Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms, or explanation methods, have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. In this paper, we introduce a method, including a dataset (FrenchRoyalty-200k), to benchmark explanation methods on the task of link prediction on KGs, when there are multiple explanations to consider. We conduct a user experiment, where users score each possible ground truth explanation based on their understanding of the explanation. We propose the use of several scoring metrics, using relevance weights derived from user scores for each predicted explanation. Lastly, we benchmark this dataset on state-of-the-art explanation methods for link prediction using the proposed scoring metrics.

Most service composition approaches rely on top-down decomposition of a problem and AI-style planning to assemble service components into a meaningful whole, impeding reuse and flexibility. In this article, we propose an approach that starts from declarative knowledge about the semantics of individual service components and algorithmically constructs a full-blown service orchestration process that supports sequence, choice, and parallelism. The output of our algorithm can be mapped directly into a number of service orchestration languages such as OWL-S and BPEL. The approach consists of two steps. First, semantic links specifying data dependencies among the services are derived and organized in a flexible network. Second, based on a user request indicating the desired outcomes from the composition, an executable composition is constructed from the network that satisfies the dependencies. The approach is unique in producing complex compositions out of semantic links between services in a flexible way. It also allows reusing knowledge about semantic dependencies in the network to generate new compositions through new requests and modification of services at runtime. The approach has been implemented in a prototype that outperforms related composition prototypes in experiments.

Data captured in OWL ontologies is generally considered to be more prone to changes than the schema in many situations. Such changes often necessitate consistency checking over the resulting ontologies in order to maintain coherent knowledge, specifically in dynamic settings. In this paper, we present an approach to check the consistency over an evolving ontology resulting from data insertions and deletions, given by some expressive underlying Description Logic dialect. The approach, assuming an initially consistent ontology, works by syntactically identifying "relevant" and representative parts of the data for the given updates, i.e., the part that may contribute to subsequent consistency checking. Our approach has demonstrated its efficacy in checking consistency over large and real-world ontologies and outperforms existing approaches in several circumstances.

The current proliferation of software services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional services, yet their direct application to software services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune software services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.

Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides technologies to annotate and match services' descriptions with users' features, interests and preferences, thus allowing for more efficient access to services and more generally information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? To this end, we exploit Description Logics reasoning through semantic matching to (i) identify useful parts of a user profile that satisfy services requirements (i.e., input parameters) and (ii) compute the description required by a service to be executed but not provided by the user profile. Our approach, part of the EC-funded project SOA4All, was evaluated on its applicability in real world scenarios with end-users.

Ranking and optimization of web service compositions represent challenging areas of research with significant implications for the realization of the “Web of Services” vision. “Semantic web services” use formal semantic descriptions of web service functionality and interface to enable automated reasoning over web service compositions. To judge the quality of the overall composition, for example, we can start by calculating the semantic similarities between outputs and inputs of connected constituent services, and aggregate these values into a measure of semantic quality for the composition. This paper takes a specific interest in combining semantic and nonfunctional criteria such as quality of service (QoS) to evaluate quality in web services composition. It proposes a novel and extensible model balancing the new dimension of semantic quality (as a functional quality metric) with a QoS metric, and using them together as ranking and optimization criteria. It also demonstrates the utility of Genetic Algorithms to allow optimization within the context of a large number of services foreseen by the “Web of Services” vision. We test the performance of the overall approach using a set of simulation experiments, and discuss its advantages and weaknesses.

Automated composition of Web services or the process of forming new value-added Web services is one of the most promising challenges facing the Semantic Web today. Semantics enables Web service to describe capabilities together with their processes, hence one of the key elements for the automated composition of Web services. In this paper, we focus on the functional level of Web services i.e. services are described according to some input, output parameters semantically enhanced by concepts in a domain ontology. Web service composition is then viewed as a composition of semantic links wherein the latter links refer to semantic matchmaking between Web service parameters (i.e. outputs and inputs) in order to model their connection and interaction. The key idea is that the matchmaking enables, at run time, finding semantic compatibilities among independently defined Web service descriptions. By considering such a level of composition, a formal model to perform the automated composition of Web services i.e. Semantic Link Matrix, is introduced. The latter model is required as a starting point to apply problem-solving techniques such as regression (or progression)-based search for Web service composition. The model supports a semantic context in order to find correct, complete, consistent and robust plans as solutions. In this paper, an innovative and formal model for an AI (Artificial Intelligence) planning-oriented composition is presented. Our system is implemented and interacting with Web services which are dedicated to Telecom scenarios. The preliminary evaluation results showed high efficiency and effectiveness of the proposed approach.

We claim that a key feature for correct and effective web service composition, and one that has largely been ignored,is the joint consideration of (semantic) causal links and causal laws, respectively in area of Description Logics (DL) and AI planning. In this paper we propose a means of specifying both causal links and laws into web service composition by integrating DL reasoning and Situation Calculus. To this end an augmented and adapted version of the logic programming language Golog i.e., sclGolog is presented as a natural formalism not only for reasoning about the latter links and laws, but also for automatically composing services. sclGolog operates as an offline interpreter that supports n-ary sensing actions to retrieve conditional compositions of services. Lastly sclGolog has been implemented and tested in the context of Telecommunication scenarios.