Knowledge graphs (KGs) are a powerful way of representing information for digital humanities. However, non-technical users often struggle at the outset of exploration, a challenge defined as the Initial Exploration Problem. The Tús Maith framework addresses this issue through curated natural language questions and answers (CuQAs) created from Competency Questions (CQs) that aim to convey the scope of a KG and provide meaningful entry points into it. While prior work has explored using large language models (LLMs) for CQ template generation, the template-filling step, where questions and answers are instantiated with entity information, remains a key challenge. In this paper, we evaluate whether LLMs have the capacity to support domain experts in this stage, focusing on the Virtual Record Treasury of Ireland (VRTI) KG, where accuracy, provenance, and robustness are crucial for practical use. Using structured JSON inputs derived from popular search terms and expert-authored templates, we generated and assessed 24,900 question-answer pairs across four LLMs (GPT-5, DeepSeek-V3.1, Gemini 2.0 Flash, Qwen-2.5-72B) under two provenance conditions (basic vs. full). Our evaluation considers slot fidelity, semantic similarity, completeness, hallucination rates, and runtime efficiency, with statistical tests conducted per run per LLM, and additional batch-level analysis (n = 68) to isolate provenance requirement effects. We further show that a lightweight JSON validation check is an effective proxy for ground truth semantic evaluation of factual question-answer pairs. These LLM-generated, validated questions form an intermediate step in the lifecycle from abstract CQ templates to filled-in questions and answers intended to be reviewed and refined by the VRTI KG’s domain experts (historians) to produce the final user-facing questions (CuQAs). To demonstrate the practical impact, we present a prototype (TMv1) of the Tús Maith framework and highlight the design implications for curator-facing interfaces: provenance-transparent interaction, validation-integrated workflows, and performance-transparent model selection.

Technology Ethics (TE) is an applied normative branch of ethics concerning ethical evaluation of technical practices with a view to prescribing changes and potentially addressing ethical dilemmas. The importance of TE education for computer science (CS) is expressed through its acknowledgement in professional codes of practice, by accreditation bodies and in literature and survey findings. Challenges to its incorporation in CS programmes include lack of expertise to develop content and assess, lack of curricula space and potential technical naivety at early stage CS education[1,2,3,4]. This poster presents a literature informed first iteration design of a serious game, SimEthica. The game can be pedagogically entangled[5] in a CS programme for cultivation of ethical thinking. SimEthica is designed to be entangled in a pedagogy for second year undergraduate Information Modelling (IM) students. Students learn how information models preserve information integrity and represent worldviews that seed AI algorithms. Virtuous practice design (VPD) proposed by Reijers provides an ethical basis for SimEthica [6]. VPD arises from explication and synthesis of virtue ethics, technical practice, narrative technologies and the philosophies of Paul Ricoeur [7].The design was evaluated by a panel including ethical, information modelling and health research expertise. A diagram of SimEthica design is presented to highlight key literature findings informing the design. The design will evolve through continuous capture and encapsulation of teacher and learners purpose, value and context.

BACKGROUND: Literature-based discovery (LBD) aims to help researchers to identify relations between concepts which are worthy of further investigation by text-mining the biomedical literature. While the LBD literature is rich and the field is considered mature, standard practice in the evaluation of LBD methods is methodologically poor and has not progressed on par with the domain. The lack of properly designed and decent-sized benchmark dataset hinders the progress of the field and its development into applications usable by biomedical experts. RESULTS: This work presents a method for mining past discoveries from the biomedical literature. It leverages the impact made by a discovery, using descriptive statistics to detect surges in the prevalence of a relation across time. The validity of the method is tested against a baseline representing the state-of-the-art "time-sliced" method. CONCLUSIONS: This method allows the collection of a large amount of time-stamped discoveries. These can be used for LBD evaluation, alleviating the long-standing issue of inadequate evaluation. It might also pave the way for more fine-grained LBD methods, which could exploit the diversity of these past discoveries to train supervised models. Finally the dataset (or some future version of it inspired by our method) could be used as a methodological tool for systematic reviews. We provide an online exploration tool in this perspective, available at https://brainmend.adaptcentre.ie/ .

Climate data is a valuable resource for understanding past weather patterns, assessing long-term climate trends, and conducting climate-related research. However, most existing knowledge graphs for climate data rely heavily on the standardized (per W3C recommendations) SOSA/SSN ontology, which can help improve general data accessibility, but typically overlooks the analytical applications of multisource climate data. To further enhance the accessibility of heterogeneous data for climate data analytics, this paper extends the CA ontology and implements a virtual knowledge graph for analytical applications. We emphasize the importance of incorporating observation metadata and geospatial representation into analytical applications. Through our study, we demonstrate the applicability of the proposed ontological model in deriving the ETCCDI indices. An example of the formation of the annual maximum daily temperature is given. Furthermore, we showcase the potential of LinkedGeoData in providing a more comprehensive geographical context for accessing climate data within the knowledge graph, leveraging the proposed ontological modeling and linked data principles.

Reducing industrial greenhouse gas (GHG) emissions has become imperative for mitigating the adverse effects of climate change. Accurate measurement and monitoring of industrial smoke plumes, which are a significant source of GHG emissions, are crucial for effective emission control strategies. This paper addresses the prospect of utilizing satellite images to measure industrial smoke plumes and explores the effectiveness of various computer vision (CV) technologies in this context. The study focuses on examining both modern deep learning and traditional machine learning models for detecting and segmenting industrial smoke plumes in satellite images. While deep learning models have shown remarkable performance in various CV tasks, their ability to accurately segment smoke plumes in satellite images remains limited, with an average intersection over union (IOU) of no more than 60%. However, certain deep learning models, such as U-Net and AttU-Net, exhibit promising capabilities in identifying challenging types of noise, including clouds, white building surfaces, and snow, which traditional machine learning models struggle with. Employing deep learning models for industrial smoke plume detection proves advantageous, as all models achieve an approximate detection accuracy and F1-Score of 90%. The findings from this research serve as a valuable foundation for further advancements in developing advanced deep learning models specifically tailored to handle the identified types of noise.

Autonomic network management approaches have not been widely adopted, mainly due to significant unsolved challenges. Challenges include technical complexity, lack of consistent models and knowledge bases describing the system, and the difficulty of evolving management methods and processes. Autonomic approaches often operate a closed control loop. Such loops enable dynamicity and are often intent driven, where system goals and requirements are declared, then automatically accomplished and maintained. These loops continuously monitor and analyze large amounts of information to infer knowledge about the system.Representing the knowledge as semantic graphs is well suited to automated inference, enabling hidden relationships, strategies and understanding to be identified. When applied in an autonomic network management system this automatic discovery of additional knowledge can be used in several ways to inform and improve intent driven closed control loops.This paper describes the design and evaluation of an ontology to represent and help interpret, validate and apply high level goals or ‘intents’ as part of a closed control loop. This approach enables these intents to be enforced, satisfied and maintained. The ontology forms part of a framework which generates graph-based data from network monitoring information collected in a commonly used network/cloud monitoring service (Prometheus). The ontology also models intents relative to the monitoring knowledge. Furthermore, the model has the capabilities to allow the monitored network to adapt, then helps plan how to continuously satisfy and maintain the intent. Finally, the ontology and framework are applied in a real-life use case, which relates to Quality of Service (QoS) assurance for a 5G Telecoms Network Slice. The use case is designed to motivate and demonstrate the usefulness of the approach.

Today's atmospheric data is generated swiftly as a result of the growth of IoT and sensor technologies and is available via data suppliers' RESTful APIs. However, sensor data mostly consists of live data streams including sensor observations, which are produced in a dispersed manner by several heterogeneous infrastructures, with little or no interoperability. RDF streams incorporating semantic data interoperability have arisen in last years and can be the foundation of intelligent semantic applications (e.g. semantic complex event processing). To enable semantic analysis of live atmospheric data streams, this article proposes a methodology for converting live data streams into Linked Data. The process leverages the most recent technologies for RML semantic mapping, ontology modeling, and Linked Data to extend the semantic usefulness of live atmospheric data, for example, by allowing for easy integration of atmospheric data streams with other live RDF streams.

With the active development of ICT and Internet technologies in climate research, individuals often need to gather different and disparate datasets and preprocess them in preparation for downstream data analysis in order to have a more full understanding of the challenges. This preparatory procedure is often lengthy due to the primary issue that data providers can-not ensure a homogeneous data format for data integration purposes. To overcome this problem, this study proposes enhancing existing relational climate data by layering a virtual knowledge graph on top of the original databases provided by various data vendors. The primary benefit of doing this is that data consumers are able to simply integrate climate data with other data sources using Linked Data principles, and climate data producers do not have to modify their data to conform to standard knowledge graph protocols.

The latest analytical models are becoming frequently used in meteorological science research. For instance, machine learning and deep learning models are being trained for weather forecasting. A solid machine learning model can give trustworthy findings that aid individuals in making weather-related decisions. However, the performance of analytical models is largely determined not only by the design of the model body but also by the input features. We address common issues of modern meteorological studies that take sensor data as the input for various analytical models. In contrast to the traditional practice of combining and preprocessing many fixed sensor data bulks to create an augmented dataset, we tunnel into remote knowledge graphs to fetch and augment the sensor data in a scalable way. As a consequence, we reduce the amount of time and storage space to preprocess diverse data in preparation for analytical models leveraging the high interoperability between knowledge graphs.

Peru Bhardwaj, John Kelleher, Luca Costabello, Declan O’Sullivan. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour.We study data poisoning attacks against KGE models for link prediction.These attacks craft adversarial additions or deletions at training time to cause model failure at test time.To select adversarial deletions, we propose to use the model-agnostic instance attribution methods from Interpretable Machine Learning, which identify the training instances that are most influential to a neural model's predictions on test instances.We use these influential triples as adversarial deletions.We further propose a heuristic method to replace one of the two entities in each influential triple to generate adversarial additions.Our experiments show that the proposed strategies outperform the state-ofart data poisoning attacks on KGE models and improve the MRR degradation due to the attacks by up to 62% over the baselines.

Abstract A very large number of historical manuscript collections are available in image formats and require extensive manual processing in order to search through them. So, we propose and build a search engine for automatically storing, indexing and efficiently searching the manuscript images. Firstly, a handwritten text recognition technique is used to convert the images into textual representations. In the next steps, we apply the named entity recognition and historical knowledge graph to build a semantic search model, which can understand the user’s intent in the query and the contextual meaning of concepts in documents, to return correctly the transcriptions and their corresponding images for users.

Recently ontologies have been exploited in a wide range of research areas for data modeling and data management. They greatly assists in defining the semantic model of the underlying data combined with domain knowledge. In this paper, we propose the Climate Analysis (CA) Ontology to model climate datasets used by remote sensing analysts. We use the data published by National Oceanic and Atmospheric Administration (NOAA) to further explore how ontology modeling can be used to facilitate the field of climatic data processing. The idea of this work is to convert relational climate data to the Resource Description Framework (RDF) data model, so that it can be stored in a graph database and easily accessed through the Web as Linked Data. Typically, this provides climate researchers, who are interested in datasets such as NOAA, with the potential of enriching and interlinking with other databases. As a result, our approach facilitates data integration and analysis of diverse climatic data sources and allows researchers to interrogate these sources directly on the Web using the standard SPARQL query language.

"Uplift" mapping execution applies a set of mapping definitions to transform non-RDF data sources to RDF. During the uplift mapping process, mapping definitions are iteratively refined until they conform with the user's expressed requirements. The W3C standard R2RML is one language which allows for specifying the mappings needed to generate RDF datasets from relational databases. Many approaches have been proposed to assess the quality of the generated RDF datasets, even though the root cause of several of those quality violations are found in mappings. In this paper, we present a framework for assessing and refining the quality of the definitions used to transform non-RDF data to RDF. This paper also provides an overview of an implementation of the proposed quality assessment framework for the R2RML mapping language, which uses the W3C standard Shapes Constraint Language (SHACL). We also provide a demonstration of the proposed framework and its implementation through a walkthrough use case.

Data processing is increasingly becoming the subject of various policies and regulations, such as the European General Data Protection Regulation (GDPR) that came into effect in May 2018. One important aspect of GDPR is informed consent, which captures one's permission for using one's personal information for specific data processing purposes. Organizations must demonstrate that they comply with these policies. The fines that come with non-compliance are of such importance that it has driven research in facilitating compliance verification. The state-of-the-art primarily focuses on, for instance, the analysis of prescriptive models and posthoc analysis on logs to check whether data processing is compliant to GDPR. We argue that GDPR compliance can be facilitated by ensuring datasets used in processing activities are compliant with consent from the very start. The problem addressed in this paper is how we can generate datasets that comply with given consent "just-in-time". We propose RDF and OWL ontologies to represent the consent that an organization has collected and its relationship with data processing purposes. We use this ontology to annotate schemas, allowing us to generate declarative mappings that transform (relational) data into RDF driven by the annotations. We furthermore demonstrate how we can create compliant datasets by altering the results of the mapping. The use of RDF and OWL allows us to implement the entire process in a declarative manner using SPARQL. We have integrated all components in a service that furthermore captures provenance information for each step, further contributing to the transparency that is needed towards facilitating compliance verification. We demonstrate the approach with a synthetic dataset simulating users (re-)giving, withdrawing, and rejecting their consent on data processing purposes of systems. In summary, it is argued that the approach facilitates transparency and compliance verification from the start, reducing the need for posthoc compliance analysis common in the state-of-the-art.

Consent is an important legal basis for the processing of personal data under the General Data Protection Regulation (GDPR), which is the current European data protection law. GPDR provides constraints and obligations on the validity of consent, and provides data subjects with the right to withdraw their consent at any time. Determining and demonstrating compliance to these obligations require information on how the consent was obtained, used, and changed over time. Existing work demonstrates feasibility of semantic web technologies in modelling information and determining compliance for GDPR. Although these address consent, they currently do not model all the information associated with it. In this paper, we address this by first presenting our analysis of information associated with consent under the GDPR. We then present GConsent, an OWL2-DL ontology for representation of consent and its associated information such as provenance. The paper presents the methodology used in the creation and validation of the ontology as well as an example use-case demonstrating its applicability. The ontology and this paper can be accessed online at https://w3id.org/GConsent .

In this paper, we argue that there is a gap to be bridged between the development and maintenance of services and the various internal and external policies that emerge and evolve outside of these systems. To bridge this gap, we propose a semantic model, i.e. ontology, for representing Data Flows and linking them with structured representations of the data that is processed (datasets, databases, queries, etc.). Data Flow Diagramming is a technique for capturing the various data and information flows between an information system and external stakeholders as well as within such a system. This technique is used in the analysis phase of information systems development and captures the inputs and outputs of various processes. Our model allows these data flows to be presented and linked with structured representations of the data that is to be used, consulted, processed, etc. We demonstrate that this model can facilitate compliance verification processes of (intelligent) systems by allowing these flows to be analyzed. Next to the ontology, which has been made available according to best practices in the field, we furthermore posit our contributions within the state of the art.

Social constructivism is grounded on the construction of information with a focus on collaborative learning through social interactions. However, it tends to ignore the human mental architecture, pillar of cognitivism. A characteristic of cognitivism is that instructional designs built upon it are generally explicit, contrarily to constructivism. This position paper proposes a novel learning task that is aimed at combining both the approaches through the use of trigger questions in a collaborative activity executed after a traditional delivery of instructions. To evaluate this new task, a metric of efficiency based upon a measure of mental workload and a measure of performance is proposed. The former measure is taken from Ergonomics, and two well know subjective self-reporting mental workload assessment techniques are envisioned. The latter measure is taken from an objective quantitative assessment of the performance of learners employing concept maps.

The General Data Protection Regulation (GDPR) is the new European data protection law whose compliance affects organisations in several aspects related to the use of consent and personal data. With emerging research and innovation in data management solutions claiming assistance with various provisions of the GDPR, the task of comparing the degree and scope of such solutions is a challenge without a way to consolidate them. With GDPR as a linked data resource, it is possible to link together information and approaches addressing specific articles and thereby compare them. Organisations can take advantage of this by linking queries and results directly to the relevant text, thereby making it possible to record and measure their solutions for compliance towards specific obligations. GDPR text extensions (GDPRtEXT) uses the European Legislation Identifier (ELI) ontology published by the European Publications Office for exposing the GDPR as linked data. The dataset is published using DCAT and includes an online webpage with HTML id attributes for each article and its subpoints. A SKOS vocabulary is provided that links concepts with the relevant text in GDPR. To demonstrate how related legislations can be linked to highlight changes between them for reusing existing approaches, we provide a mapping from Data Protection Directive (DPD), which was the previous data protection law, to GDPR showing the nature of changes between the two legislations. We also discuss in brief the existing corpora of research that can benefit from the adoption of this resource.

Data.geohive.ie aims to provide an authoritative service for serving Ireland?s national geospatial data as Linked Data. The service currently provides information on Irish administrative boundaries and the boundaries used for the Irish 2011 census. The service is designed to support two use cases: serving boundary data of geographic features at various level of detail and capturing the evolution of administrative boundaries. In this paper, we report on the development of the service and elaborate on some of the informed decisions concerned with the URI strategy and use of named graphs for the support of aforementioned use cases ? relating those with similar initiatives. While clear insights on how the data is being used are still being gathered, we provide examples of how and where this geospatial Linked Data dataset is used.

During the past few years, the analysis of data generated from Location-Based Social Networks (LBSNs) have aided in the identification of urban patterns, understanding activity behaviours in urban areas, as well as producing novel recommender systems that facilitate users’ choices. Recognizing crowd-mobility patterns in cities is very important for public safety, traffic managment, disaster management, and urban planning. In this article, we propose a framework for Recognizing the Crowd Mobility Patterns in Cities using LBSN data. Our proposed framework comprises four main components: data gathering, recurrent crowd-mobility patterns extraction, temporal functional regions detection, and visualization component. More specifically, we employ a novel approach based on Non-negative Matrix Factorization and Gaussian Kernel Density Estimation for extracting the recurrent crowd-mobility patterns in cities illustrating how crowd shifts from one area to another during each day across various time slots. Moreover, the framework employs a hierarchical clustering-based algorithm for identifying what we refer to as temporal functional regions by modeling functional areas taking into account temporal variation by means of check-ins’ categories. We build the framework using a spatial-temporal dataset crawled from Twitter for two entire years (2013 and 2014) for the area of Manhattan in New York City. We perform a detailed analysis of the extracted crowd patterns with an exploratory visualization showing that our proposed approach can identify clearly obvious mobility patterns that recur over time and location in the urban scenario. Using same time interval, we show that correlating the temporal functional regions with the recognized recurrent crowd-mobility patterns can yield to a deeper understanding of city dynamics and the motivation behind the crowd mobility. We are confident that our proposed framework not only can help in managing complex city environments and better allocation of resources based on the expected crowd mobility and temporal functional regions but also can have a direct implication on a variety of applications such as personalized recommender systems, anomalous event detection, disaster resilience management systems, and others.

The development of a city gradually forms different functional regions, such as residential districts and shopping areas. Discovering these functional regions in cities can enable new types of valuable applications that can benefit different end users: Urban planners can better identify the proximity of existing functional regions and hence, can contribute a better future planning for the cities. Tourists can differentiate scenic areas from other business and residential areas which will help in reducing effort for trip planning. Moreover, local people can better understand each part of their cities by finding areas with particular functionality. With the rise of Location-Based Social Networks (LBSNs) which attract lots of new users everyday with the potential of bridging the gap between the physical world and digital online social network services, we show in this paper that identifying functional regions taking into account temporal variations of geographic user activity has become possible and is more sensible when identifying functional regions. In this work, we propose a novel approach to modeling functional areas taking into account temporal variation by means of place categories. Our proposed approach compares between three clustering algorithms (Hierarchical, K-means, and Spectral) on areas and users of Manhattan borough in New York City using a dataset from one of the most vibrant LBSN, Foursquare. We demonstrate the impact of different temporal variations splits on the quality of the clustering algorithms comparing it to the default approach with no temporal variation. We believe that this research can not only yield a deeper understanding of a complex city but also can offer finer personalized recommendations based on regions' functionality that changes over space and time.

Typically tools that map non-RDF data into RDF format rely on the technology native to the source of the data when manipulation of data during the mapping is required. Depending on the data format, data manipulation can be performed using underlying technology, such as RDBMS for relational databases or XPath for XML. For CSV/Tabular data there is no such underlying technology, and instead transforming the source data into another format or pre/post-processing techniques are used. As part of this paper, we present a comparison framework for the state-of-the-art in converting CSV/Tabular data into RDF, where a key feature evaluated is transformation functions. We argue that existing approaches for transformation functions in such tools are complex - in number of steps and tools involved - and therefore not as traceable and transparent as one would like. We tackle these problems by defining a more generic, usable and amenable method to incorporate functions into uplift mapping languages, called FunUL. As proof of concept, we show an implementation of our method. Moreover, by using a real world Digital Humanities case study, we compare our approach with other approaches that we have identified to include transformation functions as part of the mapping for CSV/Tabular data.

Linked Open Data consists of a large set of structured data knowledge bases which have been linked together, typically using equivalence statements. These equivalences usually take the form of owl:sameAs statements linking individuals, but links between classes are far less common. Often, the lack of linking between classes is because the relationships cannot be described as elementary one to one equivalences. Instead, complex correspondences referencing multiple entities in logical combinations are often necessary if we want to describe how the classes in one ontology are related to classes in a second ontology. In this paper the authors introduce a novel Bayesian Restriction Class Correspondence Estimation (Bayes-ReCCE) algorithm, an extensional approach to detecting complex correspondences between classes. Bayes-ReCCE operates by analysing features of matched individuals in the knowledge bases, and uses Bayesian inference to search for complex correspondences between the classes these individuals belong to. Bayes-ReCCE is designed to be capable of providing meaningful results even when only small amounts of matched instances are available. They demonstrate this capability empirically, showing that the complex correspondences generated by Bayes-ReCCE have a median F1 score of over 0.75 when compared against a gold standard set of complex correspondences between Linked Open Data knowledge bases covering the geographical and cinema domains. In addition, the authors discuss how metadata produced by Bayes-ReCCE can be included in the correspondences to encourage reuse by allowing users to make more informed decisions on the meaning of the relationship described in the correspondences.

Monitoring the massive volume of data streaming from managed nodes in Telecommunication networks reacting in a timely manner is increasingly critical for modern Telecommunications Operations Support Systems (OSS). Given the large number and the varieties of the nodes in a telecoms network, the streaming monitoring data is naturally diverse and the volume is often at scales of multiple millions data points each second. These data are well modelled using formal syntaxes (e.g. Management Information Bases), making formal semantics and automated reasoning a viable solution for Telecom data modeling and correlation. This paper proposes an approach that will leverage recent developments in Semantic Reasoning and Big Data. The paper introduces how we propose to use RDF stream reasoning methods for real time event correlation, combined with MapReduce technologies in order to decentralize the large number of reasoning and correlation tasks that need to be undertaken in real time. The proposed approach is currently being implemented and will be evaluated using the diverse data types and volumes that are expected.

Ontology matching and mapping is concerned with discovering correspondences between two ontologies to create a mapping that enable applications to relate, interlink or integrate data. The construction of such mappings is not trivial as they are created to serve a purpose and result from collaboration between the different stakeholders. Current ontology-mapping metadata formats only capture a glimpse of the mapping construction process by focusing on the exchange of mappings and they provide some limited properties to facilitate reuse and discovery. For mapping governance to be possible, we argue that a suitable metadata model -- which will be presented in this paper -- needs to capture all aspects from the ontology mapping lifecycle: from the inception of a project to the execution of these mappings. This allows one to formulate queries that not only would facilitate the discovery and reuse, but also queries that allow one to govern the ontology mapping projects and render the construction processes more transparent and traceable.

Ontology mappings can be used to transform data in an existing ontology into a form that is more amenable to faceted browsing. We demonstrate the use of complex correspondences to enable more fine-grained browsing of a dataset describing people, places and events from World War One. The values of properties in this dataset contain many pieces of implicit information which has not been modelled in the original ontology. Complex correspondences are used to map the instances into an ontology with a richer hierarchy. Modifying the whole dataset is unfeasible in many applications, due to many factors e.g. the ownership of the data and the wish to continue using it in existing applications. Correspondences combined with the use of a reasoner to infer implicit information can improve the utility of the dataset without permanently modifying it. An evaluation to select a suitable reasoner performing such a task is conducted. The best performing reasoner is then applied in a faceted browser in order to explore the dataset enhanced with the mappings. In addition, the faceted browser is flexible and can be used with custom data and mappings.

In management systems, the focus is moving from managing the Quality of Service (QoS) of end-user service sessions to managing their Quality of Experience (QOE). To do this, the user experience and context of those sessions much be modelled, analysed, and optimised to ensure end user experience matches expectations as closely as possible. In this thesis, we introduce the Aesop approach, which uses semantic-based techniques to autonomically optimize end-user service delivery. The Aesop knowledge base models the end-user service management domain in a manner that is aware of the temporal properties of concepts. The autonomic Aesop Engine runs efficient semantic algorithms that implement the MAPE functions using those temporal properties to operate on small partitioned subsets of the knowledge base. The effectiveness and performance of the Aesop approach was evaluated on a HAN test bed. A significant improvement was observed on the compliance levels of high priority sessions in all experimental scenarios, with compliance levels more than doubled in some cases. A case study demonstrated that Aesop was also applicable in the Mobile Broadband Access domain.

In order to evaluate the efficacy of new research approaches as well as existing systems for management of end user service sessions, it is necessary to set up controlled sessions in a realistic, measurable, and repeatable manner. While service delivery clients support automation of end user sessions and expose mechanisms for retrieval of end user service experience and context, there is no single manner for collection of service experience and context measurements, or for collation and delivery of those measurements to a management system. The Service Experience and Context COllection (SECCO) framework was developed to fulfil three roles. Firstly, it allows end user service sessions to be run in a controlled manner. Secondly, it records end user service experience and context for running sessions. Thirdly, it compiles terminal reports for end user sessions and forwards them to Aesop for processing. This paper outlines the design and implementation of SECCO and describes three separate cases where SECCO was applied.

This paper describes a semi-automated process, framework and tools for harvesting, assessing, improving and maintaining high-quality linked-data. The framework, known as DaCura1, provides dataset curators, who may not be knowledge engineers, with tools to collect and curate evolving linked data datasets that maintain quality over time. The framework encompasses a novel process, workflow and architecture. A working implementation has been produced and applied firstly to the publication of an existing social-sciences dataset, then to the harvesting and curation of a related dataset from an unstructured data-source. The framework's performance is evaluated using data quality measures that have been developed to measure existing published datasets. An analysis of the framework against these dimensions demonstrates that it addresses a broad range of real-world data quality concerns. Experimental results quantify the impact of the DaCura process and tools on data quality through an assessment framework and methodology which combines automated and human data quality controls.

The need to autonomically optimize end-user service experience in near real time has been identified in the literature in recent years. Management systems that monitor end-user service session context exist but approaches that estimate end-user service experience from session context do not analyze the compliance of that experience with user expectations. Approaches that optimize end-user service delivery are not applicable to arbitrary services; they either optimize specific service types or use general mechanisms that do not consider service experience. The lack of a holistic model for end-user service management is a barrier to autonomic end-user service optimization. This paper presents Aesop, an approach addressing autonomic optimization of end-user service delivery using semantic-based techniques. Its knowledge base uses the End-User Service Analysis and Optimization ontology, which models the end-user service management domain and partitions knowledge that varies over time for efficient access. The Aesop Engine executes an autonomic loop in near real time, which runs semantic algorithms to monitor sessions, analyze their compliance with expectations, and plan and execute optimizations on service delivery networks. The algorithms are efficient because they operate on small partitioned subsets of the Knowledge Base held as separate self-contained models at run time. An Aesop implementation was evaluated on a home area network test bed where compliance of service sessions with expectations when optimization was active was compared with compliance of an identical set of sessions when optimization was inactive. Significant improvements were observed on compliance levels of high priority sessions in all experimental scenarios, with compliance levels more than doubled in some cases.

The data required for automatic optimization of user services usually exists in current systems, but that data is not modelled or linked in a way that facilitates automation. Knowledge engineering is a promising approach for managing the disparate communication service quality management information data sets and the links across those data sets. Once a knowledge base is in place, semantic techniques can be used to analyse and suggest optimizations to service quality. This paper describes our work in building, populating and evaluating a knowledge base for an IPTV service in Home Area Networks. Population of the knowledge base was implemented using terminal reports. The characteristics of the approach were evaluated through experimentation and the evaluation results are presented in this paper.

When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult due to data set schema heterogeneity. Prior work has focused on inter-domain class equivalence. However it is not always possible to find an equivalent class in both schemas. For example, when instances are modeled as classes in one domain (e.g. router type) but as the attribute values of a single class in the other domain (e.g. router interface). This paper investigates whether when classifying instances in one data set against a second schema, it may be more useful to use some attribute (or attribute group) other than the original class type, to perform this classification. A machine-learning based classification approach to appropriate attribute selection is presented and its operation is evaluated using two large data-sets available on the web as Linked Data. The classification problem is compounded by the less formal semantics of Linked Data when compared to full ontologies but this also highlights the strength of our approach to dealing with noisy or under-specified data-sets and schemas. The experimental results show that our attribute selection approach is capable of discovering appropriate mappings for cases where the correspondence is conditioned on one attribute and that information gain provides a suitable scoring function for selection of correspondence patterns to describe these complex attribute-based mappings.

The heterogeneity of ontologies is a major obstacle to the promised interoperability of knowledge in the Semantic Web. Ontology mappings can help to mitigate the effects by specifying the correspondences between related ontologies. However, the creation of mappings is still complex and time-consuming and therefore it is appealing to discover existing mappings and to reuse them. For reuse, it is essential to understand how a mapping was created and applied. Thus meta-data documenting the lifecycle of an ontology mapping is essential to facilitate management and reuse. Currently the mapping lifecycle is only fragmentary documented and in general the reuse of ontology mappings is insufficiently supported by applications and formats. This paper addresses the question to what extent a semantically expressive ontology-based meta-data model can aid in the discovery, management and reuse of ontology mappings. In particular, experimental results collected from a use-case study on mapping discovery are presented. Based on findings derived, it is shown that semantic rich and effective meta-data describing the ontology mapping lifecycle are crucial for management of ontology mappings. Finally, to address the identified challenges, a framework for management of ontology mappings is outlined.

This paper presents an architecture which allows consumers to securely share the services available in their home networks with remote third parties. It is implemented as a software service which can be installed on a home-gateway device. The implementation supports dynamic sharing of UPnP-compliant devices and multimedia content and enables complex service compositions across federated homes through a secure Federal Relationship Manager (FRM) service. We have extended UPnP to use XMPP as the underlying messaging protocol to maintain up-to-date state information about the availability of devices and content to federated third parties. The architecture, implementation and performance evaluations are described, demonstrating the potential for this technology to provide consumers with a mechanism for federating home services. This mechanism is sufficiently powerful to satisfy fine-grained sharing constraints through a user interface that is sufficiently simple for average consumers to master.

Managing a heterogeneous “outer edge” network is complex and error prone. It is typically performed by non-technical users. Effective HAN configuration may be hampered by a poor understanding of HAN service requirements. A challenge is to deploy and maintain meaningful and error-free heterogeneous HAN configurations. This paper explores an integrated solution to address the following requirements: managed capability sharing, usability, and security. A prototype HAN gateway architecture that builds upon explicit user-centric semantics, and enables autonomic management of shared UPnP services with appropriate access controls, is outlined.

Integration of disparate information resources has long been a significant research topic. Semantic approaches can help by allowing expression of concepts divorced from syntax and allowing rich, structured meta-data to be published in a form that is amenable to machine processing, reasoning and inter-domain concept mapping. However the creation of mappings, especially in a dynamic federations of autonomous entities, is time-consuming and vulnerable to brittleness and high maintenance costs due to change at many levels in the system. In this paper we propose an approach to managing change and maximizing mapping reuse by building explicit models of the federal relationship context of mapping deployment. These descriptions enable automated support for mapping reuse suggestions and ease discovery of relevant mappings due to changes at the federation, peer domain, shared capability or local model levels.

Policy Engineering is the process of authoring IT management policies, detecting and resolving policy conflicts and revising existing policies to accommodate changing IT resources, business goals and business processes. Policy authoring is often followed by policy enforcement where the actions specified by subjects are performed on targets (resources). In this paper, we study the use of semantically enhanced techniques, such as ontologies, to model resources and their corresponding actions, coupled with a mechanism that can accommodate frequent organizational change, to model policy subjects. For the modeling of policy subjects, the rule-based Community-based Policy management will be used. This integration falls into the category of combining Description Logics (DL) and Logic Programs (LP). We aim to study this integration primarily from the scope of overall system expressivity, but also from the scope of minimizing the cognitive load perceived by policy authors. Such an evaluation can help determine shortfalls in the design of the software system or of the policy model used. To study the balance in modeling with DL and LP techniques, the encoding of part of the Trinity College Dublin statutes will be performed, which is a sufficiently complex real-world example.

Ontology-based information integration systems have been proposed to solve the problem of combining data residing at autonomous and heterogeneous sources. For such ontology based solutions to succeed in an industrial context, the ontologies and mappings need to be managed to allow them to evolve as the data sources evolve. This paper describes a dependency modeling approach to the management of mappings in the integration system. A dependency model and tool have been prototyped within the context of a challenging logistics based industrial systems integration problem.

While the polling or request/response paradigm adopted by many network and systems management approaches form the backbone of modern monitoring and management systems, the most important and interesting events, faults, alerts and log messages arrive at the management agent in a push-based asynchronous manner. However, in the management infrastructure itself, at the point where events are initially processed and matched to subscribers, there have been few attempts to identify relationships or dependencies between events. This means that most of this burden is placed on the management application, or indeed the managers themselves. This research investigates enhancing the expressiveness of a knowledge-based networking middleware with the addition of three temporal operators to be used in subscriptions to select matching events. A prototype design is presented and a number of implementations are compared. The approach is also motivated using two scenarios for temporal correlation of warnings and faults in managed networks. The effect on the scalability of the extended knowledge-based network system is also evaluated.

With ontologies increasing in number and becoming more common place, there is an ever increasing need for tools and techniques to cope with diversity and heterogeneity. We believe that the interfaces to ontology management tools will need to be engineered to allow ordinary non technical people to be able to use them effectively. This paper reports upon a mapping process using the ‘tagging’ paradigm as a means of ontology mapping to support ordinary people.

Despite the significant research over the last ten years, commercial ubiquitous computing environments and pervasive applications remain thin on the ground. This paper looks at the explosion in application creativity on the internet in recent years – the so-called ‘web 2.0’ – in order to identify t

Increasingly there is a demand for more scalable fault management schemes to cope with the ever increasing growth and complexity of modern networks. Current distributed fault correlation schemes typically adopt rigid topologies, which require great effort in topology configuration. In this paper, we introduce a distributed fault correlation scheme which addresses this problem by using semantic-based publish/subscribe middleware. This provides loose coupling, robustness and scalability properties for the scheme.

The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding explicitly to all of the potential sources of that knowledge. Though event-based publish-subscribe models allow efficient distribution of knowledge where the event types are known globally, dynamic service chains, ad hoc networks and pervasive computing application all introduce a more fluid and heterogeneous range of context knowledge. This requires some runtime translation of knowledge between sources and sinks of network context. This paper builds on existing mapping techniques that use ontological forms of existing management information models to examine the extent to which these can be employed for runtime semantic interoperability for network knowledge. It presents results in developing a management knowledge delivery framework based on existing models and platforms, but which offers a more decentralized knowledge exchange mechanism.

When deploying collaborative applications such as Instant Messaging in ubiquitous computing environments significant enhancements can be afforded by offering additional context information, such as location information. However, such environments exert key challenges such as increased diversity of ownership and ad hoc, intermittent network connectivity that suits more decentralized computing architectures. This paper examines how a migration to a more decentralized collaborative architecture can be achieved together with a decentralization of the management of collaborative activities.

Currently a wide range of different adaptive and intelligent system solutions are being proposed for use in self-managing or autonomic networks. However, there are few means by which such proposals can be compared. This paper proposes that a benchmark be developed for autonomic systems so that progress in this field can be more systematically evaluated. Our approach assumes that autonomic systems make use of and thus expose a knowledge based representation of the service they offer, the context they react to and the governance to which they are subject. This position paper focuses on some of the issues that arise when formulating a benchmark for autonomic communications and is intended to form the basis for further discussion in the area.

The commonly articulated vision of pervasive computing represents a huge increase in the number of independently developed components that must interoperate and in the level of autonomy they must demonstrate. This motivates a shift to exchanging interoperability knowledge between components at runtime coupled with the ability of components to adapt themselves dynamically to the requirements of users moving between spaces and tasks. The confluence of the service-oriented techniques and ontology-based semantics, as semantic Web services, offers such dynamic adaptivity through knowledge-based service composition. We aim to establish a conceptual architecture for pervasive computing that integrates semantic service composition and policy-based management in providing a collective behavior that adapts to the user's changing needs, but which conforms to the goals of those responsible for the resources used by those services.

CORBA distributed object technology empowers the Java applet with standards-based connectivity to the world of information and computing services. Introducing CORBA to the Java environment means that applets are no longer restricted to simple interaction with the user but are instead capable of taking part in complex interactions with backend services. With CORBA, Java applets transcend the limitations of simple Web browser technology-CORBA-compliant Java objects become the basis for the provision of Internet and Interactive Multimedia Services on a world-scale. This session looks in detail at how CORBA and traditional WWW technology are merged to provide a new application infrastructure for the Web.

There are many driving forces which have compelled telecommunication operators and vendors to seek new solutions in telecommunications management. Increased competition has led to an increased focus on how traditional operators can do their business better through the use of integrated service and network management systems. Moreover, there is a need to integrate legacy applications and new applications, all of which could be written in different languages, perhaps running on different platforms, and almost certainly distributed over a network. CORBA has been recognised as a key technology solution. Based on the case studies performed for the ACTS project PROSPECT, this paper discusses the advantages and disadvantages of TMN (Telecommunications Management Network) and CORBA regarding network and service management, and proposes a phased approach for TMN-CORBA co-existence in the telecommunications management arena based on the Management Systems Framework of the Network Management Forum. Moreover, it presents some initial ideas concerning required basic management functionality that serve as a background for the CORBA/TMN gateway functionality to be implemented within the PROSPECT trial network.