Statistical analysis of the owl:sameAs network for aligning concepts in the linking open data cloud

The massively distributed publication of linked data has brought to the attention of scientific community the limitations of classic methods for achieving data integration and the opportunities of pushing the boundaries of the field by experimenting this collective enterprise that is the linking open data cloud. While reusing existing ontologies is the choice of preference, the exploitation of ontology alignments still is a required step for easing the burden of integrating heterogeneous data sets. Alignments, even between the most used vocabularies, is still poorly supported in systems nowadays whereas links between instances are the most widely used means for bridging the gap between different data sets. We provide in this paper an account of our statistical and qualitative analysis of the network of instance level equivalences in the Linking Open Data Cloud (i.e. the sameAs network) in order to automatically compute alignments at the conceptual level. Moreover, we explore the effect of ontological information when adopting classical Jaccard methods to the ontology alignment task. Automating such task will allow in fact to achieve a clearer conceptual description of the data at the cloud level, while improving the level of integration between datasets. <br/

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Distributed Holistic Clustering on Linked Data

Link discovery is an active field of research to support data integration in the Web of Data. Due to the huge size and number of available data sources, efficient and effective link discovery is a very challenging task. Common pairwise link discovery approaches do not scale to many sources with very large entity sets. We here propose a distributed holistic approach to link many data sources based on a clustering of entities that represent the same real-world object. Our clustering approach provides a compact and fused representation of entities, and can identify errors in existing links as well as many new links. We support a distributed execution of the clustering approach to achieve faster execution times and scalability for large real-world data sets. We provide a novel gold standard for multi-source clustering, and evaluate our methods with respect to effectiveness and efficiency for large data sets from the geographic and music domains

Knowledge Representation of Intelligent Public Services through a Semantic Model

Today citizens make intensive use of mobile communication technology, and they demand to public services providers for complex and sophisticated information. To meet these demands, the governments' services agencies must orchestrate a lot of information from various sources and formats, and deliver them in the data terminals that people commonly use: computers, net-books, tablets and smart-phones. To overcome these problems, we propose a deductible model for conceptual representation of the organizational units of the State and his services, based on ontologies designed under the Linked Open Data principles. This model allows automatic extraction of information through machines, that support governmental decision-making processes and giving to citizens a comprehensive access to find and make formalities through intelligent agent.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Knowledge Representation of Intelligent Public Services through a Semantic Model

Today citizens make intensive use of mobile communication technology, and they demand to public services providers for complex and sophisticated information. To meet these demands, the governments' services agencies must orchestrate a lot of information from various sources and formats, and deliver them in the data terminals that people commonly use: computers, net-books, tablets and smart-phones. To overcome these problems, we propose a deductible model for conceptual representation of the organizational units of the State and his services, based on ontologies designed under the Linked Open Data principles. This model allows automatic extraction of information through machines, that support governmental decision-making processes and giving to citizens a comprehensive access to find and make formalities through intelligent agent.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Descubrimiento automático de mappings en un caso de uso real con altas exigencias de certeza

Los sistemas de integración de información resuelven las diferencias entre las fuentes, en la mayoría de los casos, mediante la creación de mappings, puentes semánticos entre los elementos de las fuentes. Hasta ahora se han propuesto comparadores para generar un conjunto de mappings para cada par de elementos de las fuentes a integrar, y se han realizado estudios experimentales con ellos. El valor añadido del presente trabajo frente a los trabajos experimentales anteriores es que se ha llevado a cabo en un caso real embebido en una aplicación real (en el dominio geográfico) con altas exigencias de certeza

Mapping subjectivity: performing people-centered vocabulary alignment

This paper describes a mapping of linked data vocabularies in the area of person-related information. Aligning vocabulary terms may help curb the problem of property proliferation that occurs in linked data environments. It also facilitates the process of choosing semantics for vocabulary extensions and integration in the context of linked data applications. Although a work in progress, this investigation would provide support for semantic integration and for knowledge sharing and reuse in the area of personal information representation. It also offers an opportunity to reflect on a new generation of knowledge organization systems such as linked data vocabularies that have started to populate the web and are converging with new representation models and discovery tools in libraries and other cultural heritage institutions

Towards valid and reusable reference alignments — ten basic quality checks for ontology alignments and their application to three different reference data sets

Identifying relationships between hitherto unrelated entities in different ontologies is the key task of ontology alignment. An alignment is either manually created by domain experts or automatically by an alignment system. In recent years, several alignment systems have been made available, each using its own set of methods for relation detection. To evaluate and compare these systems, typically a manually created alignment is used, the so-called reference alignment. Based on our experience with several of these reference alignments we derived requirements and translated them into simple quality checks to ensure the alignments’ validity and also their reusability. In this article, these quality checks are applied to a standard reference alignment in the biomedical domain, the Ontology Alignment Evaluation Initiative Anatomy track reference alignment, and two more recent data sets covering multiple domains, including but not restricted to anatomy and biology

The Landscape of Ontology Reuse Approaches

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits