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2nd International Conference Citizen Observatories for natural hazards and Water Management (COWM 2018), Venice, Italy, 27-30 November 2018 2018
During climate-related crises, vast volumes of heterogeneous multimodal information are generated. Meaningfully processing and communicating this information for efficient decision support is a key challenge. The paper describes applying Semantic Web technologies for decision support during such crises. We are proposing the application of these technologies in the whole sensor to decision chain. This approach is being tested within the beAWARE EU project, with contributions by domain experts.
Empowering Persons with Deafblindness: Designing an Intelligent Assistive Wearable in the SUITCEYES Project
11th PErvasive Technologies Related to Assistive Environments (PETRA) Conference 2018
Deafblindness is a condition that limits communication capabilities primarily to the haptic channel. In the EU-funded project SUITCEYES we design a system which allows haptic and thermal communication via soft interfaces and textiles. Based on user needs and informed by disability studies, we combine elements from smart textiles, sensors, semantic technologies, image processing, face and object recognition, machine learning, affective computing, and gamification. In this work, we present the underlying concepts and the overall design vision of the resulting assistive smart wearable.
EnviroInfo 2018 2018
In every disaster time is the enemy and getting accurate and helpful real-time information for supporting decision support is critical. Data sources for Risk Management Platforms are heterogeneous. This includes data coming from several resources: sensors, social media, the general public and first responders. All this data needs to be analyzed, aggregated and fused and the semantics of the data needs to be understood. This paper discusses means for integrating and harmonizing data into an ICT platform for risk management and gives examples for semantic analysis.
1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT 2018), colocated with the 15th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2018) 2018
One of the most critical challenges faced by authorities during the management of a climate-related crisis is the overwhelming flow of heterogeneous information coming from humans and deployed sensors (e.g. cameras, temperature measurements, etc.), which has to be processed in order to filter meaningful items and provide crisis decision support. Towards addressing this challenge, ontologies can provide a semantically unified representation of the domain, along with superior capabilities in querying and information retrieval. Nevertheless, the recently proposed ontologies only cover subsets of the relevant concepts. This paper proposes a more 'all-around' lightweight ontology for climate crisis management, which greatly facilitates decision support and merges several pertinent aspects: representation of a crisis, climate parameters that may cause climate crises, sensor analysis, crisis incidents and related impacts, first responder unit allocations. The ontology could constitute the backbone of the decision support systems for crisis management.
15th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2018) 2018
In every disaster and crisis, incident time is the enemy, and getting accurate information about the scope, extent, and impact of the disaster is critical to creating and orchestrating an effective disaster response and recovery effort. Decision Support Systems (DSSs) for disaster and crisis situations need to solve the problem of facilitating the broad variety of sensors available today. This includes the research domain of the Internet of Things (IoT) and data coming from social media. All this data needs to be aggregated and fused, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. Furthermore, the interaction and integration with existing risk and crisis management systems are necessary for a better analysis of the situation and faster reaction times. This paper provides an insight into the sensor to decision chain and proposes solutions and technologies for each step.
4th International Conference on Decision Support System Technology (ICDSST 2018) 2018
As urban atmospheric conditions are tightly connected to citizens’ quality of life, the concept of efficient environmental decision support systems becomes highly relevant. However, the scale and heterogeneity of the involved data, together with the need for associating environmental information with physical reality, increase the complexity of the problem. In this work, we capitalize on the semantic expressiveness of ontologies to build a framework that uniformly covers all phases of the decision making process: from structuring and integration of data, to inference of new knowledge. We define a simplified ontology schema for representing the status of the environment and its impact on citizens’ health and actions. We also implement a novel ontology- and rulebased reasoning mechanism for generating personalized recommendations, capable of treating differently individuals with diverse levels of vulnerability under poor air quality conditions. The overall framework is easily adaptable to new sources and needs.
Information Systems Frontiers 2018
The rise of the Semantic Web has provided cultural heritage researchers and practitioners with several tools for providing semantically rich representations and interoperability of cultural heritage collections. Although indeed offering a lot of advantages, these tools, which come mostly in the form of ontologies and related vocabularies, do not provide a conceptual model for capturing contextual and environmental dependencies, contributing to long-term digital preservation. This paper presents one of the key outcomes of the PERICLES FP7 project, the Linked Resource Model, for modelling dependencies as a set of evolving linked resources. The adoption of the proposed model and the consistency of its representation are evaluated via a specific instantiation involving the domain of digital video art.
PaaSport Semantic Model: An Ontology for a Platform-as-a-Service Semantically Interoperable Marketplace
Data & Knowledge Engineering 2018
Abstract PaaS is a Cloud computing service that provides a computing platform to develop, run, and manage applications without the complexity of infrastructure maintenance. SMEs are reluctant to enter the growing PaaS market due to the possibility of being locked in to a certain platform, mostly provided by the market's giants. The PaaSport Marketplace aims to avoid the provider lock-in problem by allowing Platform provider SMEs to roll out semantically interoperable PaaS offerings and Software SMEs to deploy or migrate their applications on the best-matching offering, through a thin, non-intrusive Cloud broker. In this paper, we present the PaaSport semantic model, namely an OWL ontology, extension of the DUL ontology. The ontology is used for semantically representing a) PaaS offering capabilities and b) requirements of applications to be deployed. The ontology has been designed to optimally support a semantic matchmaking and ranking algorithm that recommends the best-matching PaaS offering to the application developer. The DUL ontology offers seamless extensibility, since both PaaS Characteristics and parameters are defined as classes; therefore, extending the ontology with new characteristics and parameters requires the addition of new specialized subclasses of the already existing classes, which is less complicated than adding ontology properties. The PaaSport ontology is evaluated through verification tools, competency questions, human experts, application tasks and query performance tests.
A Domain-Agnostic Tool for Scalable Ontology Population and Enrichment from Diverse Linked Data Sources
Selected Papers of the XIX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2017) 2017
Ontologies are a rapidly emerging paradigm for knowledge representation, with a growing number of applications in data-intensive domains. However, populating enterprise-level ontologies with massive volumes of data is a non-trivial and laborious task. Towards tackling this problem, the field of ontology population offers a multitude of approaches for populating ontologies with instances in an automated or semi-automated way. Nevertheless, most of the related tools typically analyse natural language text and neglect more structured types of information like Linked Data. The paper argues that the rapidly increasing array of published Linked Datasets can serve as the input for large-scale ontology population in data-intensive domains and presents PROPheT, a novel software tool for ontology population and enrichment. PROPheT can populate a local ontology model with instances retrieved from diverse Linked Data sources served by SPARQL endpoints. As demonstrated in the paper, the tool is domain-agnostic and can efficiently handle vast volumes of input data. To the best of our knowledge, no existing tool can offer PROPheT’s diverse extent of functionality.
Joint Proceedings of SEMANTiCS 2017 Workshops co-located with the 13th International Conference on Semantic Systems (SEMANTiCS 2017) 2017
Concept drift refers to the phenomenon that concepts change their intensional composition, and therefore meaning, over time. It is a manifestation of content dynamics, and an important problem with regard to access and scalability in the Web of Data. Such drifts go back to contextual influences due to social embedding as suggested by e.g. topic analysis, news detection, and trends in social networks. Using DBpedia as a source of timestamped Linked Open Data, we analyze the interaction between a sample of popular keywords, as recorded by Google Trends, and their respective concept drifts in DBpedia. For the latter task, we deploy SemaDrift, an ontology evolution platform for detecting and measuring content dislocation dependent on context modification. Our hypothesis is that social embedding and awareness is an important trigger for concept drift in crowdsourced knowledge bases on the Web.
Knowledge Engineering and Knowledge Management: EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, Italy, November 19-23, 2016, Revised Selected Papers 2017
Semantic drift is an active research field, which aims to identify and measure changes in ontologies across time and versions. Yet, only few practical methods have emerged that are directly applicable to Semantic Web constructs, while the lack of relevant applications and tools is even greater. This paper presents the findings, current limitations and lessons learned throughout the development and the application of a novel software tool, developed in the context of the PERICLES FP7 project, which integrates currently investigated methods, such as text and structural similarity, into the popular ontology authoring platform, Protégé. The graphical user interface provides knowledge engineers and domain experts with access to methods and results without prior programming knowledge. Its applicability and usefulness are validated through two proof-of-concept scenarios in the domains of Web Services and Digital Preservation; especially the latter is a field where such long-term insights are crucial.
Joint proceedings of the 3rd Workshop on Managing the Evolution and Preservation of the Data Web (MEPDaW 2017) and the 4th Workshop on Linked Data Quality (LDQ 2017) co-located with 14th European Semantic Web Conference (ESWC 2017) 2017
Detecting and measuring semantic drift in different versions of ontologies across time is a novel area of research that rapidly gains attention. Nevertheless, there exist only a few relevant practical methods and tools and even fewer are flexible enough to be efficiently applied to multiple domains. As the often domain-specific nature of ontologies may render methods and tools for measuring semantic drift ineffective, this paper presents the application and findings of the SemaDrift suite of methods and tools in several domains, illustrating novel insights for the first time. While developed in the context of the PERICLES FP7 project, aimed at Digital Preservation, domain-independent text and structural similarity measures, available both as a software library and as a Protégé plugin for end-users, are now applied in the Dutch Historical Census and the BBC Sports Ontology. The two different domains demonstrate its applicability and ability to pinpoint the location, nature, origins and destinations of concept drift.
Expert Systems with Applications 2017
Platform as a service (PaaS) is one of the Cloud computing services that provide a computing platform in the Cloud, allowing customers to develop, run, and manage web applications without the complexity of building and maintaining the infrastructure. The primary disadvantage for an SME to enter the emerging PaaS market is the possibility of being locked into a certain platform, mostly provided by the market's giants. The PaaSport project focuses on facilitating SMEs to deploy business applications on the best-matching Cloud PaaS offering and to seamlessly migrate these applications on demand, via a thin, non-intrusive Cloud-broker, in the form of a Cloud PaaS Marketplace. PaaSport enables PaaS provider SMEs to roll out semantically interoperable PaaS offerings, by annotating them using a unified PaaS semantic model that has been defined as an OWL ontology. In this paper we focus on the recommendation algorithm that has been developed on top of the ontology, for providing the application developer with recommendations about the best-matching Cloud PaaS offering. The algorithm consists of: a) a matchmaking part, where the functional parameters of the application are taken into account to rule out inconsistent offerings, and b) a ranking part, where the non-functional parameters of the application are considered to score and rank offerings. Τhe algorithm is extensively evaluated showing linear scalability to the number of offerings and application requirements. Furthermore, it is extensible upon future semantic model extensions, because it is agnostic to domain specific concepts and parameters, using SPARQL template queries.
The Success of European Projects Using New Information and Communication Technologies 2016
Management of change is essential to ensure the long-term reusability of digital assets. Change can be brought about in many ways, including through technological, user community and policy factors. Motivated by case studies in space science and time-based media, we consider the impact of change on complex digital objects comprising multiple interdependent entities, such as files, software and documentation. Our approach is based on modelling of digital ecosystems, in which abstract representations are used to assess risks to sustainability and support tasks such as appraisal. The paper is based on work of the EU FP7 PERICLES project on digital preservation, and presents some general concepts as well as a description of selected research areas under investigation by the project.
Studying the Cohesion Evolution of Genes Related to Chronic Lymphocytic Leukemia Using Semantic Similarity in Gene Ontology and Self-Organizing Maps
9th Int. Conf. on Semantic Web Applications and Tools for Life Sciences - SWAT4LS 2016
A significant body of work on biomedical text mining is aimed at uncovering meaningful associations between biological entities, including genes. This has the potential to offer new insights for research, uncovering hidden links between genes involved in critical pathways and processes. Recently, high-throughput studies have started to unravel the genetic landscape of chronic lymphocytic leukemia (CLL), the most common adult leukemia. CLL displays remarkable clinical heterogeneity, likely reflecting its underlying biological heterogeneity which, despite all progress, still remains insufficiently characterized and understood. This paper deploys an ontology-based semantic similarity combined with self-organizing maps for studying the temporal evolution of cohesion among CLL-related genes and the extracted information. Three consecutive time periods are considered and groups of genes are derived therein. Our preliminary results indicated that our proposed gene groupings are meaningful and that the temporal dimension indeed impacted the gene cohesion, leaving a lot of room for further promising investigations.
1st International Workshop on Detection, Representation and Management of Concept Drift in Linked Open Data (Drift-a-LOD) in conjunction with the 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW) 2016
Semantic drift is an active research field, which aims to identify and measure changes in ontologies across time and versions. Yet, only few practical methods have emerged that are directly applicable to Semantic Web constructs, while the lack of relevant applications and tools is even greater. This paper presents a novel software tool developed in the context of the PERICLES FP7 project that integrates currently investigated methods, such as text and structural similarity, into the popular ontology authoring platform, Protégé. The graphical user interface provides knowledge engineers and domain experts with access to methods and results without prior programming knowledge. Its applicability and usefulness are validated through two proof-of-concept scenarios in the domains of Web Services and Digital Preservation; especially the latter is a field where such long-term insights are crucial.
Ontology-Driven Context Interpretation and Conflict Resolution in Dialogue-Based Home Care Assistance
9th Int. Conf. on Semantic Web Applications and Tools for Life Sciences - SWAT4LS 2016
In this paper we present a framework for conversational awareness and conflict resolution in spoken dialogue systems for home care assistance. Conversational awareness is supported through OWL ontologies for capturing conversational modalities, while interpretation and incremental context enrichment is facilitated through Description Logics reasoning. Conflict resolution further assists the interaction with end users, facilitating exception handling and context prioritisation by coupling defeasible logics with medical and profile information.
Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16) 2016
Semantic drift is an active field of research, aiming to identify and measure changes in ontologies across time and versions, closely related to ontology evolution. However, practical and widely adopted methods that are directly applicable to Semantic Web constructs have yet to emerge. Building upon and extending existing work, this paper presents a framework for measuring semantic drift in ontologies across time or multiple versions, using text and structural similarity methods to provide valuable insights. Its applicability and usefulness are validated through a proof-of-concept scenario in Digital Preservation, where long-term insights about change are crucial, to track drift across a decade’s worth of real-world digital media data.
1st Workshop on Humanities in the Semantic Web co-located with 13th ESWC Conference 2016 (ESWC 2016) 2016
The fields of Digital Humanities and Digital Preservation are not yet enjoying the full potential of the Semantic Web and relevant technologies, largely due to the highly contextualized nature of their source materials. In this setting, the paper addresses the issue of representing context and use-context (i.e. context of use) of digital content, by proposing an ontology-based representation approach. This approach is based on the LRM, an upper-level ontology for describing dependencies between digital resources developed within the PERICLES FP7 project. Besides describing the novel adopted representations, the paper also presents sample instantiations of all relevant concepts based on domain ontologies developed within the same project.
7th International Conference on Knowledge Engineering and the Semantic Web (KESW 2016) 2016
In order for ontology-based applications to be deployed in real-life scenarios, significant volumes of data are required to populate the underlying models. Populating ontologies manually is a time-consuming and error-prone task and, thus, research has shifted its attention to automatic ontology population methodologies. However, the majority of the proposed approaches and tools focus on analysing natural language text and often neglect other more appropriate sources of information, such as the already structured and semantically rich sets of Linked Data. The paper presents PROPheT, a novel ontology population tool for retrieving instances from Linked Data sources and subsequently inserting them into an OWL ontology. The tool, to the best of our knowledge, offers entirely novel ontology population functionality to a great extent and has already been positively received according to user evaluation.
Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16) 2016
In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-performance machine learning algorithm for the training of extremely large emergent self-organizing maps for exploratory data analysis. The working hypothesis we present here is that the dynamics of semantic drifts can be modeled on a relaxed version of Newtonian mechanics called social mechanics. By using term distances as a measure of semantic relatedness vs. their PageRank values indicating social importance and applied as variable ‘term mass’, gravitation as a metaphor to express changes in the semantic content of a vector field lends a new perspective for experimentation. From ‘term gravitation’ over time, one can compute its generating potential whose fluctuations manifest modifications in pairwise term similarity vs. social importance, thereby updating Osgood’s semantic differential. The dataset examined is the public catalog metadata of Tate Galleries, London.
Journal of Ambient Intelligence and Smart Environments 2016
This paper presents a novel, integrated platform for energy monitoring, management and savings in the context of a Smart University Building. Namely, the proposed Smart International Hellenic University (IHU) platform integrates an intelligent, rule-based agent that enforces savings, while a variety of applications offers user interaction with the system and the means for monitoring and management. The application layer is built over a common Web Service middleware, incorporating semantic interoperability. Monitoring applications visualize raw and aggregated sensor readings, such as building energy disaggregation, environmental measurements and data center efficiency. Extensive monitoring capabilities allow users to take immediate action and devise policies towards energy-savings. Such policies are, then, autonomously enforced by the intelligent, hybrid agent, which is capable of both deliberative (long-term) and reactive (immediate) actions. The agent is also integrated with the OpenADR standard for receiving provider instructions in future Smart Grids. A pilot deployment of the agent with expert-formulated policies at the IHU premises, has managed to reduce the total daily consumption of a typical university office by approximately 16%.
Journal of Cleaner Production 2016
In an effort to tackle climate change most countries utilize renewable energy sources. This is more pronounced in the building sector, which is currently one of the major consumers of energy, mostly in the form of heat. In order to further promote the use of domestic solar hot water systems in buildings, an ontology-based decision support tool has been developed and is presented in this paper. The proposed tool aids non-technical consumers to select a domestic solar hot water system tailored to their needs, containing up-to-date information on its components and interrelationships, installation costs etc., in the form of an ontology formulated in OWL (Web Ontology Language). The optimum system configurations are computed based on various specific parameters, such as number of occupants, daily hot water requirements and house location. The backbone of the proposed system is an ontology that represents the application domain and contains information regarding the various domestic solar hot water system components along with their interrelationships. Ontologies are a rapidly evolving knowledge representation paradigm that offers various advantages and, when deployed specifically in the domestic solar hot water systems domain, deliver improved representation, sharing and re-use of the relevant information. As a conclusion, this paper presents an ontology-driven decision support system for facilitating the selection of domestic solar hot water system, which delivers certain advantages, such as sustainability of the decision support system itself, due to its open and interoperable knowledge-base, and its adaptability/flexibility in decision making policies, due to is semantic (ontological) nature.
6th Workshop on Ontology and Semantic Web Patterns (WOP 2015) co-located with the 14th Int. Semantic Web Conf. (ISWC 2015) 2015
In this paper we introduce an ODP for representing digital video resources. The aim is to model digital video files, their components and other associated entities, such as codecs and containers. The proposed design pattern facilitates the creation of relevant domain ontologies that will be deployed in the fields of media archiving and digital preservation of videos and video artworks. This ODP has been developed within the PERICLES FP7 project.
Pervasive and Mobile Computing 2015
Pervasive and sensor-driven systems are by nature open and extensible, both in terms of input and tasks they are required to perform. Data streams coming from sensors are inherently noisy, imprecise and inaccurate, with differing sampling rates and complex correlations with each other. These characteristics pose a significant challenge for traditional approaches to storing, representing, exchanging, manipulating and programming with sensor data. Semantic Web technologies provide a uniform framework for capturing these properties. Offering powerful representation facilities and reasoning techniques, these technologies are rapidly gaining attention towards facing a range of issues such as data and knowledge modelling, querying, reasoning, service discovery, privacy and provenance. This article reviews the application of the Semantic Web to pervasive and sensor-driven systems with a focus on information modelling and reasoning along with streaming data and uncertainty handling. The strengths and weaknesses of current and projected approaches are analysed and a roadmap is derived for using the Semantic Web as a platform, on which open, standard-based, pervasive, adaptive and sensor-driven systems can be deployed.
1st International Workshop on Semantic Web for Cultural Heritage In Conjunction with 19th East-European Conference on Advances in Databases and Information Systems (ADBIS 2015) 2015
The rise of the Semantic Web has provided cultural heritage researchers and practitioners with several tools for ensuring semantic-rich representations and interoperability of cultural heritage collections. Although indeed offering a lot of advantages, these tools, which come mostly in the form of ontologies and related vocabularies, do not provide a conceptual model for capturing contextual and environmental dependencies contributing to long-term digital preservation. This paper presents one of the key outcomes of the PERICLES FP7 project, the Linked Resource Model, for modelling dependencies as a set of evolving linked resources. The proposed model is evaluated via a domain-specific representation involving digital video art.
15th International Joint Conference on Neural Networks (IJCNN 2015) 2015
Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent changes in a vector space model by a combination of random indexing and evolving self-organizing maps (ESOM). To monitor semantic drifts within the observation period, an experiment was carried out on the term space of a collection of 12.8 million Amazon book reviews. For evaluation, the semantic consistency of ESOM term clusters was compared with their respective neighbourhoods in WordNet, and contrasted with distances among term vectors by random indexing. We found that at 0.05 level of significance, the terms in the clusters showed a high level of semantic consistency. Tracking the drift of distributional patterns in the term space across time periods, we found that consistency decreased, but not at a statistically significant level. Our method is highly scalable, with interpretations in philosophy.
Pervasive and Mobile Computing 2015
This paper presents a novel real-world application for energy savings in a Smart Building environment. The proposed system unifies heterogeneous wireless sensor networks under a Semantic Web Service middleware. Two complementary and mutually exclusive rule-based approaches for enforcing energy-saving policies are proposed: a reactive agent based on production rules and a deliberative agent based on defeasible logic. The system was deployed at a Greek University, showing promising experimental results (at least 4% daily savings). Although the percentage of energy savings may seem low, the greatest merit of the method is ensuring no energy is wasted by constantly enforcing the policies.
1st Joint International Workshop on Semantic Sensor Networks and Terra Cognita 2015
Understanding human activities in pervasive environments is a key challenge that involves fusion and correlation of multimodal sensor information. Many research efforts have been recently focused on knowledge-driven solutions to human activity recognition, using ontologies for defining activity models and for capturing contextual information. In most cases, however, the unrealistic assumption is made that activities are performed in a sequential, non-interrupted manner, hampering their applicability in real-world scenarios. In this paper, we present a framework for detecting interleaved activities of daily living (ADL) using (a) OWL 2 for implementing the underlying model semantics capturing contextual dependencies among activities, and (b) defeasible reasoning for introducing a flexible conflict resolution mechanism. The proposed framework has been integrated in an existing context-aware ADL recognition framework, which is being used for supporting the diagnosis of the Alzheimer’s disease in a controlled environment.
International Journal of E-Health and Medical Communications 2015
DemaWare is a Service-Oriented platform that aids in the timely assessment and monitoring of people with dementia in an Ambient Assisted Living context. This work presents in detail the underlying modules integrated in DemaWare, providing both software and hardware services. The system coordinates the retrieval of raw sensor data from a variety of sources, such as ambient and wearable sensors, and their processing into a common knowledge base. The semantic interpretation performed afterwards reasons upon collected knowledge and infers higher level observations. Finally, all knowledge is presented in suitable end-user applications that support various scenarios, e.g. lab assessment trials and monitoring in nursing home environments.
An Applied Energy Management Approach in Intelligent Environments based on a Hybrid Agent Architecture
3rd International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE) in conjunction with ECAI 2014 2014
This paper presents a framework for ambient sensing and managing a University building aimed at energy savings and user comfort. The system builds upon previous work, using a Semantic Web Service middleware for unifying the various heterogeneous sensor and actuator networks. Two applications are introduced in the framework, the Manager App and the Rule App. The latter incorporates a hybrid intelligent agent that enables both reactive and deliberate manipulation of the environment, based on user-configurable policies expressed in defeasible logic. The Manager App provides users with advanced control and renders the system sustainable. Specifically, it allows bypassing the policies and manually administrating the infrastructure, e.g. during exceptional or emergency cases. The framework is deployed and evaluated at a University course office, guaranteeing 20% daily energy savings on controlled devices, aggregated to 17% total per room savings.
3rd International Workshop on Artificial Intelligence and Assistive Medicine (AI-AM/NetMed) in conjunction with ECAI 2014 2014
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as software components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
3rd International Workshop on Artificial Intelligence Techniques for Power Systems and Energy Markets (IATEM) in conjunction with DEXA 2014 2014
This paper presents a novel framework for multi-agent coordination in smart buildings, towards the aim of energy management. The framework builds on top of an existing Service-Oriented middleware for Ambient Intelligence, which offers sensor and actuator functions of wireless devices. The middleware also provides a semantics infrastructure that assists in authoring agent policies for reducing energy consumption and maximizing user comfort. Each agent within the framework is responsible for monitoring the environmental context and controlling the electrical appliances of a specific room. However, the collective behavior of the multi-agent system is controlled by a Coordinator Agent that approves or rejects the allocation of building resources in time, aiming at more “long-term” goals that are out of the reach and scope of the individual Room Agents. The agents’ underlying logic is expressed via Defeasible Logics, a formalism offering intuitive knowledge representation and advanced conflict resolution mechanisms.
13th International Semantic Web Conference (ISWC 2014) 2014
We propose a knowledge-driven activity recognition and segmentation framework introducing the notion of context connections. Given an RDF dataset of primitive observations, our aim is to identify, link and classify meaningful contexts that signify the presence of complex activities, coupling background knowledge pertinent to generic contextual dependencies among activities. To this end, we use the Situation concept of the DOLCE+DnS Ultralite (DUL) ontology to formally capture the context of high-level activities. Moreover, we use context similarity measures to handle the intrinsic characteristics of pervasive environments in real-world conditions, such as missing information, temporal inaccuracies or activities that can be performed in several ways. We illustrate the performance of the proposed framework through its deployment in a hospital for monitoring activities of Alzheimer’s disease patients.
Expert Systems with Applications 2013
The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.
2nd International Conference on Web Intelligence, Mining and Semantics (WIMS'12) 2012
The Semantic Web represents an initiative to improve the current Web, by augmenting content with semantics and encouraging cooperation among human and software agents. The development of the logic and proof layers of the Semantic Web is currently concentrating the related research effort and is vital, since these layers allow systems to infer new knowledge from existing information, assisting them in explaining their actions and, ultimately, increasing user trust towards the Semantic Web. However, there is a lack of applications that could contribute towards developing logic-based applications. Consequently, users resort to inadequate tools that offer syntactic support, without being able to support the user semantically as well. This work presents S2DRREd, a software tool that introduces a supplementary level of semantic assistance during rule base development. The tool allows creating meta-models of the main notions of the loaded rule sets and assists the user in authoring rule bases, independently of the explicitly chosen rule language syntax. The domain of application is defeasible logic, a type of logic that allows reasoning with incomplete and conflicting information and, as such, it can play an increasingly important role in a drastically dynamic environment like the Web.
International Journal on Semantic Web and Information Systems 2011
Defeasible logic is a non-monotonic formalism that deals with incomplete and conflicting information, while modal logic deals with the concepts of necessity and possibility. These types of logics can play a significant role in the emerging Semantic Web, which aims at enriching the available Web information with meaning, leading to better cooperation between end-users and applications. Defeasible and modal logics, in general, and, particularly, deontic logic can assist by providing means for modeling agent communities, where each agent is characterized by its own cognitive profile and normative system, as well as policies, which define privacy requirements, access permissions and individual rights. Towards this direction, this article reports on the extension of DR-DEVICE, a Semantic Web-aware defeasible reasoner, with a mechanism for expressing modal logic operators, while testing the implementation via deontic logic operators, concerned with obligations, permissions and related concepts. The motivation behind this work is to develop a practical defeasible reasoner for the Semantic Web that will take advantage of the expressive power offered by modal logics, accompanied by the flexibility to define diverse agent behaviours. A further incentive is to study the various motivational notions of deontic logic and to discuss the cognitive state of agents as well as the interactions among them.
Knowledge-Based Systems 2011
The Semantic Web aims at improving the current Web, by augmenting its content with semantics and encouraging the cooperation among human users and machines. Since the basic Semantic Web infrastructure is reaching sufficient maturity, research efforts are shifting towards logic, proof and trust and rule-based systems inevitably concentrate most of the attention. Nevertheless, in order for human users to trust system answers, they have to be presented with adequate explanations that justify the derived results. And, even more importantly, these explanations have to be presented in a user-comprehensible format. Consequently, the focus in this work is on humans and the research area called proof visualization that features three main approaches: tree-based, graphical and logical/textual. Since each of the approaches presents advantages and disadvantages, this article proposes a fourth, hybrid visualization approach that combines the pros of all three approaches and attempts to leverage the respective cons. The article also presents a software tool that implements the proposed hybrid approach. The tool is called VProofH and visualizes defeasible logic proofs, offering multiple representations that adapt to user needs. Extensive scalability and user evaluation tests prove the software tool’s usability.
1st International Conference Electronic Government and the Information Systems Perspective (EGOVIS 2010) 2010
Public institutes often face the challenge of managing vast volumes of administrative documents, a need that is often met via Content Management Systems (CMSs). CMSs offer various advantages, like separation of data structure from presentation and variety in user roles, but also present certain disadvantages, like inefficient keyword-based search facilities. The new generation of content management solutions imports the notion of semantics and is based on Semantic Web technologies, such as metadata and ontologies. The benefits include semantic interoperability, competitive advantages and dramatic cost reduction. In this paper a leading Enterprise CMS is extended with semantic capabilities for automatically importing and exporting ontologies. This functionality enables reuse of repository content, semantically-enabled search and interoperability with third-party applications. The extended system is deployed in semantically managing the large volumes of documents for a state university.
Informatica: International Journal of Computing and Informatics, Slovenian Society Informatika 2010
The Semantic Web aims at enriching information with well-defined semantics, making it possible both for people and machines to understand Web content. Intelligent agents are the most prominent approach towards realizing this vision. Nevertheless, agents do not necessarily share a common rule or logic formalism, neither would it be realistic to attempt imposing specific logic formalisms in a rapidly changing world like the Web. Thus, based on the plethora of proposals and standards for logic- and rule-based reasoning for the Semantic Web, a key factor for the success of Semantic Web agents lies in the interoperability of reasoning tasks. This paper reports on the implementation of trusted, third party reasoning services wrapped as agents in a multi-agent system framework. This way, agents can exchange their arguments, without the need to conform to a common rule or logic paradigm – via an external reasoning service, the receiving agent can grasp the semantics of the received rule set. Finally, a use case scenario is presented that illustrates the viability of the proposed approach.
6th Hellenic Conference on Artificial Intelligence (SETN 2010) 2010
The Semantic Web aims at augmenting the WWW with meaning, assisting people and machines in comprehending Web content and better satisfying their requests. Intelligent agents are considered to be greatly favored by Semantic Web technologies, because of the interoperability the latter will achieve. One of the main problems in agent interoperation is the great variety in reasoning formalisms, as agents do not necessarily share a common rule or logic formalism. This paper reports on the implementation of EMERALD, a knowledge-based framework for interoperating intelligent agents in the Semantic Web. More specifically, a multi-agent system was developed on top of JADE, featuring trusted, third party reasoning services, a reusable agent prototype for knowledge-customizable agent behavior, as well as a reputation mechanism for ensuring trust in the framework. Finally, a use case scenario is presented that illustrates the viability of the proposed framework.
IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'09) 2009
The Semantic Web aims at making Web content understandable both for people and machines. Although intelligent agents can assist towards this vision, they do not have to conform to a common rule or logic paradigm. This paper reports on the first steps towards a framework for interoperating knowledge-based intelligent agents. A multi-agent system was extended with defeasible reasoning and a reusable agent model is proposed for customizable agents, equipped with a knowledge base and a Jess rule engine. Two use case scenarios display the integration of these technologies.
3rd International Symposium on Intelligent Distributed Computing (IDC 2009) 2009
Based on the plethora of proposals and standards for logic- and rule-based reasoning for the Semantic Web (SW), a key factor for the success of SW agents is interoperability of reasoning tasks. This paper reports on the first steps towards a framework for interoperable reasoning among agents in the SW that deploys third-party trusted reasoning services. This way, agents can exchange their arguments, without the need to conform to a common rule or logic paradigm – via an external reasoning service, the receiving agent can grasp the semantics of the received rule set. The paper presents how a multi-agent system was extended with a third-party trusted defeasible reasoning service, which offers agents the ability of reasoning with incomplete and inconsistent information. In addition, a brokering trade scenario is presented that illustrates the usability of the approach.
5th IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI 2009) 2009
Knowledge management, along with the more recent trend of personal knowledge management, have attracted the attention of researchers from various angles, one of which is the Semantic Web. Since semantics promise to add value to the interaction of users with computers, many applications try to incorporate them. Ontologies, the primary knowledge representation tool for the Semantic Web, can play a significant role in semantically managing personal knowledge. The scope of this paper focuses on addressing the issue of effective personal knowledge management, by proposing an ontology for modelling the domain of biographical events. The proposed ontology also undergoes a thorough evaluation, based on specific criteria presented in the literature.
5th IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI 2009) 2009
The Semantic Web (SW) is an extension to the current Web, enhancing the available information with semantics. RDF, one of the most prominent standards for representing meaning in the SW, offers a data model for referring to objects and their interrelations. Managing RDF documents, however, is a task that demands experience and expert understanding. Tools have been developed that alleviate this drawback and offer an interactive graphical visualization environment. This paper studies the visualization of RDF documents, a domain that exhibits many applications. The most prominent approaches are presented and a novel graph-based visualization software application is also demonstrated.
International Symposium on Rule Interchange and Applications (RuleML-2008) 2008
The development of the Semantic Web proceeds in steps, building each layer on top of the other. Currently, the focus of research efforts is concentrated on logic and proofs, both of which are essential, since they will allow systems to infer new knowledge by applying principles on the existing data and explain their actions. Research is shifting towards the study of non-monotonic systems that are capable of handling conflicts among rules and reasoning with partial information. As for the proof layer of the Semantic Web, it can play a vital role in increasing the reliability of Semantic Web systems, since it will be possible to provide explanations and/or justifications of the derived answers. This paper reports on the implementation of a system for visualizing proof explanations on the SemanticWeb. The proposed system applies defeasible logic, a member of the non-monotonic logics family, as the underlying inference system. The proof representation schema is based on a graph-based methodology for visualizing defeasible logic rule bases.
Expert Systems with Applications 2008
Researchers in the area of educational software have always shown a great deal of interest for the automatic synthesis of learning curricula. During the recent years, with the extensive use of metadata and the emergence of the Semantic Web, this vision is gradually turning into a reality. A number of systems for curricula synthesis have been proposed. These systems are based on strong relations defined in the metadata of learning objects, which allow them to be combined with other learning objects, in order to form a complete educational program. This article presents PASER, a system for automatically synthesizing curricula using AI Planning and Semantic Web technologies. The use of classical planning techniques allows the system to dynamically construct learning paths even from disjoint learning objects, meeting the learner’s profile, preferences, needs and abilities.
Data and Knowledge Engineering 2008
The Semantic Web represents an initiative to improve the current Web, by enhancing web pages with metadata and, thus, making the content of the Web accessible not only to humans, as it is today, but to machines as well. Nevertheless, it still remains a vision to a great extent, mainly because end-users cannot conceive the benefits that stem from it. The reason for this lies in the lack of Semantic Web-related software applications that would help human users realize the diversity of services provided by the Semantic Web. This paper presents VDR-DEVICE, a system designed specifically for the Semantic Web environment. It is an integrated development environment for deploying and visualizing defeasible logic rule bases on top of RDF Schema ontologies. Contrary to other software systems developed for the Semantic Web, VDR-DEVICE does not aim at a specific layer/technology of the Semantic Web architecture. Instead, its functionality covers most of the Semantic Web layers, starting from content representation and reaching logic.
Workshop on Logics for Intelligent Agents and Multi-Agent Systems (WLIAMAS 2008) at IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '08) 2008
Defeasible logic is a non-monotonic formalism that deals with incomplete and conflicting information. Modal logic deals with necessity and possibility, exhibiting defeasibility; thus, it is possible to combine defeasible logic with modal operators. This paper reports on the extension of the DR-DEVICE defeasible reasoner with modal and deontic logic operators. The aim is a practical defeasible reasoner that will take advantage of the expressiveness of modal logics and the flexibility to define diverse agent types and behaviors.
International Journal on Artificial Intelligence Tools 2008
The standardization of the Semantic Web has reached as far as ontologies and ontology languages. However, in order for the full potential of the Semantic Web to be achieved, the ability of reasoning over the available information is also essential. Rules can assist in this affair and various logics have been proposed for the Semantic Web domain. One of them is defeasible reasoning that deals with incomplete and conflicting information. However, despite its solid mathematical notation, it may be confusing to end users. To confront this downside, we proposed a representation schema for defeasible logic rule bases, which is based on directed graphs that feature distinct node and connection types. This paper presents DR-VisMo, a defeasible logic rule base editor and visualization system that implements this representation approach. The system also features a stratification algorithm for visualizing rule bases that deals with decisions, regarding the arrangement of the various elements in the graph. DR-VisMo is implemented as part of VDR-DEVICE, an environment for modeling and deploying defeasible logic rule bases on top of RDF ontologies.
11th Panhellenic Conference on Informatics (PCI 2007) 2007
Ontologies are the primary knowledge representation tool in the Semantic Web and are mainly used in defining common vocabularies, used in the exchange of information among Semantic Web applications. In the process of encoding ontologies, appropriate ontology languages are applied; such a language is RDF Schema, one of the dominant standards. A variety of commercial and educational tools that address the tasks of developing and manipulating RDF Schema ontologies has been developed. None of them, however, is specifically destined for the inexperienced Semantic Web user. In this paper we present RDFSbuilder, a Java-built visual authoring tool for developing RDF Schema ontologies. The system helps users to develop their model quickly and efficiently, without being concerned about syntax or semantic errors. Furthermore, it adopts a purely object-oriented representation of the RDF Schema model, emphasizing on functional flexibility and simplicity of use. As a result, the model produced is easy to understand and equally easy to handle.
1st International Conference on Web Reasoning and Rule Systems (RR 2007) 2007
This work presents a visualization algorithm for defeasible logic rule bases as well as a software tool that applies this algorithm, according to which, a directed graph is produced that represents the rule base. The graph features distinct node types for rules and atomic formulas and distinct connection types for the various rule types of defeasible logic.
Visual Languages for Interactive Computing: Definitions and Formalizations 2007
This chapter is concerned with the visualization of defeasible logic rules in the Semantic Web domain. Logic plays an important role in the development of the Semantic Web and defeasible reasoning seems to be a very suitable tool. However, it is too complex for an end-user, who often needs graphical trace and explanation mechanisms for the derived conclusions. Directed graphs can assist in this affair, by offering the notion of direction that appears to be extremely applicable for the representation of rule attacks and superiorities in defeasible reasoning. Their applicability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the connections between the nodes in the graph. In this chapter we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantages. Finally, the chapter briefly presents a tool that implements this representation methodology.
19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007) 2007
Logic and proofs constitute key factors in increasing the user trust towards the Semantic Web. Defeasible reasoning is a useful tool towards the development of the Logic layer of the Semantic Web architecture. However, having a solid mathematical notation, it may be confusing to end users, who often need graphical trace and explanation mechanisms for the derived conclusions. In a previous work of ours, we outlined a methodology for representing defeasible logic rules, utilizing directed graphs that feature distinct node and connection types. However, visualizing a defeasible logic rule base also involves the placement of the multiple graph elements in an intuitive way, a non-trivial task that aims at improving user comprehensibility. This paper presents a stratification algorithm for visualizing defeasible logic rule bases that query and reason about RDF data as well as a tool that applies this algorithm.
11th International Workshop on Non-Monotonic Reasoning 2006
Non-monotonic reasoning constitutes an approach to reasoning with incomplete or changing information and is significantly more powerful than standard reasoning, which simply deals with universal statements. Defeasible reasoning, a member of the non-monotonic reasoning family, offers the extra capability of dealing with conflicting information and can represent facts, rules and priorities among rules. The main advantages of defeasible reasoning, however, are not only limited to its enhanced representational capabilities, but also feature low computational complexity compared to mainstream non-monotonic reasoning. This paper presents a system for non-monotonic reasoning on the Semantic Web called VDR-Device, which is capable of reasoning about RDF metadata over multiple Web sources using defeasible logic rules. It is implemented on top of the CLIPS production rule system and features a RuleML compatible syntax. The operational semantics of defeasible logic are implemented through compilation into a generic deductive rule language. Since the RuleML syntax may appear complex for many users, we have also implemented a graphical authoring tool for defeasible logic rules that acts as a shell for the defeasible reasoning system. The tool constrains the allowed vocabulary through analysis of the input RDF documents, so that the user does not have to type-in class and property names.
1st Asian Semantic Web Conference (ASWC'06) 2006
Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Such reasoning is useful in many Semantic Web applications, like policies, business rules, brokering, bargaining and agent negotiations. Nevertheless, defeasible logic is based on solid mathematical formulations and is, thus, not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions. Directed graphs can assist in confronting this drawback. They are a powerful and flexible tool of information visualization, offering a convenient and comprehensible way of representing relationships between entities. Their applicability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the arcs in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantage, by defining two distinct node types, for rules and atomic formulas, and four distinct connection types for each rule type in defeasible logic and for superiority relationships. The paper also briefly presents a tool that implements this representation methodology.
4th Hellenic Conference on Artificial Intelligence (SETN-06) 2006
Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Nevertheless, it is based on solid mathematical formulations and is not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions. Directed graphs (or digraphs) can assist in this affair, but their applicability is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the connections in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting their expressiveness, but also trying to counter their major disadvantage, by defining multiple node and connection types.
10th Panhellenic Conference on Informatics (PCI 2005) 2005
Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web, such as policies and business rules, agent brokering and negotiation, ontology and knowledge merging, etc. However, the syntax of defeasible logic may appear too complex for many users. In this paper we present a graphical authoring tool for defeasible logic rules that acts as a shell for the DR-DEVICE defeasible reasoning system over RDF metadata. The tool helps users to develop a rule base using the OO-RuleML syntax of DR-DEVICE rules, by constraining the allowed vocabulary through analysis of the input RDF namespaces, so that the user does not have to type-in class and property names. Rule visualization follows the tree model of RuleML. The DR-DEVICE reasoning system is implemented on top of the CLIPS production rule system and builds upon an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules.
4th International Semantic Web Conference (ISWC2005), Demo/Poster Session 2005
RuleML is a promising standardization effort for rule languages for the Semantic Web. However, the RuleML syntax may appear too complex for many users. Furthermore, the interplay between various Semantic Web technologies and languages impose a demand for using multiple, diverse tools for building rule-based applications for the Semantic Web. In this demonstration we present VDR-Device, a visual RuleML-compliant rule editor and an integrated development environment for developing and using defeasible logic rule bases on top of RDF ontologies. The visual rule editor constrains the allowed vocabulary through analysis of the input RDF ontologies. The development environment is supported by an RDF-aware defeasible reasoning system. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web, such as policies and business rules, agent brokering and negotiation, ontology and knowledge merging, etc., mainly due to interesting features, such as conflicting rules and rule priorities.
International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML-2005) 2005
Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web. However, the RuleML syntax of defeasible logic may appear too complex for many users. Furthermore, the interplay between various technologies and languages, such as defeasible reasoning, RuleML, and RDF impose a demand for using multiple, diverse tools for building rule-based applications for the Semantic Web. In this paper we present VDR-Device, a visual integrated development environment for developing and using defeasible logic rule bases on top of RDF ontologies. VDR-Device integrates in a user-friendly graphical shell, a visual RuleML-compliant rule editor that constrains the allowed vocabulary through analysis of the input RDF ontologies and a defeasible reasoning system that processes RDF data and RDF Schema ontologies.