Drug traceability is a critical process involving monitoring and validation of the origin, quality, and safety of pharmaceutical products throughout their supply chain to prevent the distribution of counterfeit, substandard, or expired drugs that could harm patients. Traditional centralized solutions for drug traceability, relying on intermediaries and central authorities, introduce risks of data manipulation, corruption, and single points of failure. This work presents the design and implementation of a novel solution for decentralized drug traceability based on blockchain technology and on a reputation mechanism that operates on top of a trustworthy decentralized knowledge base, thus integrating three core technologies: blockchain, semantic, and reputation methods. Blockchain technologies ensure transparent and secure supply chain processes while providing a trustworthy estimation of the reputation of supply chain participants. Semantic technologies address drug data heterogeneity by ensuring interoperability and creating mappings between various data sources, including verifying the identities of the various users. Additionally, the reputation mechanism promotes transparency and accountability, as stakeholders contribute feedback on drug quality, authenticity, and reliability. This fosters a culture of trust and reliability, offering the drug supply chain an effective tool for continuous improvement and informed decision-making based on aggregated feedback, ultimately enhancing overall quality and safety throughout the distribution network. The design and implementation of the system, along with several evaluations, show the feasibility of the new semantic blockchain system in real-world scenarios and the improvement of the entities with a high reputation score. Our solution is more trustworthy, discouraging fraudulent activities as security is based on the various properties included in the semantic model.

After the fire that destroyed most of the Notre-Dame de Paris cathedral's roof and vaults, scientists gathered in an effort to help the restoration process of the cathedral. Several digital methods and heterogeneous data acquisitions were introduced in the process, including many images and annotations. Part of this data focuses on stone degradation phenomena, a crucial element when evaluating the damages caused by the fire and the state of the cathedral before the restoration started. In this paper, we present the first implementation of a dataset creation pipeline with the aim of training AI models to automatically detect and segment stone alteration patterns in images taken in the context of the restoration of Cultural Heritage buildings. Our resulting dataset will be improved in a near future with more data, while conforming with the ambition to provide our experts and researchers with reliable, structured data.

Drug traceability is currently a very challenging area given the complexity of several issues, including drug quality and counterfeit medications. The counterfeited drugs have a major impact on human life, treatment outcomes and economic burden. To deal with these issues, we propose a semantic blockchain-based system for drug traceability that aims at detecting counterfeit drugs in order to improve the patients' safety and quality of life as well as eliminating manufacturers' potential loss and increasing their revenue. Our proposal is based on blockchain and semantic web technologies to enhance the representation capability of data in the pharmaceutical supply chain.

In the context of drug traceability, counterfeit drugs have a significant influence on customer health and trust towards the manufacturers. Therefore, the awareness of drug safety has led to a considerable need for improved traceability in the supply chain. To do so, we have proposed an approach combining blockchain and semantic web technologies to ensure drug traceability in a secure and trustworthy manner. The main contribution of this work is the construction of the Drug Traceability Ontology, then using this ontology alongside with the blockchain technology to check the drugs authenticity.

After the fire that took place in the cathedral Notre-Dame de Paris, a Scientific research group composed of 9 Working Groups was set up to study the building. With the aim of creating a digital ecosystem for spatio-temporal monitoring of a restoration site, efforts have been put to develop methodologies for data management and enrichment. In line with this approach, this work focuses on the exploration, analysis and enrichment of a corpus of images and annotations of the cathedral, with regards to their temporal, spatial and semantic aspects, in addition to their visual content. Using prevailing clustering methods focusing on different criteria, we obtained several clusters of images from which new information can be inferred. By exploring these complementary aspects, we identified similarity links between the images. Further work will focus on multimodal analyses of the corpus.

Over the last decade, a large number of digital documentation projects have demonstrated the potential of image-based modelling of heritage objects in the context of documentation, conservation, and restoration. The inclusion of these emerging methods in the daily monitoring of the activities of a heritage restoration site (context in which hundreds of photographs per day can be acquired by multiple actors, in accordance with several observation and analysis needs) raises new questions at the intersection of big data management, analysis, semantic enrichment, and more generally automatic structuring of this data. In this article we propose a data model developed around these questions and identify the main challenges to overcome the problem of structuring massive collections of photographs through a review of the available literature on similarity metrics used to organise the pictures based on their content or metadata. This work is realized in the context of the restoration site of the Notre-Dame de Paris cathedral that will be used as the main case study.

In recent years, the number of data sources and the amount of generated data are increasing continuously. This voluminous data leads to several issues of storage capacities, data inconsistency, and difficulty of analysis. In the midst of all these difficulties, data integration techniques try to offer solutions to optimally face these problems. In addition, adding semantics to data integration solutions has proven its utility for tackling these difficulties, since it ensures semantic interoperability. In our work, which is placed in this context, we propose a semantic-based data integration and mapping maintenance approach with application to drugs domain. The contributions of our proposal deal with 1) a virtual semantic data integration and 2) an automated mapping maintenance based on deep learning techniques. The goal is to support the continuous and occasional data sources changes, which would highly affect the data integration. To this end, we focused mainly on managing metadata change within an integrated structure, refereed to as mapping maintenance. Our deep learning models encapsulate both convolutional, and Long short-term memory networks. A prototype has been developed and performed on two use cases. The process is fully automated and the experiments show significant results compared to the state of the art.

The traceability of drugs remains a significant issue, especially with the number of products proposed on the Web. Detecting counterfeit products is a challenge, given what these products could represent as a danger to health and the economy. Several studies have been made in the literature and tried to propose solutions. However, several limitations remain. In this context, this paper presents our framework, ChainDrugTrac, for the traceability and detection of counterfeit pharmaceutical products. After studying existing work and identifying the limits, our contributions could be resumed around 1) proposing architecture for drug traceability based on the blockchain and 2) developing a blockchain-based prototype supporting the proposed architecture. The prototype has been implemented to evaluate our proposal and show its effectiveness.

In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC's IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre's visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns.

RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge Bases (KBs) representing as close as possible the actual contents of the KB both in terms of structure and data. RDF Summarization allows for better exploration and visualization of the underlying RDF graphs, optimization of queries or query evaluation in multiple steps, better understanding of connections in Linked Datasets and many other applications. In the literature, there are efforts reported presenting algorithms for extracting summaries from RDF KBs. These efforts though provide different results while applied on the same KB, thus a way to compare the produced summaries and decide on their quality and best-fitness for specific tasks, in the form of a quality framework, is necessary. So in this work, we propose a comprehensive Quality Framework for RDF Graph Summarization that would allow a better, deeper and more complete understanding of the quality of the different summaries and facilitate their comparison. We work at two levels: the level of the ideal summary of the KB that could be provided by an expert user and the level of the instances contained by the KB. For the first level, we are computing how close the proposed summary is to the ideal solution (when this is available) by defining and computing its precision, recall and F-measure against the ideal solution. For the second level, we are computing if the existing instances are covered (i.e. can be retrieved) and at which degree by the proposed summary. Again we define and compute its precision, recall and F-measure against the data contained in the original KB. We also compute the connectivity of the proposed summary compared to the ideal one, since in many cases (like, e.g., when we want to query) this is an important factor and in general in RDF, linked datasets are usually used. We use our quality framework to test the results of three of the best RDF Graph Summarization algorithms, when summarizing different (in terms of content) and diverse (in terms of total size and number of instances, classes and predicates) KBs and we present comparative results for them. We conclude this work by discussing these results and the suitability of the proposed quality framework in order to get useful insights for the quality of the presented results.

In this paper we present a Semantic Indoor Trajectory Model aimed at supporting the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we are interested in combining aspects of state-of-the art semantic outdoor trajec-tory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC's In-doorGML standard. We drive the discussion on those modeling issues and details that have been overlooked so far or where our approach deviates from typical practices. We illustrate the modeling part with instantiations from the Louvre Museum in an effort to provide a pragmatic view of what a Semantic Indoor Trajectory Model ought to represent and ideally also how.

The large number of linked datasets in the Web, and their diversity in terms of schema representation has led to a fragmented dataset landscape. Querying and addressing information needs that span across disparate datasets requires the alignment of such schemas. Majority of schema and ontology alignment approaches focus exclusively on class alignment. Yet, relation alignment has not been fully addressed, and existing approaches fall short on addressing the dynamics of datasets and their size.In this work, we address the problem of relation alignment across disparate linked datasets. Our approach focuses on two main aspects. First, online relation alignment, where we do not require full access, and sample instead for a minimal subset of the data. Thus, we address the main limitation of existing work on dealing with the large scale of linked datasets, and in cases where the datasets provide only query access. Second, we learn supervised machine learning models for which we employ various features or matchers that account for the diversity of linked datasets at the instance level. We perform an experimental evaluation on real-world linked datasets, DBpedia, YAGO, and Freebase. The results show superior performance against state-of-the-art approaches in schema matching, with an average relation alignment accuracy of 84%. In addition, we show that relation alignment can be performed efficiently at scale.

Nowadays, electronic museum guides have evolved to a point that can act as navigational and informational devices in the museum context; thus they also enable the collection of large volumes of spatiotemporal visitor movement data, from which individual visitor trajectories can be extracted and analyzed. These trajectories have individual characteristics expressed through unique semantics in each museum context (based on the museum, its exhibits and its visitors) and they are restricted in an indoor environment that provides additional constraints. This work presents the benefits, the challenges, and a direction for studying museum visitor movements through context-aware indoor trajectory modeling, mining and analysis.

We consider here the problem of adding diversity requirements for the results of continuous top-k queries in a large scale social network, while preserving an efficient, continuous processing. We propose the DA-SANTA algorithm, which smoothly adds content diversity to the continuous processing of top-k queries at the social network scale. The experimental study demonstrates the very good properties in terms of effectiveness and efficiency of this algorithm.

With the huge popularity of social networks, publishing and consuming content through information streams is nowadays at the heart of the new Web. Top-k queries over the streams of interest allow limiting results to relevant content, while continuous processing of such queries is the most effective approach in large scale systems. Current systems fail in combining continuous top-k processing with rich scoring models including social network criteria. We present in this paper our vision on the possible features of a social network of information streams, with a rich scoring model compatible with continuous top-k processing.

The Linked Open Data (LOD) cloud brings together information described in RDF and stored on the web in (possibly distributed) RDF Knowledge Bases (KBs). The data in these KBs are not necessarily described by a known schema and many times it is extremely time consuming to query all the interlinked KBs in order to acquire the necessary information. To tackle this problem, we propose a method of summarizing large RDF KBs using approximate RDF graph patterns and calculating the number of instances covered by each pattern. Then we transform the patterns to an RDF schema that describes the contents of the KB. Thus we can then query the RDF graph summary to identify whether the necessary information is present and if so its size, before deciding to include it in a federated query result.

The Linked Open Data (LOD) cloud brings together information described in RDF and stored on the web in (possibly distributed) RDF Knowledge Bases (KBs). The data in these KBs are not necessarily described by a known schema and many times it is extremely time consuming to query all the interlinked KBs in order to acquire the necessary information. But even when the KB schema is known, we need actually to know which parts of the schema are used. We solve this problem by summarizing large RDF KBs using top-K approximate RDF graph patterns, which we transform to an RDF schema that describes the contents of the KB. This schema describes accurately the KB, even more accurately than an existing schema because it describes the actually used schema, which corresponds to the existing data. We add information on the number of various instances of the patterns, thus allowing the query to estimate the expected results. That way we can then query the RDF graph summary to identify whether the necessary information is present and if it is present in significant numbers whether to be included in a federated query result.

Recent years have seen the rise of Web data, in particular Linked Data, with, up to now, more than 1000 datasets in the Linked Open Data Cloud (LOD). These datasets are mostly of entity-centric nature and are highly heterogeneous in terms of domains, language, schema, etc. Hence, the vision of uniformly querying such resources in the LOD has a long way to go. While equivalent entity instances across datasets are often linked by sameAs links, relations from different datasets and schemas are usually not aligned. In this paper, we propose an on-line instance-based relation alignment approach. The alignment may be performed during query execution and requires partial information from the datasets. We align relations to a target dataset using association rule mining approaches. We sample for equivalent entity instances with two main sampling strategies. Preliminary experiments, show that we are able to align relations with high accuracy, even if accessing the entire datasets is impossible or impractical.

Information streams provide today a prevalent way of publishing and consuming content on the Web, especially due to the great success of social networks. Top-k queries over the streams of interest allow limiting results to the most relevant content, while continuous processing of such queries is the most effective approach in large scale systems. However, current systems fail in combining continuous top-k processing with rich scoring models including social network criteria. We present here the SANTA algorithm, able to handle scoring functions including content similarity, but also social network criteria and events in a continuous processing of top-k queries. We propose a variant (SANTA+) that accelerates the processing of interaction events in social networks. We compare SANTA/SANTA+ with an extension of a state-of-the-art algorithm and report a rich experimental study of our approach.

We propose to demonstrate DORIS, a system that maps the schema of a Web Service automatically to the schema of a knowledge base. Given only the input type and the URL of the Web Service, DORIS executes a few probing calls, and deduces an intensional description of the Web service. In addition, she computes an XSLT transformation function that can transform a Web Service call result in XML to RDF facts in the target schema. Users will be able to play with DORIS, and to see how real-world Web Services can be mapped to large knowledge bases of the Semantic Web.