciamento de conhecimento podem buscar dados, inexistentes nas fases atuais, em projetos anteriores bem sucedidos. O uso de ontologia e discutido como uma abordagem na geração de conhecimento armazenado em um banco de dados. A solução proposta busca estimar custos baseada em projetos anteriores. E formulada uma pergunta que descreva a função do produto e configurações. O modelo ontológico busca na base de dados classes, instâncias e propriedades e gera uma estimativa de custos. Os custos do projeto anterior são reutilizados para gerar uma nova estimativa de custos ágil sem necessidade de consultar outros setores da indústria. Este projeto de dissertação segue o framework metodológico Design Science Research para fazer entregas parciais ate a entrega do artefato final, um modelo ontológico. Esta proposta possui grande potencial na indústria, considerando que não existem ferramentas que atendam as fases iniciais com a mesma eficiência

Design and Evaluation of Algorithms for Parallel Classification of Ontologies

Description Logics are a family of knowledge representation formalisms with formal semantics. In recent years, DLs have influenced the design and standardization of the Web Ontology Language OWL. The acceptance of OWL as a web standard has promoted the widespread utilization of DL ontologies on the web. One of the most frequently used inference services of description logic reasoners classifies all named classes of OWL ontologies into a subsumption hierarchy. Due to emerging OWL ontologies from the web community consisting of up to hundreds of thousand of named classes and the increasing availability of multi-processor and multi- or many-core computers, the need for parallelizing description logic inference services to achieve a better scalability is expected. The contribution of this thesis has two aspects. On a theoretical level, it first presents algorithms to construct a TBox in parallel, which are independent of a particular DL logic, however they sacrifice completeness. Then, a sound and complete algorithm for TBox classification in parallel is presented. In this algorithm all the subsumption relationships between concepts of a partition assigned to a single thread are found correctly, in other words, correctness of the TBox subsumption hierarchy is guaranteed. Thereafter, we provide an extension of the sound and complete algorithm which is used to handle TBox classification concurrently and more efficiently. This thesis also describes an optimization technique suitable for better partitioning the list of concepts to be inserted into the TBox. On a practical level, a running prototype, Parallel TBox Classifier was implemented for each generation of the classifier based on the above theoretical foundations, respectively. The Parallel TBox Classifier is used to evaluate the practical merit of the proposed algorithms as well as the effectiveness of the designed optimizations against existing state-of-the-art benchmarks. The empirical results illustrate that Parallel TBox Classifier outperforms the Sequential TBox Classifier on real world ontologies with a linear or superlinear speedup factor. Parallel TBox Classifier can form a basis to develop more efficient parallel classification techniques for real world ontologies with different sizes and DL complexities