Two approaches to the dataset interlinking recommendation problem

Whenever a dataset t is published on the Web of Data, an exploratory search over existing datasets must be performed to identify those datasets that are potential candidates to be interlinked with t. This paper introduces and compares two approaches to address the dataset interlinking recommendation problem, respectively based on Bayesian classifiers and on Social Network Analysis techniques. Both approaches define rank score functions that explore the vocabularies, classes and properties that the datasets use, in addition to the known dataset links. After extensive experiments using real-world datasets, the results show that the rank score functions achieve a mean average precision of around 60%. Intuitively, this means that the exploratory search for datasets to be interlinked with t might be limited to just the top-ranked datasets, reducing the cost of the dataset interlinking process. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11749-2_25.EC/FP7/LinkedUpCNPq/160326/2012-5CNPq/303332/2013-1CNPq/557128/2009-9FAPERJ/E-26/170028/2008FAPERJ/E-26/103.070/2011FAPERJ/E-26/101.382/2014CAPES/141082

The Semantic Web MIDI Tape: An Interface for Interlinking MIDI and Context Metadata

The Linked Data paradigm has been used to publish a large number of musical datasets and ontologies on the Semantic Web, such as MusicBrainz, AcousticBrainz, and the Music Ontology. Recently, the MIDI Linked Data Cloud has been added to these datasets, representing more than 300,000 pieces in MIDI format as Linked Data, opening up the possibility for linking fine-grained symbolic music representations to existing music metadata databases. Despite the dataset making MIDI resources available in Web data standard formats such as RDF and SPARQL, the important issue of finding meaningful links between these MIDI resources and relevant contextual metadata in other datasets remains. A fundamental barrier for the provision and generation of such links is the difficulty that users have at adding new MIDI performance data and metadata to the platform. In this paper, we propose the Semantic Web MIDI Tape, a set of tools and associated interface for interacting with the MIDI Linked Data Cloud by enabling users to record, enrich, and retrieve MIDI performance data and related metadata in native Web data standards. The goal of such interactions is to find meaningful links between published MIDI resources and their relevant contextual metadata. We evaluate the Semantic Web MIDI Tape in various use cases involving user-contributed content, MIDI similarity querying, and entity recognition methods, and discuss their potential for finding links between MIDI resources and metadata

OntoMathPROOntoMath^{PRO} Ontology: A Linked Data Hub for Mathematics

In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.Comment: 15 pages, 6 images, 1 table, Knowledge Engineering and the Semantic Web - 5th International Conferenc

MeLinDa: an interlinking framework for the web of data

The web of data consists of data published on the web in such a way that they can be interpreted and connected together. It is thus critical to establish links between these data, both for the web of data and for the semantic web that it contributes to feed. We consider here the various techniques developed for that purpose and analyze their commonalities and differences. We propose a general framework and show how the diverse techniques fit in the framework. From this framework we consider the relation between data interlinking and ontology matching. Although, they can be considered similar at a certain level (they both relate formal entities), they serve different purposes, but would find a mutual benefit at collaborating. We thus present a scheme under which it is possible for data linking tools to take advantage of ontology alignments.Comment: N° RR-7691 (2011

Identifying candidate datasets for data interlinking

One of the design principles that can stimulate the growth and increase the usefulness of the Web of data is URIs linkage. However, the related URIs are typically in different datasets managed by different publishers. Hence, the designer of a new dataset must be aware of the existing datasets and inspect their content to define sameAs links. This paper proposes a technique based on probabilistic classifiers that, given a datasets S to be published and a set T of known published datasets, ranks each Ti ∈ T according to the probability that links between S and Ti can be found by inspecting the most relevant datasets. Results from our technique show that the search space can be reduced up to 85%, thereby greatly decreasing the computational effort. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39200-9_29

Retrieval, crawling and fusion of entity-centric data on the web

While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entitycentric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs. The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-3-319-53640-8_1

From RESTful Services to RDF: Connecting the Web and the Semantic Web

RESTful services on the Web expose information through retrievable resource representations that represent self-describing descriptions of resources, and through the way how these resources are interlinked through the hyperlinks that can be found in those representations. This basic design of RESTful services means that for extracting the most useful information from a service, it is necessary to understand a service's representations, which means both the semantics in terms of describing a resource, and also its semantics in terms of describing its linkage with other resources. Based on the Resource Linking Language (ReLL), this paper describes a framework for how RESTful services can be described, and how these descriptions can then be used to harvest information from these services. Building on this framework, a layered model of RESTful service semantics allows to represent a service's information in RDF/OWL. Because REST is based on the linkage between resources, the same model can be used for aggregating and interlinking multiple services for extracting RDF data from sets of RESTful services

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

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

A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.LinkedU