Achieving semantic interoperability across heterogeneous experimental data systems remains a major barrier to data-driven scientific discovery. The Analytical Information Markup Language (AnIML), a flexible XML-based standard for analytical chemistry and biology, is increasingly used in industrial R&D labs for managing and exchanging experimental data. However, the expressivity of the XML schema permits divergent interpretations across stakeholders, introducing inconsistencies that undermine the interoperability the AnIML schema was designed to support. In this paper, we present the AnIML Ontology , an OWL 2 ontology that formalises the semantics of AnIML and aligns it with the Allotrope Data Format to support future cross-system and cross-lab interoperability. The ontology was developed using an expert-in-the-loop approach combining LLM-assisted requirement elicitation with collaborative ontology engineering. We validate the ontology through a multi-layered approach: data-driven transformation of real-world AnIML files into knowledge graphs, competency question verification via SPARQL, and a novel validation protocol based on adversarial negative competency questions mapped to established ontological anti-patterns and enforced via SHACL constraints.

Ontology-based question generation is an important application of semantic-aware systems that enables the creation of large question banks for diverse learning environments. The effectiveness of these systems, both in terms of the calibre and cognitive difficulty of the resulting questions, depends heavily on the quality and modelling approach of the underlying ontologies, making it crucial to assess their fitness for this task. To date, there has been no comprehensive investigation into the specific ontology aspects or characteristics that affect the question generation process. Therefore, this paper proposes a set of requirements and task-specific metrics for evaluating the fitness of ontologies for question generation tasks in pedagogical settings. Using the ROMEO methodology (a structured framework used for identifying task-specific metrics), a set of evaluation metrics have been derived from an expert assessment of questions generated by a question generation model. To validate the proposed metrics, we apply them to a set of ontologies previously used in question generation to illustrate how the metric scores align with and complement findings reported in earlier studies. The analysis confirms that ontology characteristics significantly impact the effectiveness of question generation, with different ontologies exhibiting varying performance levels. This highlights the importance of assessing ontology quality with respect to Automatic Question Generation (AQG) tasks.

This paper presents a novel approach to ontology-based automatic question generation (AQG), which aims to facilitate the creation of ‘complex’ educational questions, i.e. questions requiring larger knowledge cover and higher cognitive/reasoning processes. We leverage on the notion of Query Graphs, a graph-like structure that can represent natural language queries through appropriate mappings, previously used for semantic parsing. We propose the use of Query Graphs as a formalism for representing templates that incorporate multiple ontology-based constraints during question generation, which can then guide the selection of relevant ontology elements and properties. By incorporating these constraints, our approach elevates the level of reasoning required to answer the questions, therefore overcoming the limitations of existing methodologies. We evaluate the approach by modelling templates for AQG and comparing the resulting questions with existing state-of-the-art methods, on performance based on complexity levels. The empirical evaluation demonstrates that our model is indeed able to generate questions that demand higher cognitive skills.

The study of autonomous agents has a long history in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies, and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies, and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this article motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities.

Within characteristic function games, agents have the option of joining one of many different coalitions, based on the utility value of each candidate coalition. However, determining this utility value can be computationally complex since the number of coalitions increases exponentially with the number of agents available. Various approaches have been proposed that mediate this problem by distributing the computational load so that each agent calculates only a subset of coalition values. However, current approaches are either highly inefficient due to redundant calculations, or make the benevolence assumption (i.e. are not suitable for adversarial environments). We introduce DCG, a novel algorithm that distributes the calculations of coalition utility values across a community of agents, such that: (i) no inter-agent communication is required; (ii) the coalition value calculations are (approximately) equally partitioned into shares, one for each agent; (iii) the utility value is calculated only once for each coalition, thus redundant calculations are eliminated; (iv) there is an equal number of operations for agents with equal sized shares; and (v) an agent is only allocated those coalitions in which it is a potential member. The DCG algorithm is presented and illustrated by means of an example. We formally prove that our approach allocates all of the coalitions to the agents, and that each coalition is assigned once and only once.

Since the seminal work of Dung, Argumentation Frameworks have been shown to find solutions to n-person cooperative games. In multi-agent systems, decentralised methods for multi-agent system coalition structure generation have been proposed. This paper offers the first dialogue game that utilises argumentation frameworks to find a coalition structure and a payoff vector in a decentralised manner. The payoff vector found is in the core set of stable solutions if the core is non-empty. This dialogue game also puts restrictions on the payoff vectors that can be proposed so that the most unfair ones are discarded. Lastly an algorithm is described that allows the agents to find out if the core is empty.

In this paper, we develop a novel algorithm that allows service consumers to execute business processes (or workflows) of interdependent services in a dependable manner within tight time-constraints. In particular, we consider large interorganizational service-oriented systems, where services are offered by external organizations that demand financial remuneration and where their use has to be negotiated in advance using explicit service-level agreements (as is common in Grids and cloud computing). Here, different providers often offer the same type of service at varying levels of quality and price. Furthermore, some providers may be less trustworthy than others, possibly failing to meet their agreements. To control this unreliability and ensure end-to-end dependability while maximizing the profit obtained from completing a business process, our algorithm automatically selects the most suitable providers. Moreover, unlike existing work, it reasons about the dependability properties of a workflow, and it controls these by using service redundancy for critical tasks and by planning for contingencies. Finally, our algorithm reserves services for only parts of its workflow at any time, in order to retain flexibility when failures occur. We show empirically that our algorithm consistently outperforms existing approaches, achieving up to a 35-fold increase in profit and successfully completing most workflows, even when the majority of providers fail.

Ontology modularization has received growing interest from the research community lately, since it supports tasks such as ontology design/reuse and knowledge selection and integration. Most of the research efforts have concentrated on approaches to extract modules, or generate partitions from an ontology. However these approaches are influenced by different definitions of ontology modularization and thus tend to vary w.r.t. the concepts and properties in the ontology that should define the module, and on the characteristics that modules should exhibit, which often depend on the task for which the modularization process is performed. This diversity of approaches makes the comparative evaluation of the output of different modularization processes hard to perform. In this paper, we propose an entropy inspired measure for modularization, Integrated Ontology Entropy, that approximates the information content of modules, and hence provides a profile for the module generated. This measure is independent of the modularization technique used, and is calculated as a function of the number of edges connecting the named concepts in the ontology, when a graph representation of the ontology is utilized. In the paper we apply this measure to different modularization techniques and we empirically show how the measure captures different characteristics of modules, such as the degree of redundancy and the level of connectedness.

Web services promise to revolutionize the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable metadata, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures.

Semantic Web services have been one of the most significant research areas within the semantic Web vision, and have been recognized as a promising technology that exhibits huge commercial potential. Current semantic Web service research focuses on defining models and languages for the semantic markup of all relevant aspects of services, which are accessible through a Web service interface. The Web service modelling ontology (WSMO) is one of the most significant semantic Web service framework proposed to date. To support the standardization and tool support of WSMO, a formal semantics of the language is highly desirable. As there are a few variants of WSMO and it is still under development, the semantics of WSMO needs to be formally defined to facilitate easy reuse and future development. In this paper, we present a formal object-Z semantics of WSMO. Different aspects of the language have been precisely defined within one unified framework. This model provides a formal unambiguous specification, which can be used to develop tools and facilitate future development.

In this paper we therefore propose a reformulation of the entropy metric to evaluate the amount of information carried by both the ontology structure, and also by the language elements (i.e. the semantics associated with the edges in the ontological graph). To evaluate this approach, the reformulated metric is empirically compared to Calemt & Daemi's original entropy metric, for a variety of different sized modules. The results suggest that not only can entropy differentiate between structurally different modules of the same size, but that our improved entropy metric provides a finer grain differentiation than the original entropy metric.

Changes in an ontology may have a disruptive impact on any system using it. This impact may depend on structural changes such as introduction or removal of concept definitions, or it may be related to a change in the expected performance of the reasoning tasks. As the number of systems using ontologies is expected to increase, and given the open nature of the semantic Web, introduction of new ontologies and modifications to existing ones are to be expected. Dynamically handling such changes, without requiring human intervention, becomes crucial. This paper presents a framework that isolates groups of related axioms in an OWL ontology, so that a change in one or more axioms can be automatically localised to a part of the ontology.

Semantic Web services, one of the most significant research areas within the semantic Web vision, has attracted increasing attention from both the research community and industry. The Web service modelling ontology (WSMO) has recently been proposed as an enabling framework for the total/partial automation of the tasks (e.g., discovery, selection, composition, mediation, execution, monitoring, etc.) involved in both intra- and inter-enterprise integration of Web services. To support the standardization and tool support of WSMO, a formal semantics of the language is highly desirable. As there are a few variants of WSMO and it is still under development, the semantics of WSMO needs to be formally defined to facilitate easy reuse and future development. In this paper, we present a formal object-Z semantics of WSMO. Different aspects of the language have been precisely defined within one unified framework. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in this paper, can be used to identify and eliminate errors presented in existing documentation.

To support standardization and tool support of OWL-S, a formal semantics of the language is highly desirable. In this paper, we present a formal Object-Z semantics of OWL-S. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in the paper, can be used to identify and eliminate errors in the current documentation.

This article introduces the special issue of ACM Transactions on Autonomous and Adaptive Systems devoted to research papers arising from the three Technical Forum Group meetings held in 2004 and 2005 that were organized and sponsored by the European FP6 Coordination Action AgentLink III.

As the Web services and grid community adopt semantic Web technology, we observe a shift towards higher-level workflow composition and service discovery practices. While this provides excellent functionality to non-expert users, more sophisticated middleware is required to hide the details of service invocation and service integration. An investigation of a common bioinformatics use case reveals that the execution of high-level workflow designs requires additional processing to harmonise syntactically incompatible service interfaces. In this paper, we present an architecture to support the automatic reconciliation of data formats in such Web service workflows. The mediation of data is driven by ontologies that encapsulate the information contained in heterogeneous data structures supplying a common, conceptual data representation. Data conversion is carried out by a configurable mediator component, consuming mappings between XML schemas and OWL ontologies. We describe our system and give examples of our mapping language against the background of a bioinformatics use case

The main goal of the workshop is to discuss approaches that use a mobile device (e.g. mobile phone, smartphone, PDA) for interactions with objects in the real world. Relevant topics include (but are not limited to) mobile interaction with the real world; mobile devices as user interfaces for terminals and vending machines; and Frameworks, middleware and APIs for the development of applications that take mobile interactions with the real world into account. The workshop combines technical presentations with the presentation of prototypes and focussed discussions to drive interaction between participants.

While the grid and Web services have helped us support heterogeneous resource access through the use of service oriented architectures, they have not addressed the issue of heterogeneous data representation. Since service providers often describe their service interfaces using different data models than those assumed by the client, it is common for additional processing to be required to compensate for the mismatch in data formats. By utilising technology from the semantic Web, we are able to augment existing Web service systems with middleware to automatically perform data harmonisation when a syntactic mismatch occurs. To achieve this, we have developed a mapping language which can be used to annotate XML data structures with OWL concepts and properties, a mapping language engine to implement this language, and a dynamic web service invocation component to execute Web services.

The UK e-Science programme is relying on the evolution of the paper lab book into a pervasive data gathering lab system. To date take up of existing commercial or research lab book replacement systems has not been great. In this paper, we reconsider both the role of the lab book in the experimental cycle, as well as its affective and experiential properties as an artefact, in order to design an e-Science lab book that will be acceptable to the scientists who will use it. To this end we combined and extended existing design analysis models in order to assess the artefact functionally and experientially. We present the approach we developed, the prototype we designed based on our analysis, and the results of the formative study we performed of the artefact in real use. We show that our design elicitation method strongly contributed to the success of our prototype's take up.

Describes the interactions between task characteristics and human agent interfaces in a team rendezvous route-planning task. The agents include an interface agent and two different task agents that perform similar tasks. The MokSAF (Mock modular Semi-Automated Forces) interface agent links an artificial intelligence (AI) route planning agent to a geographic information system (GIS). Through this agent, the user specifies a start and an end point, and describes the composition and characteristics of a military platoon. Two aided conditions and one non-aided condition were examined. In the first aided condition, an autonomous route-planning agent (RPA) determines a minimum-cost path between the specified end points. The user is allowed to define additional "intangible" constraints that describe situational or social information that should be considered when determining the route. In the second aided condition, a different agent, a cooperative RPA, uses the same knowledge of the terrain and cost functions available to the autonomous RPA, but restricts its search to paths within regions drawn by the user. In the unaided condition (the naive RPA), the user draws the route manually, then submits it to be tested against the terrain and cost functions for feasibility. Both aided conditions are superior to the control but differ in their relative effectiveness by scenario. In this paper, we examine the varieties of challenges faced by commanders in two scenarios and relate them to the differential effectiveness of the agents.

Interface agents are being developed to assist users with a variety of tasks. To perform effectively, such agents need knowledge of user preferences. An agent architecture has been developed which observes a user performing tasks, and identifies features which can be used as training data by a learning algorithm. Using the learned profile, an agent can give advice to the user on dealing with new situations. The architecture has been applied to two different information filtering domains: classifying incoming mail messages (Magi) and identifying interesting USENet news articles (UNA). This paper describes the architecture and examines the results of experimentation with different learning algorithms and different feature extraction strategies within these domains.