Combining a co-occurrence-based and a semantic measure for entity linking

One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38288-8_37

Knowledge-based Relational Search in Cultural Heritage Linked Data

Peer reviewe

Searching for a methodology to define culturally relevant relationships between digital collections in archives, libraries and museums

The emergence of Semantic Web and LOD - Linked Open Data - technologies enable that digital objects representing the holdings of archives, libraries and museums collections be semantically interlinked throughout the Web. What are the different types of cultural relevant relationships that may exists between digital objects of collections in archives, libraries and museums throughout the Web? How discover, organize and formalize these relationships to be used by curators in LOD applications? A methodology to analyze the holdings of archives, libraries and museums is proposed based on onomasiologic perspective. Such methodology is applied to a hypothetical competency question that might be proposed by a digital curator. Results indicated that conceptual models such as FRBR, CIDOC CRM and EDM may provide a rich repertoire of semantic relationships that may be used in LOD applications to interlink collections in heritage institutions

Searching for a methodology to define culturally relevant relationships between digital collections in archives, libraries and museums

The emergence of Semantic Web and LOD - Linked Open Data - technologies enable that digital objects representing the holdings of archives, libraries and museums collections be semantically interlinked throughout the Web. What are the different types of cultural relevant relationships that may exists between digital objects of collections in archives, libraries and museums throughout the Web? How discover, organize and formalize these relationships to be used by curators in LOD applications? A methodology to analyze the holdings of archives, libraries and museums is proposed based on onomasiologic perspective. Such methodology is applied to a hypothetical competency question that might be proposed by a digital curator. Results indicated that conceptual models such as FRBR, CIDOC CRM and EDM may provide a rich repertoire of semantic relationships that may be used in LOD applications to interlink collections in heritage institutions

SCARLET: SemantiC relAtion discoveRy by harvesting onLinE onTologies

We present a demo of SCARLET, a technique for discovering relations between two concepts by harvesting the Semantic Web, i.e., automatically finding and exploring multiple and heterogeneous online ontologies. While we have primarily used SCARLET's relation discovery functionality to support ontology matching and enrichment tasks, it is also available as a stand alone component that can potentially be integrated in a wide range of applications. This demo will focus on presenting SCARLET's functionality and its different parametric settings that can influence the trade-off between its accuracy and time performance

PowerAqua: Open Question Answering on the Semantic Web

With the rapid growth of semantic information in the Web, the processes of searching and querying these very large amounts of heterogeneous content have become increasingly challenging. This research tackles the problem of supporting users in querying and exploring information across multiple and heterogeneous Semantic Web (SW) sources. A review of literature on ontology-based Question Answering reveals the limitations of existing technology. Our approach is based on providing a natural language Question Answering interface for the SW, PowerAqua. The realization of PowerAqua represents a considerable advance with respect to other systems, which restrict their scope to an ontology-specific or homogeneous fraction of the publicly available SW content. To our knowledge, PowerAqua is the only system that is able to take advantage of the semantic data available on the Web to interpret and answer user queries posed in natural language. In particular, PowerAqua is uniquely able to answer queries by combining and aggregating information, which can be distributed across heterogeneous semantic resources. Here, we provide a complete overview of our work on PowerAqua, including: the research challenges it addresses; its architecture; the techniques we have realised to map queries to semantic data, to integrate partial answers drawn from different semantic resources and to rank alternative answers; and the evaluation studies we have performed, to assess the performance of PowerAqua. We believe our experiences can be extrapolated to a variety of end-user applications that wish to open up to large scale and heterogeneous structured datasets, to be able to exploit effectively what possibly is the greatest wealth of data in the history of Artificial Intelligence

Interlinking educational data to web of data

With the proliferation of educational data on the Web, publishing and interlinking eLearning resources have become an important issue nowadays. Educational resources are exposed under heterogeneous Intellectual Property Rights (IPRs) in different times and formats. Some resources are implicitly related to each other or to the interest, cultural and technical environment of learners. Linking educational resources to useful knowledge on the Web improves resource seeking. This becomes crucial for moving from current isolated eLearning repositories towards an open discovery space, including distributed resources irrespective of their geographic and system boundaries. Linking resources is also useful for enriching educational content, as it provides a richer context and other related information to both educators and learners. On the other hand, the emergence of the so-called "Linked Data" brings new opportunities for interconnecting different kinds of resources on the Web of Data. Using the Linked Data approach, data providers can publish structured data and establish typed links between them from various sources. To this aim, many tools, approaches and frameworks have been built to first expose the data as Linked Data formats and to second discover the similarities between entities in the datasets. The research carried out for this PhD thesis assesses the possibilities of applying the Linked Open Data paradigm to the enrichment of educational resources. Generally speaking, we discuss the interlinking educational objects and eLearning resources on the Web of Data focusing on existing schemas and tools. The main goals of this thesis are thus to cover the following aspects: -- Exposing the educational (meta)data schemas and particularly IEEE LOM as Linked Data -- Evaluating currently available interlinking tools in the Linked Data context -- Analyzing datasets in the Linked Open Data cloud, to discover appropriate datasets for interlinking -- Discussing the benefits of interlinking educational (meta)data in practice