Logical image understanding involves interpreting and reasoning about the relationships and consistency within an image's visual content. This capability is essential in applications such as industrial inspection, where logical anomaly detection is critical for maintaining high-quality standards and minimizing costly recalls. Previous research in anomaly detection (AD) has relied on prior knowledge for designing algorithms, which often requires extensive manual annotations, significant computing power, and large amounts of data for training. Autoregressive, multimodal Vision Language Models (AVLMs) offer a promising alternative due to their exceptional performance in visual reasoning across various domains. Despite this, their application to logical AD remains unexplored. In this work, we investigate using AVLMs for logical AD and demonstrate that they are well-suited to the task. Combining AVLMs with format embedding and a logic reasoner, we achieve SOTA performance on public benchmarks, MVTec LOCO AD, with an AUROC of 86.0% and an F1-max of 83.7% along with explanations of the anomalies. This significantly outperforms the existing SOTA method by 18.1% in AUROC and 4.6% in F1-max score.

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As part of modern research practices, the FAIR data principles have become essential for data discoverability, usability, and sharing.Existing implementations for automatically assessing FAIR adherence (FAIRness) often suffer from limited usability, inconsistent accuracy, and difficult-to-interpret results, as they require explicit rules to cover for specific FAIR assessment frameworks, which are not easy to generalize.This paper introduces Fair-Way, an open source tool that leverages Large Language Models (LLMs) to automate FAIRness assessment.Fair-Way applies a divide-and-conquer approach to decompose the assessment process into fine-grained tasks, as well as to split the metadata into manageable chunks.Evaluation demonstrates that Fair-Way achieves performance comparable to existing tools, while outperforming them in several key metrics.Moreover, Fair-Way generalizes across FAIR assessment indicators without requiring explicitly programmed logic and supports both structured and unstructured metadata in diverse formats.Finally, it enables user-defined, domain-specific tests, which are typically not supported by other systems.Overall, Fair-Way represents a scalable and flexible solution to accelerate FAIR data practices across research domains.

Gradient inversion attacks can reconstruct the victim's private data once they have access to the victim's model and gradient. However, existing research is still immature, and many attacks are conducted in ideal conditions. It is unclear how damaging such attacks really are and how they can be effectively defended. In this paper, we first summarize the current relevant researches and their limitations. Then we design a general gradient inversion attack framework, which can attack both FedSGD and FedAVG. We propose approaches to enhance the label inference and image restoration, respectively. Our approach surpasses the SOTA attacks, by successfully attacking the batches from ImageNet while other methods fail to attack. Finally, we suggest several defense strategies without any utility loss from extensive experiments. We are confirmed that our work makes people aware of the privacy issues and can actively avoid the potential risks.

Many datasets are of increasingly high dimension- ality, where a large number of features could be irrelevant to the learning task. The inclusion of such features would not only introduce unwanted noise but also increase computational complexity. Deep neural networks (DNNs) outperform machine learning (ML) algorithms in a variety of applications due to their effectiveness in modelling complex problems and handling high-dimensional datasets. However, due to non-linearity and higher-order feature interactions, DNN models are unavoidably opaque, making them black-box methods. In contrast, an interpretable model can identify statistically significant features and explain the way they affect the model’s outcome. In this paper, we propose a novel method to improve the interpretability of blackbox models in the case of high-dimensional datasets. First, a black-box model is trained on full feature space that learns useful embeddings on which the classification is performed. To decompose the inner principles of the black-box and to identify top-k important features (global explainability), probing and perturbing techniques are applied. An interpretable surrogate model is then trained on top-k feature space to approximate the black-box. Finally, decision rules and counterfactuals are derived from the surrogate to provide local decisions. Our approach outperforms tabular learners, e.g., TabNet and XGboost, and SHAP-based interpretability techniques, when tested on a number of datasets having dimensionality between 54 and 20,5311.1GitHub: https://github.com/rezacsedu/DeepExplainHidim

In the era of big data and artificial intelligence, distributed machine learning has emerged as a promising solution to address privacy and security concerns while fostering collaboration between multiple parties. However, with the data increased in terms of volume, velocity, veracity and variety, ensuring effective data management and responsible data sharing in these systems remains a challenge. In this paper, we explore the potential solutions and propose a system architecture that incorporates FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to promote effective and secure collaboration in federated learning. A minimum set of metadata schemes tailored for distributed machine learning and a decentralized authentication and authorization mechanism based on self-sovereign identity and policy-based access control architecture are proposed. To demonstrate the effectiveness of the proposed system, we conduct a FAIRness assessment and evaluate the model performance with a federated learning use case. Our work contributes to the development of an efficient, secure, and collaborative data ecosystem, fostering innovation in artificial intelligence and machine learning.

The fast-growing complexity of decentralized infrastructures outpaces end-users from acquiring the required knowledge to operate them. A possible remedy for this ever-aggravating problem might be the integration of natural-language-driven interfaces that allow end-users to interact with those systems. This work focuses on a Distributed Analytics (DA) use case, which includes the challenge above. We propose a DA-tailored and module-based monitoring and transaction bot called HAICO, which assists users in keeping track of their activities. Our user study (n=50) shows that our approach indeed contributes to increased transparency and accessibility of digital assets in these ecosystems. The results show that integrating automated agents can lower the barriers for non-technology-versed users to foster efficiency and adoption time.

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.

In incident management, response and recovery actions are designed to effectively mitigate ongoing or future cyberattacks. A security playbook consists of a pipeline of instructions to document necessary response and recovery actions to deal with a specific type of incident. Since many organisations lack the resources, expertise and know-how to handle incidents, sharing playbooks across organisations could significantly improve their response capabilities against cyberattacks. However, playbooks are often organisation specific and usually not machine-readable, sharable and interoperable. In this work, we propose a semantic web-based approach to capture the knowledge of incident response and recovery steps to support sharing of playbooks based on a standardised and common vocabulary. To further demonstrate our approach, we introduce SASP, a proof-of-concept tool based on Semantic MediaWiki for playbook management. In this paper, we describe the key requirements from incident handlers to share playbooks, SASP architecture design, and its core components and functionalities. We then discuss the results of our user-centric evaluation conducted on members of different Security Operation Centres and the further potential of the solution.

Data-centric workflows across organizations are gaining more and more popularity. To automate this process, the traditional approaches centralise related data from different organizations to the cloud, and then use a workflow engine to complete cross-organizational collaboration. There are limitations of those approaches, such as the requirement of data centralisation which could lead to the leakage of critical data. In this work, we present a workflow platform for consuming distributed data based on Kubernetes and JupyterFlow, and use blockchain technology to guarantee security and privacy through empowering the data owner with control of their own data. To reduce replication and network throughput, the blockchain only contains the meta data referring to the data in off-chain storage. We develope a JupyterHub extension to support data registration and query, and used the RESTFul API to connect the web application with the blockchain network. Finally, we demonstrate a simple data processing use case as proof-of-concept to validate our proposed platform.

Recent trends lead to more and more available data from automated manufacturing systems. Many data scientists collect data from production machines via the fast and widespread OPC Unified Architecture (OPC UA) communication protocol. However, heterogeneous vendor-specific configurations require high manual effort for establishing new data collections, and therefore valuable metadata is either extracted hard-coded or not at all. Our previous publication tackled this challenge purely from a computer science perspective and proposed a transformation of the query language XPath to OPC UA to enable more convenient and expressive queries. Remaining open research questions include a more sophisticated data collection from multiple automated manufacturing systems and proper integration into enterprise-scaled industrial communication systems.This paper focuses the manufacturing perspective and covers these research questions with a real-world implementation for a 500 tons High Pressure Die Casting (HPDC) production cell with six embedded OPC UA servers. We formulate XPath queries and apply these to augment the current workflow, which only extracts raw sensor values, to a more comprehensive one that additionally captures metadata such as units, value ranges, and measurement precision. Our main contributions include an aggregation of multiple individual data collection setups into a general one and an embedding of these into a fully-fledged data lake integration. The results demonstrate an integration of an XPath-enhanced OPC UA data collection into industrial communication for automated manufacturing systems, which dramatically reduces complexity and manual effort for experts.

The study of genetic variants (GVs) can help find correlating population groups and to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning techniques are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation, deep neural networks (DNNs) can learn non-linear mappings that allow transforming GVs data into more clustering and classification friendly representations than manual feature selection. In this paper, we propose convolutional embedded networks (CEN) in which we combine two DNN architectures called convolutional embedded clustering (CEC) and convolutional autoencoder (CAE) classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning to 95 million GVs from the '1000 genomes' (covering 2,504 individuals from 26 ethnic origins) and 'Simons genome diversity' (covering 279 individuals from 130 ethnic origins) projects. Quantitative and qualitative analyses with a focus on accuracy and scalability show that our approach outperforms state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index (ARI) of 0.915, the normalized mutual information (NMI) of 0.92, and the clustering accuracy (ACC) of 89 percent. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient (MCC) score of 0.9004 and 0.8245, respectively. Further, to provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees (GBT) and SHapley Additive exPlanations (SHAP). Overall, our approach is transparent and faster than the baseline methods, and scalable for 5 to 100 percent of the full human genome.

In this paper, we propose an explainable approach for hate speech detection from the under-resourced Bengali language, which we called DeepHateExplainer. In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates using a neural ensemble method of transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased/uncased, and XLM-RoBERTa). Subsequently, important (most and least) terms are identified using sensitivity analysis and layer-wise relevance propagation (LRP), before providing human-interpretable explanations11To foster reproducible research, we make available the data, source codes, models, and notebooks: https://github.com/rezacsedu/DeepHateExplainer. Finally, we compute comprehensiveness and sufficiency scores to measure the quality of explanations w.r.t faithfulness. Evaluations against machine learning (linear and tree-based models) and neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1-scores of 78%, 91%, 89%, and 84%, for political, personal, geopolitical, and religious hates, respectively, outperforming both ML and DNN baselines22Read an extended version of this paper: https://arxiv.org/abs/2012.14353.

Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.

In this paper1, we proposed an explainable deep neural networks (DNN)-based method for automatic detection of COVID-19 symptoms from chest radiography (CXR) images, which we call ‘DeepCOVIDExplainer’. We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID-19 cases. CXR images are first comprehensively preprocessed and augmented before classifying with a neural ensemble method, followed by highlighting class-discriminating regions using gradient-guided class activation maps (Grad-CAM ++) and layer-wise relevance propagation (LRP). Further, we provide human-interpretable explanations for the diagnosis. Evaluation results show that our approach can identify COVID-19 cases with a positive predictive value (PPV) of 91.6%, 92.45%, and 96.12%, respectively for normal, pneumonia, and COVID-19 cases, respectively, outperforming recent approaches.1Read longer version of this paper: https://arxiv.org/pdf/2004.04582.pdf

In the production industry, the volume, variety, and velocity of data as well as the number of deployed protocols increase exponentially due to the influences of the Internet-of-Things (IoT) advances. While hundreds of isolated solutions exist to utilize these data, e.g., optimizing processes or monitoring machine conditions, the lack of a unified data handling and exchange mechanism hinders the implementation of approaches to improve the quality of decisions and processes in such an interconnected environment. The vision of anInternet of Productionpromises the establishment of aWorldwide Lab, where data from every process in the network can be utilized, even interorganizational and across domains. While numerous existing approaches consider interoperability from an interface and communication system perspective, fundamental questions of data and information interoperability remain insufficiently addressed. In this article, we identifytenkey issues, derived from three distinctive real-world use cases that hinder large-scale data interoperability for industrial processes. Based on these issues, we derive a set offivekey requirements for future (IoT) data layers, building upon the FAIR data principles. We propose to address them by creatingFactDAG, a conceptual data layer model for maintaining a provenance-based, directed acyclic graph of facts, inspired by successful distributed version-control and collaboration systems. Eventually, such a standardization should greatly shape the future of interoperability in an interconnected production industry.

Abstract An accurate diagnosis and prognosis for cancer are specific to patients with particular cancer types and molecular traits, which needs to address carefully. The discovery of important biomarkers is becoming an important step toward understanding the molecular mechanisms of carcinogenesis in which genomics data and clinical outcomes need to be analyzed before making any clinical decision. Copy number variations (CNVs) are found to be associated with the risk of individual cancers and hence can be used to reveal genetic predispositions before cancer develops. In this paper, we collect the CNVs data about 8000 cancer patients covering 14 different cancer types from The Cancer Genome Atlas. Then, two different sparse representations of CNVs based on 578 oncogenes and 20,308 protein-coding genes, including genomic deletions and duplication across the samples, are prepared. Then, we train Conv-LSTM and convolutional autoencoder (CAE) networks using both representations and create snapshot models. While the Conv-LSTM can capture locally and globally important features, CAE can utilize unsupervised pretraining to initialize the weights in the subsequent convolutional layers against the sparsity. Model averaging ensemble (MAE) is then applied to combine the snapshot models in order to make a single prediction. Finally, we identify most significant CNVs biomarkers using guided-gradient class activation map plus (GradCAM++) and rank top genes for different cancer types. Results covering several experiments show fairly high prediction accuracies for the majority of cancer types. In particular, using protein-coding genes, Conv-LSTM and CAE networks can predict cancer types correctly at least 72.96% and 76.77% of the cases, respectively. Contrarily, using oncogenes gives moderately higher accuracies of 74.25% and 78.32%, whereas the snapshot model based on MAE shows overall 2.5% of accuracy improvement.

The discovery of important biomarkers is a significant step towards understanding the molecular mechanisms of carcinogenesis; enabling accurate diagnosis for, and prognosis of, a certain cancer type. Before recommending any diagnosis, genomics data such as gene expressions (GE) and clinical outcomes need to be analyzed. However, complex nature, high dimensionality, and heterogeneity in genomics data make the overall analysis challenging. Convolutional neural networks (CNN) have shown tremendous success in solving such problems. However, neural network models are perceived mostly as 'black box' methods because of their not well-understood internal functioning. However, interpretability is important to provide insights on why a given cancer case has a certain type. Besides, finding the most important biomarkers can help in recommending more accurate treatments and drug repositioning. Moreover, the 'right to explanation' of the EU GDPR gives patients the right to know why and how an algorithm made a diagnosis decision. Hence, in this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data. We used genomics data about 9,074 cancer patients covering 33 different cancer types from the Pan-Cancer Atlas on which we trained CNN and VGG16 networks using guided-gradient class activation maps++ (GradCAM++). Further, we generate class-specific heat maps to identify significant biomarkers and computed feature importance in terms of mean absolute impact to rank top genes across all the cancer types. Quantitative and qualitative analyses show that both models exhibit high confidence at predicting the cancer types correctly giving an average precision of 96.25%. To provide comparisons with the baselines, we identified top genes, and cancer-specific driver genes using gradient boosted trees and SHapley Additive exPlanations (SHAP). Finally, our findings were validated with the annotations provided by the TumorPortal.

An important part of the Semantic Web vision is the idea that data is shared seamlessly and that world wide distributed, accessible, and interlinked knowledge bases can be created. However, the current incarnation of the Semantic Web falls short of this vision: while some necessary infrastructure (e.g., Linked Data) has been put in place, the current use of Linked Data in the Semantic Web is still happening in data silos, and sharing and reusing of knowledge is cumbersome and not straightforward. Recently the idea of prototypical objects was proposed to remedy this situation. This concept, known as Prototypes originates from early Frame systems and is also adopted in programming languages such as Javascript. In this vision paper we describe how a distributed file system forms a natural habitat for prototype knowledge representation, advancing the Semantic Web. In particular, we describe how we envision the deployment of Linked Data and Prototype Knowledge bases atop of the InterPlanetary File System (IPFS), which has several useful features matching the needs for knowledge representation based on prototypes.

Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics by using the OpenIoT1middleware; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.

A significant portion of the LOD cloud consists of Life Sciences data sets, which together contain billions of clinical facts that interlink to form a "Web of Clinical Data". However, tools for new publishers to find relevant datasets that could potentially be linked to are missing, particularly in specialist domain-specific settings. Based on a set of domain-specific keywords extracted from a local dataset, this paper proposes methods to automatically identify relevant public SPARQL endpoints from a list of candidates.

Most of the existing benchmark systems for federated SPARQL query systems rely on a set of predefined static queries over a particular set of data sources. Such benchmark are useful for comparing general purpose SPARQL query federation systems such as FedX, SPLENDID etc. However, special purpose federation systems such as TopFed, SAFE etc. cannot be tested with these static benchmarks since these systems only operate on a specific data sets and the corresponding queries. To facilitate the process of benchmarking for such special purpose SPARQL query federation systems, we propose QFed, a dynamic SPARQL query set generator that takes into account the characteristics of both dataset and queries along with the cost of data communication. Our experimental results show that QFed can successfully generate a large set of meaningful federated SPARQL queries to be considered for the performance evaluation of different federated SPARQL query engines.

The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from multiple sources and augment the design of in silico experiments for cancer chemoprevention drug discovery. The different components of the LBDS facilitate both the bioinformaticians and the biomedical researchers to publish, link, query and visually explore the heterogeneous datasets. We have extensively evaluated the usability of the entire platform. In this paper, we showcase three different workflows depicting real-world scenarios on the use of LBDS by the domain users to intuitively retrieve meaningful information from the integrated sources. We report the important lessons that we learned through the challenges encountered and our accumulated experience during the collaborative processes which would make it easier for LD practitioners to create such dataspaces in other domains. We also provide a concise set of generic recommendations to develop LD platforms useful for drug discovery.

Bioinformatics research relies heavily on the ability to discover and correlate data from various sources. The specialization of life sciences over the past decade, coupled with an increasing number of biomedical datasets available through standardized interfaces, has created opportunities towards new methods in biomedical discovery. Despite the popularity of semantic web technologies in tackling the integrative bioinformatics challenge, there are many obstacles towards its usage by non-technical research audiences. In particular, the ability to fully exploit integrated information needs using improved interactive methods intuitive to the biomedical experts. In this report we present ReVeaLD (a Real-time Visual Explorer and Aggregator of Linked Data), a user-centered visual analytics platform devised to increase intuitive interaction with data from distributed sources. ReVeaLD facilitates query formulation using a domain-specific language (DSL) identified by biomedical experts and mapped to a self-updated catalogue of elements from external sources. ReVeaLD was implemented in a cancer research setting; queries included retrieving data from in silico experiments, protein modeling and gene expression. ReVeaLD was developed using Scalable Vector Graphics and JavaScript and a demo with explanatory video is available at http://www.srvgal78.deri.ie:8080/explorer. A set of user-defined graphic rules controls the display of information through media-rich user interfaces. Evaluation of ReVeaLD was carried out as a game: biomedical researchers were asked to assemble a set of 5 challenge questions and time and interactions with the platform were recorded. Preliminary results indicate that complex queries could be formulated under less than two minutes by unskilled researchers. The results also indicate that supporting the identification of the elements of a DSL significantly increased intuitiveness of the platform and usability of semantic web technologies by domain users.

Biomedical and genomic data are inherently heterogeneous and their recent proliferation over the Web has demanded innovative querying methods to help domain experts in their clinical and research studies. In this paper we present the use of Semantic Web technologies in querying diverse phenotype-genotype associations for supporting personalized medicine and potentially helping to discover new associations. Our initial results suggest that Semantic Web technologies has competitive advantages in extracting, consolidating and presenting phenotype-genotype associations that resides in various bioinformatics resources. The developed querying method could support researchers and medical professionals in discovering and utilizing information on published associations relating disease, treatment, adverse events and environmental factors to genetic markers from multiple repositories.

Biosimulation researchers use a variety of models, tools and languages for capturing and processing different aspects of biological processes. However, current modeling methods do not capture the underlying semantics of the biosimulation models sufficiently to support building, reusing, composing and merging complex biosimulation models originating from diverse experiments. In this paper, we propose an ontology based and multi-layered biosimulation model to facilitate researchers to share, integrate and collaborate their knowledge bases at Web scale. In particular, we investigate the semantic biosimulation model under the context of the multi-scale finite element (FE) modelling of the inner-ear. The proposed ontology-based biosimulation model will provide a homogenized and standardized access to the shared, semantically integrated and harmonized datasets for clinical data (histological data, micro-CT images of the cochlea, pathological data) and inner ear FE simulation models. The work presented in this paper is analyzed and designed as part of the SIFEM EU project.

Increasingly, ad-hoc online task groups must make decisions about jointly created artifacts such as open source software and Wikipedia articles. Time-consuming and laborious attention to textual discussions is needed to make such decisions, for which computer support would be beneficial. Yet there has been little study of the argumentation patterns that distributed ad-hoc online task groups use in evaluation and decision-making. In a corpus of English Wikipedia deletion discussions, we investigate the argumentation schemes used, the role of the arguer's experience, and which arguments are acceptable to the audience. We report three main results: First, the most prevalent patterns are the Rules and Evidence schemes from Walton's catalog of argumentation schemes [33], which comprise 36% of arguments. Second, we find that familiarity with community norms correlates with the novices' ability to craft persuasive arguments. Third, acceptable arguments use community-appropriate rhetoric that demonstrate knowledge of policies and community values while problematic arguments are based on personal preference and inappropriate analogy to other cases.

A set of well-integrated clinical terminologies is at the core of delivering an efficient clinical trial system. The design and outcomes of a clinical trial can be improved significantly through an unambiguous and consistent set of clinical terminologies used in a participating clinical institute. However, due to lack of generalised legal and technical standards, heterogeneity exists between prominent clinical terminologies as well as within and between clinical systems at several levels, e.g., data, schema, and medical codes. This article specifically addresses the problem of integrating local or proprietary clinical terminologies with the globally defined universal concepts or terminologies. To deal with the problem of ambiguous, inconsistent, and overlapping clinical terminologies, domain and knowledge representation specialists have been repeatedly advocated the use of formal ontologies. We address two key challenges in developing an ontology-based clinical terminology (1) an ontology building methodology for clinical terminologies that are separated in global and local layers, and (2) aligning global and local clinical terminologies. We present Semantic Electronic Health Record (SEHR) ontology that covers multiple sub-domains of Healthcare and Life Sciences (HCLS) through specialisation of the upper-level Basic Formal Ontology (BFO). One of the main features of SEHR is layering and adaptation of local clinical terminologies with the upper-level BFO. Our empirical evaluation shows an agreement of clinical experts confirming SEHR's usability in clinical trials.

The Web evolved from a text-based system to the current rich and interactive medium that supports images, 2D graphics, audio and video. The major media type that is still missing is 3D graphics. Although various approaches have been proposed (most notably VRML/X3D), they have not been widely adopted. One reason for the limited acceptance is the lack of 3D interaction techniques that are optimal for the hypertext-based Web interface. We present a novel strategy for accessing integrated information spaces, where hypertext and 3D graphics data are simultaneously available and linked. We introduce a user interface that has two modes between which a user can switch anytime: the driven by simple hypertext-based interactions "don't-make-me-think" mode, where a 3D scene is embedded in hypertext and the more immersive 3D "take-me-to-the-Wonderland" mode, which immerses the hypertextual annotations into the 3D scene. A user study is presented, which characterizes the user interface in terms of its efficiency and usability.

We present a component-based, conceptual architecture for Semantic Web applications. It describes the high-level functionality that substantially differentiates Resource Description Framework (RDF)-supported applications from database-driven applications. We provide a strong empirical grounding for this architecture through a survey of Semantic Web applications over most of the past decade. Our empirical approach allows us to describe the current state of the art for the development and deployment of applications on the Web of Data. In addition, we determine how far the adoption of signature research topics of the Semantic Web, such as, data reuse, data integration, and reasoning, has progressed. We, then, discuss the main implementation challenges that developers using Semantic technologies are facing, as observed in the survey. We build on this in order to suggest future approaches to facilitate the standardization of components and the development of software engineering tools to increase the uptake of the Web of Data.

As open data formats are becoming popular amongst Web developers, this gives rise to an increase in the creation and consumption of structured data. Data creators are easily linking diverse datasets which is creating a graph of interlinked data called the Web of Data. This increase in linked structured data demands mechanisms to ensure that access is controlled and granted to only those who are eligible to consume the data. However, although there is a lot of work done related to access control on the Web, this cannot be applied directly to structured data due to the different nature of how the data is formatted. Moreover, not much has been done to create fine-grained access control mechanisms for the Web of Data. In this paper we therefore present our access control framework that consists of a light-weight vocabulary for defining fine-grained privacy preferences for structured data and a privacy preference manager which allows users to (1) create privacy preferences based on our vocabulary and (2) restrict access to their data to third-party users.

Social tagging in online communities has become an important method for reflecting classified thoughts of individual users. A number of social Web sites provide tagging functionalities and also offer folksonomies within or across the sites. However, it is practically not easy to find users' interests based on such folksonomies. In this paper, we provide a novel approach for clustering user-centric interests by analyzing tagging practices of individual users. To do this, we collect Really Simple Syndication data from blogosphere, find conceptual clusters using formal concept analysis, and then evaluate the significance of these clusters. The results of the empirical evaluation show that we can effectively recommend different collections of tags to an individual or a set of users.

One of the ways of decreasing selection times in displayed menus is through the use of radial layouts which shorten distances to the items and increase the item sizes. Previous work on radial menus does not demonstrate that radial layouts are beneficial for menu hierarchies. The paper focuses on this topic.

There have been many studies of computer based text reading. However, only a few have considered text integrated with video and 3D graphics. This paper presents an investigation into the effects of varying (a) text drawing style (plain, billboard, Anti-Interference, shadow), (b) image polarity (positive and negative), and (c) background style (video and 3D) on text readability. Reading speed and accuracy were measured and subjective views of participants recorded.

We demonstrate Sig.ma, both a service and an end user application to access the Web of Data as an integrated information space.

While the use of Web 2.0 tools and principles in organizations - a practice commonly known as Enterprise 2.0 - helps knowledge workers to collaboratively build and exchange information more easily, it introduces several issues in terms of efficiently integrating and retrieving this information. In t

Currently a large number of Web sites are driven by Content Management Systems (CMS) which manage textual and multimedia content but also - inherently - carry valuable information about a site’s structure and content model. Exposing this structured information to the Web of Data has so far required considerable expertise in RDF and OWL modelling and additional programming effort. In this paper we tackle one of the most popular CMS: Drupal. We enable site administrators to export their site content model and data to the Web of Data without requiring extensive knowledge on Semantic Web technologies. Our modules create RDFa annotations and – optionally – a SPARQL endpoint for any Drupal site out of the box. Likewise, we add the means to map the site data to existing ontologies on the Web with a search interface to find commonly used ontology terms. We also allow a Drupal site administrator to include existing RDF data from remote SPARQL endpoints on the Web in the site. When brought together, these features allow networked RDF Drupal sites that reuse and enrich Linked Data. We finally discuss the adoption of our modules and report on a use case in the biomedical field and the current status of its deployment. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Although tagging is a widely accepted practice on the Social Web, it raises various issues like tags ambiguity and heterogeneity, as well as the lack of organization between tags. We believe that Semantic Web technologies can help solve many of these issues, especially considering the use of formal resources from the Web of Data in support of existing tagging systems and practices. In this article, we present the MOAT—Meaning Of A Tag—ontology and framework, which aims to achieve this goal. We will detail some motivations and benefits of the approach, both in an Enterprise 2.0 ecosystem and on the Web. As we will detail, our proposal is twofold: It helps solve the problems mentioned previously, and weaves user-generated content into the Web of Data, making it more efficiently interoperable and retrievable.

Search engines focusing on particular media types face difficulties in discovering suitable URIs on the Web. Since the engines are only interested in a small fraction of the Web, a crawler should use heuristics to concentrate on that fraction. To devise such a heuristic, we postulate four hypotheses based on RFCs and W3C recommendations to find cues for certain content types. Tests on a corpus of 22m files (793GB content size) containing 630m URIs show that for the content types text, image, and application, the recommendations are mostly being followed, while results for audio and video are much less consistent. Our findings and recommendations can be implemented as heuristics for efficient discovery of structured content on the Web on top of existing crawlers.

Publishing information on the Semantic Web using common formats enables data to be linked together, integrated and reused. In order to fully leverage the potential for interlinking data by reusing existing schemas, an intuitive way of viewing current usage of RDF vocabularies is required. We present a system which allows a user to view the most frequently occurring namespaces and classes in a large Semantic Web dataset, and the main linkage patterns that exist between them. Users can select a namespace of interest in order to examine usage of a particular ontology, and see how it is being combined with other vocabularies.

In this paper, we present an interactive matrix-based multi-dimensional visualization component which enables the users to explore a text collection along different conceptual dimensions. Of importance in our approach are the tight coupling of the users' personal ontologies representing their spheres of interest with the visualization component and the application of barycenter heuristic for edge crossing minimization to enhance its visual display. We also discuss how IVEA, the information visualization tool containing the proposed component, can address the commonly perceived constraints of building a personal ontology from scratch for IVEA to work.

Current search engines do not fully leverage semantically rich datasets, or specialise in indexing just one domain-specific dataset.We present a search engine that uses the RDF data model to enable interactive query answering over richly structured and interlinked data collected from many disparate sources on the Web.

Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly given the different nature of Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas.

This paper provides an overview on the ECOSPACE Integrated Project that is partly funded by the European Commission in the framework of the collaborative work environments framework. We present the motivation, goals and research approach of the project

In this poster we discuss a graphical notation for representing queries for semistructured data. We try to strike a balance between expressiveness of the query language and simplicity and understandability of the graphical notation. We present the primitives of the notation by means of examples.

Wikis are popular collaborative hypertext authoring environments, but they neither support structured access nor information reuse. Adding semantic annotations helps to address these limitations. We present an architecture for Semantic Wikis and discuss design decisions including structured access, views, and annotation language. We present our prototype SemperWiki that implements this architecture.

We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR) contained within this intersection: Description Logic Programs (DLP), and the closely related Description Horn Logic (DHL) which is an expressive fragment of first-order logic (FOL). DLP provides a significant degree of expressiveness, substantially greater than the RDF-Schema fragment of Description Logic. We show how to perform DLP-fusion: the bidirectional translation of premises and inferences (including typical kinds of queries) from the DLP fragment of DL to LP, and vice versa from the DLP fragment of LP to DL. In particular, this translation enables one to "build rules on top of ontologies": it enables the rule KR to have access to DL ontological definitions for vocabulary primitives (e.g., predicates and individual constants) used by the rules. Conversely, the DLP-fusion technique likewise enables one to "build ontologies on top of rules": it enables ontological definitions to be supplemented by rules, or imported into DL from rules. It also enables available efficient LP inferencing algorithms/implementations to be exploited for reasoning over large-scale DL ontologies.

OntoWebber is a system for managing data on the Web with formally encoded semantics. It aims at solving the problems current technologies are confronted with, namely, the reusability of software components, flexibility in personalization, and ease of maintenance for data intensive Web sites. Based on a domain ontology and a site modeling ontology, site views on the underlying data can be constructed as site models. Instantiation of these models will create a browsable Web site, and manipulation of the site models helps to reduce the high effort of personalizing and maintaining the Web site. In this paper we present the architecture and demonstrate the major components of the system.

Semantic Web Services are a promising combination of Semantic Web and Web service technology, aiming at providing means of automatically executing, discovering and composing semantically marked-up Web services. We envision peer-to-peer networks which allow for carrying out searches in real-time on permanently reconfiguring networks to be an ideal infrastructure for deploying a network of Semantic Web Service providers. However, P2P networks evolving in an unorganized manner suffer from serious scalability problems, limiting the number of nodes in the network, creating network overload and pushing search times to unacceptable limits. We address these problems by imposing a deterministic shape on P2P networks: We propose a graph topology which allows for very efficient broadcast and search, and we provide an efficient topology construction and maintenance algorithm which, crucial to symmetric peer-to-peer networks, does neither require a central server nor super nodes in the network. We show how our scheme can be made even more efficient by using a globally known ontology to determine the organization of peers in the graph topology, allowing for efficient concept-based search.

Metadata for the World Wide Web is important, but metadata for Peer-to-Peer (P2P) networks is absolutely crucial. In this paper we discuss the open source project Edutella which builds upon metadata standards defined for the WWW and aims to provide an RDF-based metadata infrastructure for P2P applications, building on the recently announced JXTA Framework. We describe the goals and main services this infrastructure will provide and the architecture to connect Edutella Peers based on exchange of RDF metadata. As the query service is one of the core services of Edutella, upon which other services are built, we specify in detail the Edutella Common Data Model (ECDM) as basis for the Edutella query exchange language (RDF-QEL-i) and format implementing distributed queries over the Edutella network. Finally, we shortly discuss registration and mediation services, and introduce the prototype and application scenario for our current Edutella aware peers.

Article Share on Enabling knowledge representation on the Web by extending RDF schema Authors: Jeen Broekstra Aidministrator Nederland bv, Holland Aidministrator Nederland bv, HollandView Profile , Michel Klein Vrije Universiteit Amsterdam, Holland Vrije Universiteit Amsterdam, HollandView Profile , Stefan Decker Department of Computer Science, Stanford University, Stanford Department of Computer Science, Stanford University, StanfordView Profile , Dieter Fensel Vrije Universiteit Amsterdam, Holland Vrije Universiteit Amsterdam, HollandView Profile , Frank van Harmelen Vrije Universiteit Amsterdam, Holland Vrije Universiteit Amsterdam, HollandView Profile , Ian Horrocks Department of Computer Science, University of Manchester, UK Department of Computer Science, University of Manchester, UKView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 467–478https://doi.org/10.1145/371920.372105Online:01 April 2001Publication History 50citation1,138DownloadsMetricsTotal Citations50Total Downloads1,138Last 12 Months20Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Ontobroker applies Artificial Intelligence techniques to improve access to heterogeneous, distributed and semi-structured information sources as they are presented in the World Wide Web or organization-wide intranets. It relies on the use of ontologies to annotate web pages, formulate queries and derive answers. In this paper we will briefly sketch Ontobroker. Then we will discuss its main shortcomings, i.e. we will share the lessons we learned from our exercise. We will also show how On2broker overcomes these limitations. Most important is the separation of the query and inference engines and the integration of new web standards like XML and RDF.