Object State Classification (OSC) is a critical task in computer vision, enabling systems to understand the functional state of objects. This work proposes a novel end-to-end architecture for OSC that leverages the inherent relationship between object classification and state recognition. Our approach first classifies the object and then uses object-specific attention mechanisms to focus on relevant features for state classification. This two-stage design allows the model to effectively capture object-state dependencies while maintaining modularity and flexibility. We conduct an extensive ablation study to analyze the impact of key parameters, such as attention mechanisms and loss weighting, and evaluate our method against three baselines across four benchmark datasets. Experimental results demonstrate that our approach outperforms competing methods by a significant margin, achieving state-of-the-art performance.

We investigate the problem of Object State Classification (OSC) in the context of zero-shot learning. Specifically, we propose the first method for Zero-shot Object-agnostic State Classification (OaSC) that, given an image, infers the state of a single object without relying on the knowledge or the estimation of the object class. In that direction, we capitalize on Knowledge Graphs (KGs) for structuring and organizing external knowledge, which, in combination with visual information, enable effective inference of the states of objects that have not been encountered in the training set. Having this unique property, a significant strength of our method is that it can handle an Open Set of object classes. We investigate the performance of OaSC in various datasets and settings, against several hypotheses and in comparison with state-of-the-art approaches for object attribute classification. OaSC outperforms these methods significantly across all benchmarks.1

Property Graphs are a versatile and expressive data model that has gained widespread adoption due to their flexibility in supporting labeled and attributed nodes and edges. They are well-established in research communities and are becoming widespread in companies and organizations across various sectors. They have been boosted by a fervent ISO/IEC standardization activity, leading to dedicated query and schema languages. While the current standards are still evolving, opportunities remain to enrich them with features such as composability. The plethora of existing query languages reflects a rich and diverse ecosystem, which ongoing unification efforts aim to align. This tutorial aims to deepen the understanding of Property Graph standards by showcasing their strengths, highlighting recent unification efforts, clarifying the central role of schema constraints, and exploring the rich landscape of research and industrial opportunities shaping the future of graph data management.

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is a critical and long-held aim for the Artificial Intelligence community. Training systems with relevant data is a typical approach; however, finding the data required is not always possible, especially when much of this knowledge is commonsense. In this paper, we present a comparison of topology-based and semantics-based methods for extracting information about object-action and object-state association relations from knowledge graphs, such as ConceptNet, WordNet, ATOMIC, YAGO, WebChild and DBpedia. Moreover, we propose a novel method for extracting information about object-action and object-state associations from knowledge graphs. Our method is composed of a set of techniques for locating, enriching, evaluating, cleaning and exposing knowledge from such resources, relying on semantic similarity methods. Some important aspects of our method are the flexibility in deciding how to deal with the noise that exists in the data, and the capability to determine the importance of a path through training, rather than through manual annotation.

A representational limitation of current argumentation frameworks is their inability to deal with sets of entities and their properties, for example to express that an argument is applicable for a specific set of entities that have a certain property and not applicable for all the others. In order to address this limitation, we recently introduced Abstract Argumentation Frameworks with Domain Assignments (AAFDs), which extend Abstract Argumentation Frameworks (AAFs) by assigning to each argument a domain of application, i.e., a set of entities for which the argument is believed to apply. We provided formal definitions of AAFDs and their semantics, showed with examples how this model can support various features of commonsense and non-monotonic reasoning, and studied its relation to AAFs. In this paper, aiming to provide a deeper insight into this new model, we present more results on the relation between AAFDs and AAFs and the properties of the AAFD semantics, and we introduce an alternative, more expressive way to define the domains of arguments using logical predicates. We also offer an implementation of AAFDs based on Answer Set Programming (ASP) and evaluate it using a range of experiments with synthetic datasets.

Abstract Abstract argumentation frameworks (AAFs), introduced by Dung (1995, Artif. Intell., 228, 321–357), enabled a new way of reasoning with arguments, which does not take into account the internal structure of arguments but only how they are related to each other. The only form of relation considered in AAFs is a binary attack relation on the set of arguments. From the definitions of acceptability semantics of AAFs, it is obvious that attacks actually have a dual role: on the one hand, they generate conflicts; on the other hand, they can defend other arguments from attacks. In this paper, we propose a framework, where the modeller can explicitly specify the role of each attack. For this purpose, we define a set of conflict-generating attacks ${\mathcal {R}_{C}}$ and a set of defending attacks ${\mathcal {R}_{d}}$, as well as a family of semantics that considers the role of each attack while determining which arguments are attacked, which are defended and which will be included in each extension. We study the formal properties of the proposed framework and semantics, show that our framework is a generalization of AAFs and assess its semantics against a set of principles. Finally, we present a web application that provides an interface for creating custom argumentation frameworks and uses ASP to compute their extensions.

Abstract Automatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.

Informal care is fundamental in the wellbeing and resilience of elderly and people with chronic conditions. However, solutions for the effective collaboration of healthcare professionals, patients and informal carers are not yet widely available. CareKeeper builds on a state-of-the-art personal health system, augmenting it with Artificial Intelligence and Big Data technologies, to boost informal care coordination. In this paper we report on the design of the platform with the aim of providing a light-weighted communication solution to support practical challenges about sharing the responsibility of caring, such as the frequency of visits, support to routinely activities and timely intervention in case of emergency and need.

The continuous growth of high volumes of biomedical data in healthcare generates significant challenges for their efficient management. This data requires efficient management and analysis in order to derive meaningful and actionable information. Especially in the current situation of the COVID-19 pandemic, complications that might occur after the onset of this disease are important. Such a complication is Acute Respiratory Distress Syndrome (ARDS), which is a serious respiratory condition with high mortality and associated morbidity. A large number of basic and clinical studies demonstrated that early diagnosis and intervention are keys to improve the survival rate of patients with ARDS. Therefore, there is a pressing need for the development and clinical testing of predictive models for ARDS events, which might improve the clinical diagnosis or the management of ARDS. In this paper, we focus on two distinct objectives; namely a) to design a scalable data science platform, built on open source technologies able to streamline the development of such models, and b) to exploit the platform using publicly available big datasets to develop such models. To this direction, we employ random forests and logistic regression algorithmic models for the early prediction and diagnosis of ARDS. Our approach achieves better results in all metrics, when compared to relevant published efforts using the MIMIC III dataset.

Argumentative discourse rarely consists of opinions whose claims apply universally. As with logical statements, an argument applies to specific objects in the universe or relations among them, and may have exceptions. In this paper, we propose an argumentation formalism that allows associating arguments with a domain of application. Appropriate semantics are given, which formalise the notion of partial argument acceptance, i.e. the set of objects or relations that an argument can be applied to. We show that our proposal is in fact equivalent to the standard Argumentation Frameworks of Dung, but allows a more intuitive and compact expression of some core concepts of commonsense and non-monotonic reasoning, such as the scope of an argument, exceptions, relevance and others.

The rapid explosion of linked data has resulted into many weakly structured and incomplete data sources, where typing information might be missing. On the other hand, type information is essential for a number of tasks such as query answering, integration, summarization and partitioning. Existing approaches for type discovery, either completely ignore type declarations available in the dataset (implicit type discovery approaches), or rely only on existing types, in order to complement them (explicit type enrichment approaches). Implicit type discovery approaches are based on instance grouping, which requires an exhaustive comparison between the instances. This process is expensive and not incremental. Explicit type enrichment approaches on the other hand, are not able to identify new types and they can not process data sources that have little or no schema information. In this paper, we present HInT, the first incremental and hybrid type discovery system for RDF datasets, enabling type discovery in datasets where type declarations are missing. To achieve this goal, we incrementally identify the patterns of the various instances, we index and then group them to identify the types. During the processing of an instance, our approach exploits its type information, if available, to improve the quality of the discovered types by guiding the classification of the new instance in the correct group and by refining the groups already built. We analytically and experimentally show that our approach dominates in terms of efficiency, competitors from both worlds, implicit type discovery and explicit type enrichment while outperforming them in most of the cases in terms of quality.

Currently, there is a move towards adopting multi-clouds due to their main benefits, including vendor lock-in avoidance and optimal application realisation via different cloud services. However, such multi-cloud applications face a new challenge related to the dynamicity and uncertainty that even a single cloud environment exhibits. As such, they cannot deliver a suitable service level to their customers, resulting in SLA penalty costs and application provider reputation reduction. To this end, we have previously proposed a cross-level and multi-cloud application adaptation architecture. Towards realising this architecture, this paper proposes two extensions of the CAMEL language allowing to specify advanced adaptation rules and histories. Such extensions not only enable to cover cross-level application adaptation by executing adaptation workflows but also to progress such an adaptation to address both the application and exploited cloud services evolution.

Exploration and information identification constitute challenging research problems, with important applications in sensemaking over structured argumentative dialogues. In this paper, we present the implementation ArgQL, a high-level declarative query language, designed for querying dialogical data, structured in the principles of argumentation. We implement the language using an AIF-based representation and a translation of ArgQL into (complex) SPARQL queries. ArgQL provides a simple and intuitive way to query a structured dialogue using pure argumentative terminology.

Effective and accurate service discovery and composition rely on complete specifications of service behaviour, containing inputs and preconditions that are required before service execution, outputs, effects and ramifications of a successful execution and explanations for unsuccessful executions. The previously defined Web Service Specification Language (WSSL) relies on the fluent calculus formalism to produce such rich specifications for atomic and composite services. In this work, we propose further extensions that focus on the specification of QoS profiles, as well as partially observable service states. Additionally, a design framework for service-based applications is implemented based on WSSL, advancing state of the art by being the first service framework to simultaneously provide several desirable capabilities, such as supporting ramifications and partial observability, as well as non-determinism in composition schemas using heuristic encodings; providing explanations for unexpected behaviour; and QoS-awareness through goal-based techniques. These capabilities are illustrated through a comparative evaluation against prominent state-of-the-art approaches based on a typical SBA design scenario.

Current PaaS platforms enable single or hybrid cloud deployments. However, such deployment types cannot best cover the user application requirements as they do not consider the great variety of services offered by different cloud providers and the effects of vendor lock-in. On the other hand, multi-cloud deployment enables selecting the best possible service among equivalent ones providing the best trade-off between performance and cost. In addition, it avoids cases of service level deterioration due to service under-performance as main effects of vendor lock-in. While many multi-cloud application deployment research prototypes have been proposed, such prototypes do not examine the effect that deployment decisions have on application performance. As such, they blindly attempt to satisfy low-level hardware requirements by neglecting the impact of allocation decisions on higher-level requirements at the component or application level. To this end, this paper proposes a new IaaS selection algorithm which, apart from being able to satisfy both low and high level requirements of different types, it also exploits deployment knowledge offered via reasoning over previous application execution histories to take the best possible allocation decisions. The experimental evaluation clearly shows that by considering this extra knowledge, more optimal deployment solutions are derived, able to maintain the service levels requested by users, in less solving time.

Over the last few years we witness an explosion on the development of data management solutions for big data applications. To this direction NoSQL databases provide new opportunities by enabling elastic scaling, fault tolerance, high availability and schema flexibility. Despite these benefits, their limitations in the flexibility of query mechanisms impose a real barrier for any application that has not predetermined access use-cases. One of the main reasons for this bottleneck is that NoSQL databases do not support joins. In this poster we present a solution that efficiently supports joins over such databases. More specifically, we present a query optimization and execution module placed on top of Cassandra clusters that is able to efficiently combine information stored in different columnfamilies. Our preliminary evaluation demonstrates the feasibility of our solution and the advantages gained when compared to a recent commercial solution by DataStax. To the best of our knowledge our approach is the first and the only available open source solution allowing joins over NoSQL Cassandra databases.

This paper presents an extension to OWL-Q, a prominent semantic quality-based service description language, called Q-SLA, enabling to specify SLAs. This extension advances the state-of-the-art by covering all possible information aspects needed to enable proper and automatic support to all service management activities. A particular use-case is also provided highlighting Q-SLA's main benefits.

Abstract Action languages have emerged as an important field of knowledge representation for reasoning about change and causality in dynamic domains. This paper presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces.

While various platforms are offering facilities for single-cloud application design, deployment and provisioning, there is a need to move to multiple clouds in order to achieve cost-effectiveness and avoid vendor lock-in. Apart from not supporting multi-cloud application management, many platforms usually focus on the deployment and provisioning phases of the cloud-based application lifecycle by neglecting the design phase. However, the design selection of the best possible cloud service composition affects the provisioning phase, as the more distant from optimality is the selected solution, the more adaptation actions will be enacted. To this end, there is a high need for cloud application design tools and methods which can select the best possible cloud service composition based on user requirements. This paper satisfies this need by proposing a cloud service composition approach able to optimally compose different types of cloud services by simultaneously satisfying various types of user requirements. These types, not concurrently supported by any cloud application design tool, include quality, deployment, security, placement and cost requirements. Moreover, the proposed approach addresses a particular design choice type not currently considered in literature.

The Service-Oriented Computing (SOC) paradigm is currently being adopted by many developers, as it promises the construction of applications through reuse of existing Web Services (WSs). However, current SOC tools produce applications that interact with users in a limited way. This limitation is overcome by model-based Human-Computer Interaction (HCI) approaches that support the development of applications whose functionality is realized with WSs and whose User Interface (UI) is adapted to the user's context. Typically, such approaches do not consider various functional issues, such as the applications' semantics and their syntactic robustness in terms of the WSs selected to implement their functionality and the automation of the service discovery and selection processes. To this end, we propose a model-driven design method for interactive service-based applications that is able to consider the functional issues and their implications for the UI. This method is realized by a semiautomatic environment that can be integrated into current model-based HCI tools to complete the development of interactive service front-ends. The proposed method takes as input an HCI task model, which includes the user's view of the interactive system, and produces a concrete service model that describes how existing services can be combined to realize the application's functionality. To achieve its goal, our method first transforms system tasks into semantic service queries by mapping the task objects onto domain ontology concepts; then it sends each resulting query to a semantic service engine so as to discover the corresponding services. In the end, only one service from those associated with a system task is selected, through the execution of a novel service concretization algorithm that ensures message compatibility between the selected services.

Service-orientation paves the way for the Internet of Services (IoS), where millions of services will be available to realize the everyday user applications or tasks. Consequently, as a great number of functionally equivalent services will be available for a specific user task, the service nonfunctional aspect should be considered for filtering and selecting among these services. The state-of-the-art approaches in nonfunctional service discovery exploit constraint solving techniques to optimize the matchmaking time between a service offer and demand pair. However, they do not scale well, as matchmaking time is proportional to the offer number, so they are not yet suitable for the IoS. To this end, this article proposes three novel alternative techniques that intelligently organize the service offer space to improve the overall matchmaking time. These techniques are theoretically and experimentally evaluated. The results show that all techniques optimize the matchmaking time without sacrificing accuracy and that each technique is better in different circumstances.

We address the problem of synthesizing specifications for composite Web services, starting from those of their component services. Unlike related work in programming languages, we assume the definition of the component services (i.e. their code) to be unavailable - at best, they are known by a specification which (safely) approximates their functional behavior. Within this scenario, we deduce general formula schemes to derive specifications for basic constructs such as sequential, parallel compositions and conditionals and provide details on how to handle the special cases of loops and asynchronous execution. The resulting specifications facilitate service verification and service evolution as well as auditing processes, promoting trust between the involved partners.

Service-orientation paves the way for the Internet of Services (IoS), where millions of services will be available for building novel applications. As such, the service non-functional aspect should be considered for filtering and selecting among the great number of functionally-equivalent services that will be available for a specific user task. Until now, the state-of-the-art work in non-functional service discovery has exploited constraint solving techniques to optimize the matchmaking time between a non-functional service offer and demand pair. However, as matchmaking time is proportional to the offer number, this work does not scale well so it is not yet appropriate for the IoS. To this end, two alternative techniques are proposed to improve the overall matchmaking time. Both techniques were theoretically and experimentally evaluated. The results show that both techniques optimize the matchmaking time without sacrificing accuracy, while the second technique is quite scalable.

The evolution of ontologies is an undisputed necessity in ontology-based data integration. Yet, few research efforts have focused on addressing the need to reflect ontology evolution onto the underlying data integration systems. We present Exelixis, a web platform that enables query answering over evolving ontologies without mapping redefinition. This is achieved by rewriting queries among ontology versions. First, changes between ontologies are automatically detected and described using a high level language of changes. Those changes are interpreted as sound global-as-view (GAV) mappings. Then query expansion is applied in order to consider constraints from the ontology and unfolding to apply the GAV mappings. Whenever equivalent rewritings cannot be produced we a) guide query redefinition and/or b) provide the best "over-approximations", i.e. the minimally-containing and minimally-generalized rewritings. For the demonstration we will use four versions of the CIDOC-CRM ontology and real user queries to show the functionality of the system. Then we will allow conference participants to directly interact with the system to test its capabilities.

In p2p networks, top-k query processing can provide a lot of advantages both in time and bandwidth consumption. Several algorithms have been proposed for the evaluation of top-k queries. A large percentage of them follow the Threshold Approach. We focus on the main adaptations of threshold algorithms fulfilling the requirements of modern p2p applications. We introduce two algorithms optimized for ranking queries in p2p networks and present their characteristics. In the setting of a simulation of large p2p networks, we evaluate the algorithms' performance. Our experiments demonstrate that in some cases a threshold algorithm can improve top-k query processing, while in others it is far more costly. The results show that distributed query processing can be more effective than a simple threshold algorithm in a p2p network.

Giorgos Flouris, Mathieu d'Aquin, Grigoris Antoniou, Jeff Pan, Dimitris Plexousakis; Special Issue on Ontology Dynamics, Journal of Logic and Computation,

QoS-based Web Service (WS) discovery has been recognized as the main solution for filtering and selecting between functionally equivalent WSs stored in registries or other types of repositories. There are two main techniques for QoS-based WS matchmaking (filtering): ontology-based and Constraint Programming (CP)-based. Unfortunately, the first technique is not efficient as it is based on the rather immature technology of ontology reasoning, while the second one is not accurate as it is based on syntactic QoS-based descriptions and faulty matchmaking metrics. In our previous work, we have developed an extensible and rich ontology language for QoS-based WS description. Moreover, we have devised a semantic alignment algorithm for aligning QoS-based WS descriptions so as to increase the accuracy of QoS-based WS matchmaking algorithms. Finally, we have developed two alternative CP-based QoS-based WS matchmaking algorithms: a unary-constrained and n-ary-constrained one. In this paper, we claim that Mixed-Integer Programming (MIP) should be used as a matchmaking technique instead of CP and we provide experimental results proving it. In addition, we analyze and experimentally evaluate our matchmaking algorithms against a competing techniques one in order to demonstrate their efficiency and accuracy.

The goal of service oriented architectures (SOAs) is to enable the creation of business applications through the automatic discovery and composition of independently developed and deployed (Web) services. Automatic discovery of Web services (WSs) can be achieved by incorporating semantics into a richer WS description model (WSDM) and by the use of semantic Web (SW) technologies in the WS matchmaking and selection (i.e., discovery) process. A sufficiently rich WSDM should encompass not only functional but also nonfunctional aspects like quality of service (QoS). QoS is a set of performance and domain-dependent attributes that has a substantial impact on WS requesters' expectations. Thus, it can be used for distinguishing between many functionally equivalent WSs that are available nowadays. This paper starts by defining QoS in the context of WSs. Its main contribution is the analysis of the requirements for a semantically rich QoS-based WSDM and an accurate, effective QoS-based WS Discovery (WSDi) process. In addition, a road map of extending current WS standard technologies for realizing semantic, functional, and QoS-based WSDi, respecting the above requirements, is presented.

The goal of service oriented architectures is to enable the creation of business applications through the automatic discovery and composition of independently developed and deployed (Web) services. Automatic discovery of Web Services (WSs) can be achieved by incorporating semantics into a richer WS description model (WSDM) and the use of Semantic Web (SW) technologies in the WS matchmaking and selection models. A sufficiently rich WSDM should encompass not only functional but also non-functional aspects like Quality of Service (QoS). QoS is a set of performance attributes that has a substantial impact on WS requesters' expectations. Thus, it can be used as a discriminating factor of functionally-equivalent WSs. The focus of this paper is twofold: to analyze the requirements of a semantically rich QoS-based WSDM and to provide SW and constrained- based mechanisms for enriching syntactic QoS-based WS Discovery (WSDi) algorithms. In addition, a roadmap of extending WS standard techniques for realizing semantic, functional and QoS-based WSDi is presented.

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumptions that fluents persist and actions have effects on the subsequent situation only. We provide a solution based on an extension of the situation calculus and the work of McCain and Turner. More specifically, we study the case where there are conflicting effects of concurrently executing actions and we distinguish between hard and soft integrity constraints.

The ramification problem is a hard and ever present problem in systems exhibiting a dynamic behavior. The area of temporal databases in particular is still lacking satisfactory solutions to the ramification problem. In this paper, we address the problem for the concurrent execution of both instantaneous actions and actions with duration. The proposed solution advances previous work by considering actions with effects occurring in any of the possible future situations resulting from an action's execution.

This paper focuses on the optimization of the navigation through voluminous subsumption hierarchies of topics employed by Portal Catalogs like Netscape Open Directory (ODP). We advocate for the use of labeling schemes for modeling these hierarchies in order to efficiently answer queries such as subsumption check, descendants, ancestors or nearest common ancestor, which usually require costly transitive closure computations. We first give a qualitative comparison of three main families of schemes, namely bit vector, prefix and interval based schemes. We then show that two labeling schemes are good candidates for an efficient implementation of label querying using standard relational DBMS, namely, the Dewey Prefix scheme [6] and an Interval scheme by Agrawal, Borgida and Jagadish [1]. We compare their storage and query evaluation performance for the 16 ODP hierarchies using the PostgreSQL engine.

The ramification problem is a hard and ever present problem in systems exhibiting dynamic behavior. The area of temporal databases in particular still lacks satisfactory solutions to the ramification problem. In this paper we address the ramification problem based on causal relationships that take time into account. We study the problem for both instantaneous actions and actions with duration. The proposed solution advances previous work by considering actions with effects occurring in any of the possible future situations resulting from an action's execution.

Real-scale Semantic Web applications, such as Knowledge Portals and E-Marketplaces, require the management of large volumes of metadata, i.e., information describing the available Web content and services. Better knowledge about their meaning, usage, accessibility or quality will considerably facilitate an automated processing of Web resources. The Resource Description Framework (RDF) enables the creation and exchange of metadata as normal Web data. Although voluminous RDF descriptions are already appearing, sufficiently expressive declarative languages for querying both RDF descriptions and schemas are still missing. In this paper, we propose a new RDF query language called RQL. It is a typed functional language (a la OQL) and relies on a formal model for directed labeled graphs permitting the interpretation of superimposed resource descriptions by means of one or more RDF schemas. RQL adapts the functionality of semistructured/XML query languages to the peculiarities of RDF but, foremost, it enables to uniformly query both resource descriptions and schemas. We illustrate the RQL syntax, semantics and typing system by means of a set of example queries and report on the performance of our persistent RDF Store employed by the RQL interpreter.

This paper focuses on the semantics of Telos, a language for representing knowledge about information systems. Telos is intended to support the development of information systems, especially in the requirements modeling phase. An object‐oriented representational framework is supported by Telos. Its features include aggregation, generalization, and classification, the treatment of attributes as first‐class objects and the explicit representation of time. Telos also provides an assertion sublanguage for expressing deductive rules and integrity constraints. A possible‐worlds semantics is defined for Telos knowledge bases. This semantics is intended to capture the peculiarities involved in the interpretation of temporal expressions. The integration of time has also inspired the treatment of existence in Telos. An ontology of objects based on the property of existence is proposed. In the spirit of KRYPTON, Telos knowledge bases are specified functionally, in terms of the operations provided for querying and updating them. This knowledge‐level analysis will allow us to specify exactly what a knowledge base can be ASK‐ed or TELL‐ed about the domain of discourse. Soundness, consistency, and completeness results have also been proven to complete the specification of Telos knowledge bases. This formal account of the language provides a logical framework that can be used to verify the correctness of any proposed implementation of the system.