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In this paper we introduce a method for exploiting entities from the emerging Web of Data for enhancing various Information Retrieval (IR) services. The approach is based on named-entity recognition applied in a set of search results, and on a graph of documents and identified entities that is constructed dynamically and analyzed stochastically using a Random Walk method. The result of this analysis is exploited in two different contexts: for automatic query expansion and for re-ranking a set of retrieved results. Evaluation results in the 2015 TREC Clinical Decision Support Track illustrate that query expansion can increase recall by retrieving more relevant hits, while re-ranking can notably improve the ranked list of results by moving relevant but low-ranked hits in higher positions.

SPARQL is a standard query language for retrieving and manipulating RDF data.However, the majority of SPARQL implementations require the data to be available in advance, i.e., to exist in main memory or in a RDF repository (accessible through a SPARQL endpoint). Nonetheless, Linked Data exists in the Web in various forms; even an HTML Web page can contain RDF data through RDFa, or RDF data may be dynamically created by a Web Service. In this paper, we propose and demonstrate an extension of SPARQL 1.1, called SPARQL-LD, that allows to directly and flexibly exploit this wealth of data. SPARQL-LD allows to fetch, query and integrate in the same SPARQL query: i) data coming from online RDF or JSON-LD files, ii) data coming from dereferenceable URIs, iii) data embedded in Web pages as RDFa, iv) data that is dynamically created by Web Services, and v) data coming by querying other endpoints. A distinctive characteristic of this extension is that it enables to fetch and query even data in datasets returned by a portion of the query, i.e. discovered at query-execution time.&nbsp

In this paper we report the main results of a study on interoperability objectives and approaches in digital preservation (DP), conducted within the APARSEN Network of Excellence (NoE)1. The aim of the investigation was to collect interoperability challenges and goals from various initiatives and project partners and to produce a matrix of solutions and guidelines that can guide the stakeholders in DP to the multi-dimensional and complex landscape of digital preservation interoperability. The paper describes the main findings of the research, including 1) an overview of the current projects and initiatives on interoperability in different areas of digital preservation, 2) an analysis of the main interoperability scenarios and challenges encountered by partners and other stakeholders in their daily life activity that served to drive the definition of the main common interoperability objectives for digital preservation, 3) a broad matrix of models, standards and services for interoperability that cover the main areas of digital preservation, which can be used as a working instrument to navigate the complex ecosystem of the current interoperability solutions, and 4) a list of recommendations and guidelines to create the ground for a coordinated and interoperable digital preservation ecosystem.

This paper presents Epimenides, a system that implements a novel interoperability dependency reasoning approach for assisting digital preservation activities. A distinctive feature is that it can model also converters and emulators, and the adopted modelling approach enables the automatic reasoning needed for reducing the human effort required for checking (and monitoring) whether a task on a digital object (digital collection in general) is performable. Finally, the paper describes (in the form of scenarios) concrete preservation activities of a research data archive (DANS) and elaborates on how Epimenides could be used and the benefits that would bring.

Earth science communities use various ontologies/schemas to describe different kinds of datasets and for different reasons, and there is not an integrated (harmonized) schema for describing all concepts of Earth science. The purpose of this deliverable is to summarize and analyze the results of the surveys conducted in WP15, identify the needs and gaps between communities based also on the results of the user requirements (WP12) and propose strategies for having harmonized metadata, semantics and ontologies. The deliverable is organized in three main sections aiming at (a) analyzing the results of the WP15 survey, (b) inspecting how the needs of communities are fulfilled and identifying gaps based on the user requirements and (c) describing general approaches for harmonization/integration of information and how they fit into SCIDIP-ES (and in particular in WP33). The first section is the analysis of the WP15 results. Particular focus is given in the part of the survey concerning metadata, semantics and ontologies. The objective is to perform an analysis of the semantic models that are widely used by different communities, in terms of their usability, adoption, configurability, extensibility across different Earth Science domains. The second section uses the results of the user requirements analysis (WP12) as a pivot in order to identify if the aforementioned ontologies cover the needs of communities. The results of the two previous sections are used for the definition of the strategies to have harmonized metadata, semantics and ontologies, able to satisfy the user needs coping with the different Earth Science domain approaches. For the definition of the strategy, core ontologies as well as extensions and mappings for the Earth Science domain have been investigated. The results of this deliverable (the strategy) will be provided as input in T21.8 (Finding Aids Toolkit Development).

The main digital preservation strategies are based on metadata and in many cases SemanticWeb languages, like RDF/S, are used for expressing them. However RDF/S schemas or ontologies are not static, but evolve. This evolution usually happens independently of the “metadata” (ontological instance descriptions) which are stored in the various Metadata Repositories (MRs) or Knowledge Bases (KBs). Nevertheless, it is a common practice for a MR/KB to periodically update its ontologies to their latest versions by “migrating” the available instance descriptions to the latest ontology versions. Such migrations incur gaps regarding the specificity of the migrated metadata, i.e. inability to distinguish those descriptions that should be reexamined (for possible specialization as consequence of the migration) from those for which no reexamination is justified. Consequently, there is a need for principles, techniques, and tools for managing the uncertainty incurred by such migrations, specifically techniques for (a) identifying automatically the descriptions that are candidate for specialization, (b) computing, ranking and recommending possible specializations, and (c) flexible interactive techniques for updating the available descriptions (and their candidate specializations), after the user (curator of the repository) accepts/rejects such recommendations. This problem is especially important for curated knowledge bases which have increased quality requirements (as in e-Science). In this paper we elaborate on this problem, we propose a general approach, and discuss examples and a prototype application that we have developed assuming the RFD/S framework.

A quite general view of the digital preservation problem and its associated tasks (e.g. intelligibility and task-performability checking, risk detection, identification of missing resources for performing a task) is to approach it from a dependency management point of view. In this paper we extend past rule-based approaches for dependency management for modeling also converters and emulators and we demonstrate how this modeling allows performing the desired reasoning and thus enables offering more advanced digital preservation services. Specifically these services can greatly reduce the human effort required for periodically checking (monitoring) whether a task on a digital object is performable.

The effective preservation of both current and historical scientific data will underpin a multitude of ecological, economic and political decisions that shape the future of our society. The SCIDIP-ES project addresses the long-term preservation of the knowledge encoded in scientific data by providing preservation e- infrastructure services which support the persistent storage, access and management needs. Using exemplars from the Earth Science domain we highlight the key preservation challenges and barriers to be overcome by the SCIDIP-ES infrastructure. SCIDIP-ES augments existing science data e-infrastructures by adding specific services and toolkits which implement core preservation concepts, thus guaranteeing the long-term access and exploitation of data assets across and beyond their designated communities.

A user consultation exercise has been conducted to identify current practices with respect to long-term data preservation within the earth science domain. This survey has been conducted as two separate but related tasks and covered a wide range of topics including semantics, metadata, ontologies, policies and technologies. This report contains the results of the direct user consultations and also includes information from a web-based independent search activity. It will be used as input to work package (WP) 33 which is concerned with the definition of data preservation policies and harmonised semantics, ontologies and metadata as well as the development of a preliminary architecture for a European earth science long-term data preservation infrastructure.

The first part of the report is devoted to the state of art. We analyse the main international projects in the field, as well as the standards, recommendations and guidelines for keeping and preserving digital objects, with a special attention on the management of provenance and authenticity. The state of the art is completed by an extensive reference list and by an appendix where all the major projects in the area, their goals and their results are individually presented. On the whole, the state of the art testifies that significant scientific contributions have been given, and that a good level of theoretical formalization has been achieved in this area, even if a large gap still divides the mostly theoretical results of the scientific community from the actual practices carried on in most repositories. This gap needs to be filled with more concrete guidelines and proposals. Acting in this direction we propose a model of the digital object lifecycle, in order to identify the main events that impact on authenticity and provenance and to investigate in detail, for each of them the evidence that has to be gathered in order to conveniently document the history of the digital object. The crucial problem to be addressed is, of course, interoperability, since along its lifecycle the digital object may go through several changes of custody, and therefore the authenticity evidence needs to be managed and interpreted by systems, both keeping and preservation systems, which may be different from the ones that gathered it. Thus, the authenticity evidence needs to comply with a common standard. Achieving such a standard is a quite an ambitious goal, but some basic guidelines can be developed. The model and the guidelines proposed in this report may be considered as a preliminary step in this direction, and a basis to derive operational guidelines to improve the current (and often very limited) practices in managing authenticity and provenance in keeping and preservation systems. The report also documents other important activities that have been carried as part of APARSEN WP24. Interesting original results are presented about provenance interoperability and reasoning (described in detail at ID2401), which include a discussion of the mapping between different provenance models, and the proposal of a set of relevant reasoning rules for reducing the amount of provenance information that has to be explicitly stored and for making corrections easier. A further discussion is devoted to secure logging mechanisms, a specific aspect of the problem, which has a significant impact on managing authenticity

Ranking is a ubiquitous requirement whenever we confront a large collection of atomic or interrelated artifacts. This paper elaborates on this issue for the case of RDF schemas. Specifically, several metrics for evaluating automatic methods for ranking schema elements are proposed and discussed. Subsequently the creation of a test collection for evaluating such methods is described, upon which several ranking methods (from simple to more sophisticated) for RDF schemas are evaluated. This formal way for evaluating ranking methods, apart from yielding credible and repeatable results, gave us some interesting insights to the problem. Finally, our experiences from exploiting these ranking methods for visualizing RDF schemas, specifically for deriving and visualizing top-k schema subgraphs, are reported.

The Compound Term Composition Algebra (CTCA) is an algebra with four algebraic operators, whose composition can be used to specify the meaningful (valid) compound terms (conjunctions of terms) in a given faceted taxonomy in an e±cient and flexible manner. The "positive" operations allow the derivation of valid compound terms through the declaration of a small set of valid compound terms. The "negative" operations allow the derivation of valid compound terms through the declaration of a small set of invalid compound terms. In this paper, we formally define the model-theoretic semantics of the operations and the closed-world assumptions adopted in each operation. We prove that CTCA is monotonic with respect to both valid and invalid compound terms, meaning that the valid and invalid compound terms of a subexpression are not invalidated by a larger expression. We show that CTCA cannot be directly represented in Description Logics. However, we show how we could design a metasystem on top of Description Logics in order to implement this algebra.

Although symbolic data tables summarize huge sets of data they can still become very large in size. This paper proposes a method for compressing a symbolic data table using the recently emerged Compound Term Composition Algebra. One charisma of CTCA is that the closed world hypotheses of its operations can lead to a remarkably high 'compression ratio'. The compacted form apart from having much lower storage space requirements, it allows designing more e±cient algorithms for symbolic data analysis.

In this study we address the problem of answering queries over information sources storing objects which are indexed by terms ar ranged in a taxonomy. We examine query languages of di®erent expres sivity and sources with di®erent kinds of taxonomies. In the simplest kind, the taxonomy includes just term-to-term subsumption links. This case is used as a basis for further developments, in which we consider taxonomies consisting of term-to-queries links. An algorithm for query evaluation is presented for this kind of taxonomies, and it is shown that the addition of negation to the query language leads to intractability. Finally, query-to-query taxonomies are considered.

A materialized faceted taxonomy is an information source where the objects of interest are indexed according to a faceted taxonomy. This paper shows how from a materialized faceted taxonomy, we can mine an expression of the Compound Term Composition Algebra that specifies exactly those compound terms that have non-empty interpretation. The mined expressions can be used for encoding compactly (and subsequently reusing) the domain knowledge that is stored in existing materialized faceted taxonomies. Furthermore, expression mining is very crucial for reorganizing taxonomy-based sources which were not initially designed according to a clear faceted approach (like the directories of Google and Yahoo!), so as to have a semantically clear, and compact faceted structure. We analyze this problem and we give an analytical description of all algorithms needed for expression mining.