Fuzzy Knowledge representation and reasoning

Obesity is associated with various diseases, particularly cardiovascular diseases, diabetes type 2, obstructive sleep apnea, certain types of cancer, osteoarthritis, and asthma. The knowledge of the obesity related cancer (ORC) domain is highly required to be represented in a structured and formalised shape. In this paper, we develop an ontology to represent ORC domain knowledge with its diseases, symptoms, diagnosis, and treatments. The proposed ontology is based on the Web Ontology Language (OWL 2) integrated with the fuzzy logic. The fuzzy developed ontology handles the overlapping concepts, ingesting more concepts, and copes with the linguistic domain variables, which were not possible using the regular ontologies. It allows the users to query the fuzzy Dl reasoner for element and answer them with the fuzzy ontology. By developing the fuzzy ORC ontology, it is expected to be a good practice for the ontologists and knowledge engineers.

Research interest within membrane computing is becoming increasingly interdisciplinary. In particular, one of the latest applications is fault diagnosis. The underlying mechanism was conceived by bridging spiking neural P systems with fuzzy rule-based reasoning systems. Despite having a number of publications associated with it, this research line still lacks a proper formalization of the foundations.

This paper aims to articulate the current approaches to handle the uncertainty problem in data mining (DM). The difficulties in DM are given to show the impact of the uncertainty problem in DM's various applications. As it has been stated that is no common DM method to handle the uncertainty problem in DM and based upon the literature of cognitive science, this paper highlights a new classification approach to overcome the uncertainty problem in DM that is the Relative-Fuzzy (RF) approach and its ML/RFL-Based Net software tool.

We present a prototype version of an ontology-based framework, called PROF-ONTO, that integrates IoT devices and users with domotic environments. PROF-ONTO is based on a novel OWL 2 ontology, called OASIS (Ontology for Agents, Systems, and Integration of Services), modelling behaviors of agents such as IoT devices and users, and other information concerning user requests, their executions, restrictions and authorizations. User requests are performed by automatically selecting compatible devices: agents expose their behaviors and are invoked accordingly to what they are able to do on specific categories of components. OASIS is also used to build semantic knowledge bases that operate as transparent communication and information exchange systems among agents.

As the Internet of Things evolves, attention has been paid to adding intelligence capabilities, some of them via semantic technologies. However, many of these developments are still too device-centric and more needs to be done to make the IoT more people-centric. One important aspect in this direction is supporting spatial constructs that are meaningful to humans. We present the OpenThings platform as a basis to provide meaning via semantic technologies and demonstrate how it can be used to build environments that take into account a human-centered view of temporal and spatial context. We exemplify this by building a prototype for a system that supports proxemic interactions and time awareness.

Ontology is the core of the semantic applications, so the quality of it has a direct proportion with Ontology. It impacts directly on the long life of the semantic applications. There are different negative effects in the design of Ontology' classes as Blob, Lazy Class, Large Class, and Singleton. These negative effects called bad smells. However, detecting smells is not supported in any Ontology editors. This paper proposes a metric method called ONTOPYTHO. It can detect classes' smells automatically from Ontology models even if it is a Big Ontology. We programed ONTOPYTHO via Python and SPARQL languages. We evaluated the proposed method ONTOPYTHO by applying it on twelve publicly OWL Ontology projects. The detected smells appeared 117495 times in the twelve projects. The results showed that both the size and the number of classes of OWL Ontology has no effect in the presence of the smells. The results also showed that 69.24% of the classes are lazy classes. This means that big OWL Ontologies are not big in their nature, but because of theses lazy classes. The proposed method is the first method that detects Lazy class smell in the design of big OWL Ontology. In our random sample of big Ontologies, Lazy class smell appears approximately 99.8% of the smells.

As the Internet of Things evolves, attention has been paid to adding intelligence capabilities, some of them via semantic technologies. However, many of these developments are still too device-centric and more needs to be done to make the IoT more people-centric. One important aspect in this direction is supporting spatial constructs that are meaningful to humans. We present the OpenThings platform as a basis to provide meaning via semantic technologies and demonstrate how it can be used to build environments that take into account a human-centered view of temporal and spatial context. We exemplify this by building a prototype for a system that supports proxemic interactions and time awareness.

As the Internet of Things evolves, attention has been paid to adding intelligence capabilities, some of them via semantic technologies. However, many of these developments are still too device-centric and more needs to be done to make the IoT more people-centric. One important aspect in this direction is supporting spatial constructs that are meaningful to humans. We present the OpenThings platform as a basis to provide meaning via semantic technologies and demonstrate how it can be used to build environments that take into account a human-centered view of temporal and spatial context. We exemplify this by building a prototype for a system that supports proxemic interactions and time awareness.

We present a Linked Data analysis method which relies on knowledge patterns for constructing a logical architecture of the knowledge in a dataset. This can then be exploited to compare heterogeneous datasets, enhance interoperability between them and make implicit knowledge emerge.

The need to address the challenge of vagueness across several domains of applicability of ontology is gaining research attention. The presence of vagueness in knowledge represented with description logic impairs automating reasoning and inference making. The importance of reducing this vagueness in the formalization of medical knowledge representation is rising, considering the vulnerability of this domain to the expression of vague concepts or terms. This vagueness may be addressed from the perspective of ontology modeling language application such as ontology web language (OWL). Although several attempts have been made to tackle this problem in other disease prognoses such as diabetes and cardiovascular diseases, a similar effort is missing for breast cancer. Minimizing vagueness in breast cancer ontology is necessary to enhance automated reasoning and handle knowledge representation problems. This study proposes a framework for reducing vagueness in breast cancer ontology. The approach obtained breast cancer crisp ontology and applied fuzzy ontology elements based on the Fuzzy OWL2 model to formulate breast cancer fuzzy ontology. This was achieved by extending the elements of OWL2 (a more expressive version of OWL) with annotation properties to fuzzify the breast cancer crisp ontology. Results obtained showed a significant reduction of vagueness in the domain, yielding 0.38 for vagueness spread and 1.0 for vagueness explicitness. In addition, ontology metrics such as completeness, consistency, correctness and accuracy were also evaluated, and we obtained impressive performance. The implication of this result is the reduction of vagueness in breast cancer ontology, which provides increased computational reasoning support to applications using the ontology.

Current Computer Vision systems are expected to allow for the management of data acquired by physically distributed cameras. This is especially the case for modern surveillance systems, which require communication between components and a combination of their outputs in order to obtain a complete view of the scene. Information fusion techniques have been successfully applied in this area, but several problems remain unsolved. One of them is the increasing need for coordination and cooperation between independent and heterogeneous cameras. A solution to achieve an understanding between them is to use a common and well-defined message content vocabulary. In this research work, we present a formal ontology aimed at the symbolic representation of visual data, mainly detected tracks corresponding to real-world moving objects. Such an ontological representation provides support for spontaneous communication and component interoperability, increases system scalability and facilitates the development of high-level fusion procedures. The ontology is used by the agents of Cooperative Surveillance Multi-Agent System, our multi-agent framework for multi-camera surveillance systems.

This paper contains description of such knowledge representation model as Object-Oriented Dynamic Network (OODN), which gives us an opportunity to represent knowledge, which can be modified in time, to build new relations between objects and classes of objects and to represent results of their modifications. The model is based on representation of objects via their properties and methods. It gives us a possibility to classify the objects and, in a sense, to build hierarchy of their types. Furthermore, it enables to represent relation of modification between concepts, to build new classes of objects based on existing classes and to create sets and multisets of concepts. OODN can be represented as a connected and directed graph, where nodes are concepts and edges are relations between them. Using such model of knowledge representation, we can consider modifications of knowledge and movement through the graph of network as a process of logical reasoning or finding the right solutions o...

Ambient assisted living (AAL) makes it possible to build assistance for older adults according to the person’s context. Understanding the person’s context sometimes involves transforming one’s home into a smart home. Typically, this is carried out using nonintrusively distributed sensors and calm technologies. Older adults often have difficulty performing activities of daily living, such as taking medication, drinking coffee, watching television, using certain electronic devices, and dressing. This difficulty is even greater when these older adults suffer from cognitive impairments. Defining an assistance solution requires a multidisciplinary and iterative collaborative approach. It is necessary, therefore, to reason about the imperatives and solutions of this multidisciplinary collaboration (e.g., clinical), as well as the adaptation of technical constraints (e.g., technologies). A common approach to reasoning is to represent knowledge using logic-based formalisms, such as ontologi...

This paper contains description of such knowledge representation model as Object-Oriented Dynamic Network (OODN), which gives us an opportunity to represent knowledge, which can be modified in time, to build new relations between objects and classes of objects and to represent results of their modifications. The model is based on representation of objects via their properties and methods. It gives us a possibility to classify the objects and, in a sense, to build hierarchy of their types. Furthermore, it enables to represent relation of modification between concepts, to build new classes of objects based on existing classes and to create sets and multisets of concepts. OODN can be represented as a connected and directed graph, where nodes are concepts and edges are relations between them. Using such model of knowledge representation, we can consider modifications of knowledge and movement through the graph of network as a process of logical reasoning or finding the right solutions o...

Current Computer Vision systems are expected to allow for the management of data acquired by physically distributed cameras. This is especially the case for modern surveillance systems, which require communication between components and a combination of their outputs in order to obtain a complete view of the scene. Information fusion techniques have been successfully applied in this area, but several problems remain unsolved. One of them is the increasing need for coordination and cooperation between independent and heterogeneous cameras. A solution to achieve an understanding between them is to use a common and well-defined message content vocabulary. In this research work, we present a formal ontology aimed at the symbolic representation of visual data, mainly detected tracks corresponding to real-world moving objects. Such an ontological representation provides support for spontaneous communication and component interoperability, increases system scalability and facilitates the development of high-level fusion procedures. The ontology is used by the agents of Cooperative Surveillance Multi-Agent System, our multi-agent framework for multi-camera surveillance systems.

In this report we present an introductory overview of ontological engineering from the informatics (computer science) perspective. The term "ontology" is inherited from philosophy,in which it is a branch of metaphysics concerned with the nature of being. Ontologies are knowledge management and representation techniques that can be analyzed theoretically using concepts from description logic for Web Ontology Language. Ontological engineering paradigm,have become an efficient methodology for knowledge representation and management in many intelligent information systems and e-technologies for all domains and tasks.

The process mining (PM) field combines techniques from computational intelligence which has been lately considered to encompass artificial intelligence (AI) or even the latter, augmented intelligence (AIs) systems, and the data mining (DM) to process modelling in order to analyze event logs. To this end, this paper presents a semantic-based process mining framework (SPMaAF) that exhibits high level of accuracy and conceptual reasoning capabilities particularly with its application in real world settings. The proposed framework proves useful towards the extraction, semantic preparation, and transformation of events log from any domain process into minable executable formats-with focus on supporting the further process of discovering, monitoring and improvement of the extracted processes through semantic-based analysis of the discovered models. Practically, the implementation of the proposed framework demonstrates the main contribution of this paper; as it presents a Semantic-Fuzzy mining approach that makes use of labels (i.e. concepts) within event logs about a domain process using a case study of the Learning Process. The paper provides a method which aims to allow for mining and improved analysis of the resulting process models through semantic-labelling (annotation), representation (ontology) and reasoning (reasoner). Consequently, the series of experimentations and semantically motivated algorithms shows that the proposed framework and its main application in real-world has the capacity of enhancing the PM results or outcomes from the syntactic to a much more abstraction levels.

In this article, joint medical and data analysis expertise is brought to bear using fuzzy knowledge representation and 'intelligent' aggregation techniques to solve a difficult medical diagnosis problem, that of sleep apnea syndrome screening.

The process mining (PM) field combines techniques from computational intelligence which has been lately considered to encompass artificial intelligence (AI) or even the latter, augmented intelligence (AIs) systems, and the data mining (DM) to process modelling in order to analyze event logs. To this end, this paper presents a semantic-based process mining framework (SPMaAF) that exhibits high level of accuracy and conceptual reasoning capabilities particularly with its application in real world settings. The proposed framework proves useful towards the extraction, semantic preparation, and transformation of events log from any domain process into minable executable formats-with focus on supporting the further process of discovering, monitoring and improvement of the extracted processes through semantic-based analysis of the discovered models. Practically, the implementation of the proposed framework demonstrates the main contribution of this paper; as it presents a Semantic-Fuzzy mining approach that makes use of labels (i.e. concepts) within event logs about a domain process using a case study of the Learning Process. The paper provides a method which aims to allow for mining and improved analysis of the resulting process models through semantic-labelling (annotation), representation (ontology) and reasoning (reasoner). Consequently, the series of experimentations and semantically motivated algorithms shows that the proposed framework and its main application in real-world has the capacity of enhancing the PM results or outcomes from the syntactic to a much more abstraction levels.

This paper reports on the lesson learned from setting up and running a course on smart spaces based ambient intelligence within a "postgraduate" degree in Information & Computer Science Engineering. The course consists of a lab module where students are asked to propose, develop and demonstrate simple smart environment applications. Specifically, the course is built around Smart-M3, the open source interoperability component of an open innovation platform developed within the European Project SOFIA (2009-11). This paper illustrates the above summarized course, presenting the design style and the application development flow specifically devised for the course, the students evaluation approach taken, the course achievements, the issues that still need to be handled, and it will envisage topics and further extensions.

With the popularization of computers and advancement of information technology (IT), there has been a rapid growth in the application of IT, thus greatly changed living habits, consumption behaviors, and health awareness of the modern society. Meanwhile, the demand is ever increasing for healthcare services based on information and knowledge that lead to personal health attention by people gradually. This research aims to design and develop an ontology driven knowledge-base system for generating personalized Diet-Aid web services based on standard Health Level Seven (HL7) health screening data. The system provides intelligent, personalized web services of Diet plan for users through an ontological knowledge engine. The system developed is capable of generating a dietetic plan and filter out unsuitable food on site according to user's health conditions and the accessible recipe information. The REST-based Diet-Aid web services can be accessed service ubiquitously by using any Internet-enabled device.

Medical Ontologies play a central role in integrating heterogeneous databases of various model organisms. The Hepatobiliary system is very important for human vital processes. It has an ability to regulate the other systems. Furthermore, it plays an important role in many body functions like protein production. Also, it may be affected by many pathologic conditions, which affect other organs negatively.. This paper investigates the current studies on Ontology-based approach and Ontology techniques for Hepatobiliary System Diseases. We present conceptual view for the Hepatobiliary system and its infected diseases. Besides, we propose a new classification schema for the research efforts investigated so far. We classified the research efforts investigated so far based on the Hepatobiliary system organs: Liver, Gallbladder, Bile duct and Pancreas. Besides, we discuss the current research gaps found in this research area. widely acknowledged that Ontologies can make a significant contribution to the design and implementation of information systems in the medical field. On the other hand, human body systems consist of specific cells, tissues, and organs that work together to perform specific functions. Conceptually, these systems are interconnected and dependent, so they can't work separately, such as (Nervous System, Respiratory System, Immune System, Digestive System and Hepatobiliary System) . The Hepatobiliary system includes four organs (Liver, Gallbladder, Bile duct and Pancreas). Where, Hepatobiliary is the one of the important systems in the human body. It is responsible for lots of processes inside the body. These processes are important to keep body regulated and healthy. Conceptually, it plays an important role in many body functions like protein production. It is also responsible for detoxification, metabolism, synthesis, and storage of various substances, however, this vital system may be affected by hazardous conditions

Data segmentation plays a critical role in performing human activity recognition (HAR) in the ambient assistant living (AAL) systems. It is particularly important for complex activity recognition when the events occur in short bursts with attributes of multiple sub-tasks. Although past efforts were made in segmenting the real-time sensor data stream such as static/dynamic window sizing approaches, little has been explored to use the description of the activity of daily living (ADL) to support generic/user-specific preferences at segmentation stage. Therefore, this paper proposes semanticbased segmentation approach which uses ontology to perform terminology-box (T-Box) and assertion-box (A-Box) reasoning, along with logical rules to infer whether the incoming sensor event is related to a given sequences of the activity. A use-case scenario is used to illustrate how the proposed approach conducts semantic segmentation of real-time sensor data stream to recognise an elderly persons complex activities.

In this paper we address two problems. The first pertains to implementation of medical guidelines in an e-health system supporting self-management of chronic diseases. The system allows patients to enter observed symptoms and measured parameters, then makes assessment of disease state and informs about necessary actions. We propose to formalize guidelines as sets of fuzzy rules. Fuzziness is introduced to cope with uncertainty resulting from self-observations bias, low quality of sensors and limited patients skills. The second problem is more general. It concerns the reuse of knowledge gathered in ontologies and an application of Semantic Web technologies to perform fuzzy inference. We show that, despite the fact that commonly used ontology languages and supporting tools are not intended to handle vagueness and uncertainty, they can be successfully integrated to represent and execute a set of fuzzy rules. The proposed method consists in refactoring a domain ontology, then introducing additional relations expressing fuzzy properties, encoding Mamdani fuzzy rules in SWRL language and executing them with use of Pellet OWL reasoner. We describe a fuzzy reasoning engine applying this approach and discuss translation of fuzzy rules to SWRL constructs taking as example a complete set of rules formalizing a medical guideline for asthma control assessment.

Query-driven information retrieval aims at supporting users to request and retrieve relevant data from sensor networks. Due to energy and capacity constraints that characterize sensor networks, information retrieval should avoid flooding the network with queries, but rather find the most efficient propagation path that maximizes the recall of relevant data while minimizing the number of sensor nodes being accessed. This is the problem of query propagation, for which numerous approaches for sensor networks have been proposed. Although, one unaddressed issue that remains is the issue of fuzziness of users' queries and fuzziness of sensor data. When crisp criteria are used to express queries and select query recipients during propagation, some sensor nodes that are relevant can be "missed." Therefore, this paper's objective is to integrate a fuzzy semantic mapping mechanism, which has been published in a previous research, into a new, cluster- based fuzzy query propag...

Over the last decade a number of technologies have been developed that support individuals in keeping themselves active. This can be done via e-coaching mechanisms and by installing more advanced technologies in their homes. The objective of the Active Healthy Ageing (AHA) Platform is to integrate existing tools, hardware, and software that assist individuals in improving and/or maintaining a healthy lifestyle. This architecture is realized by integrating several hardware/software components that generate various types of data. Some examples include heart-rate data, coaching information, in-home activity patterns, mobility patterns, and so on. Various subsystems in the AHA platform can share their data in a semantic and interoperable way, through the use of a AHA data-store and a wearable devices ontology. This paper presents such an ontology for wearable data interoperability in Ambient Assisted Living environments. The ontology includes concepts such as height, weight, locations, ...

The benefits of autonomous data exchange in software environments and multi-device communication have triggered the development of various middleware services to enhance data exchange in distributed systems. These middleware solutions have gained great importance in reducing overall system complexity and enabling interoperability in the context of information sharing. This paper takes a closer look at two data exchange middleware solutions: The Smart-M3 platform and VSL overlay for Internet of Things. They both solve key challenges in the environment of device heterogeneity and propose a datacentric approach to information exchange. We explain the core differences in their key features and give an overview of their field of application.

Machine activity recognition aims to automatically predict human activities from a series of sensor signals. It is a key aspect to several emerging applications, especially in the pervasive computing field. However, this problem faces several challenges due to the complex, relational and ambiguous nature of human activities. These challenges still defy the majority of traditional pattern recognition approaches, whether they are knowledge-based or data-driven. Concretely, the current approaches to activity recognition in sensor environments fall short to represent, reason or learn under uncertainty, complex relational structure, rich temporal context and abundant common-sense knowledge. Motivated by these shortcomings, our work focuses on the combination of both data-driven and knowledge-based paradigms in order to address this problem. In particular, we propose two logic-based statistical relational activity recognition frameworks which we describe in two different parts. The first ...

Although the Semantic Web data standards are established, ontology-based applications built on the standards are relatively limited. This is partly due to high learning curve and efforts demanded in building ontology-based Semantic Web applications. In this paper, we describe an ontology application management (OAM) framework that aims to simplify creation and adoption of ontology-based application that is based on the Semantic Web technology. OAM introduces an intermediate layer between user application and programming and development environment in order to support ontology-based data publishing and access, abstraction and interoperability. The framework focuses on providing reusable and configurable data and application templates, which allow the users to create the applications without programming skill required. Three forms of templates are introduced: database to ontology mapping configuration, recommendation rule and application templates. We describe two case studies that ad...

Over the past 30 years, information technology has gradually transformed the way health care is provisioned for patients. Chronic Obstructive Pulmonary Disease (COPD) is an incurable malady that threatens the lives of millions around the world. The huge amount of medical information in terms of complex interdependence between progression of health problems and various other factors makes the representation of data more challenging. This study investigated how formal semantic standards could be used for building an ontology knowledge repository to provide ubiquitous healthcare and medical recommendations for COPD patient to reduce preventable harm. The novel contribution of the suggested framework resides in the patient-centered monitoring approach, as we work to create dynamic adaptive protection services according to the current context of patient. This work executes a sequential modular approach consisting of patient, disease, location, devices, activities, environment and service...

There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty.<br>This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic.<br>T...

We present, FT-SWRL, a fuzzy temporal extension to the Semantic Web Rule Language (SWRL), which combines fuzzy theories based on the valid-time temporal model to provide a standard approach for modeling imprecise temporal domain knowledge in OWL ontologies. The proposal introduces a fuzzy temporal model for the semantic web, which is syntactically defined as a fuzzy temporal SWRL ontology (SWRL-FTO) with a new set of fuzzy temporal SWRL built-ins for defining their semantics. The SWRL-FTO hierarchically defines the necessary linguistic terminologies and variables for the fuzzy temporal model. An example model demonstrating the usefulness of the fuzzy temporal SWRL built-ins to model imprecise temporal information is also represented. Fuzzification process of interval-based temporal logic is further discussed as a reasoning paradigm for our FT-SWRL rules, with the aim of achieving a complete OWL-based fuzzy temporal reasoning. Literature review on fuzzy temporal representation approa...

Linked Open Data in smart cities are an emerging source of open information comprising the needs and requirements of the citizens. However, there are some limitations when it is necessary to store data in sets of different abstraction and aggregation levels. In this paper, we present a new approach to provide smart cities with a tool for storing the data from citizens that provides the ability to select the type of abstraction or aggregation level required in each moment. In addition, the paper presents a novel way to share semantic data following the Linked Open Data paradigm by using a rule engine based reasoner inferring new statements based on spatio-temporal rules.

Over the past 30 years, information technology has gradually transformed the way health care is provisioned for patients. Chronic Obstructive Pulmonary Disease (COPD) is an incurable malady that threatens the lives of millions around the world. The huge amount of medical information in terms of complex interdependence between progression of health problems and various other factors makes the representation of data more challenging. This study investigated how formal semantic standards could be used for building an ontology knowledge repository to provide ubiquitous healthcare and medical recommendations for COPD patient to reduce preventable harm. The novel contribution of the suggested framework resides in the patient-centered monitoring approach, as we work to create dynamic adaptive protection services according to the current context of patient. This work executes a sequential modular approach consisting of patient, disease, location, devices, activities, environment and service...

Context aware computing as well as wearable and ubiquitous computing often attain with pattern recognition on incoming sensor data. Recognizing more (useful) contexts requires more information about the context, and thus more sensors and better recognition algorithms. In order to enable logic inference on incoming data, the proposed work assumes that incoming data are represented by means of semantic languages (e.g., RDF, OWL, etc.). Nevertheless, in a context aware computing purely logic-based reasoning on context may not be enough. So, the work introduces soft computing techniques to approximate context recognition. Specifically, this paper introduces an approach to context analysis and recognition that relies on f-SPARQL[1] tool, that is a flexible extension of SPARQL. In particular, in this work a JAVA implementation of f-SPARQL and the integrated support for fuzzy clustering and classification are discussed. This tool is exploited in the architecture that foresees some task ori...

Automation of smart home for ambient assisted living is currently based on a widespread use of sensors. As efficient as it seems to be, this solution can sometimes be problematic when one focus on user acceptability intimately related to cost and intrusivity. In this paper, we propose a context-aware system based on the semantic analysis of each user request at runtime. Our goal is to infer user data usually sensored by using advanced semantic web tools to adapt home automation services to people with special needs. To take up this challenge, an ontology, automatically derived from a model-driven process, firstly defines usersystem interactions. Then, the use of rules allows an inference engine to deduce user location and intention leading to adapted service delivery.

We present a Linked Data analysis method which relies on knowledge patterns for constructing a logical architecture of the knowledge in a dataset. This can then be exploited to compare heterogeneous datasets, enhance interoperability between them and make implicit knowledge emerge.

Abstract—Pervasive computing introduces complex systems consisting of many users, sensors, and applications that should react to context data, provide services, and manipulate devices in a predictable and reliable manner. Context data sensed from the environment is largely uncertain due to lack of precision and imperfect or faulty sensors. Uncertainty is generally dealt with at the level of individual context data. Due to the difficulties associated with catering for data of such fine granularity in applications, the environment can be ...

Ontology is the core of the semantic applications, so the quality of it has a direct proportion with Ontology. It impacts directly on the long life of the semantic applications. There are different negative effects in the design of Ontology' classes as Blob, Lazy Class, Large Class, and Singleton. These negative effects called bad smells. However, detecting smells is not supported in any Ontology editors. This paper proposes a metric method called ONTOPYTHO. It can detect classes' smells automatically from Ontology models even if it is a Big Ontology. We programed ONTOPYTHO via Python and SPARQL languages. We evaluated the proposed method ONTOPYTHO by applying it on twelve publicly OWL Ontology projects. The detected smells appeared 117495 times in the twelve projects. The results showed that both the size and the number of classes of OWL Ontology has no effect in the presence of the smells. The results also showed that 69.24% of the classes are lazy classes. This means that big OWL Ontologies are not big in their nature, but because of theses lazy classes. The proposed method is the first method that detects Lazy class smell in the design of big OWL Ontology. In our random sample of big Ontologies, Lazy class smell appears approximately 99.8% of the smells.

Current paper investigates how user modeling, context awareness and content adaptation in Smart Home environments can be handled formally in order to capture the semantics that emerge from a newly introduced user experience: SandS is in fact a complete ecosystem of users within a social network, creating and exchanging content in the form of so-called recipes and developing a collective intelligence which adapts its operation through appropriately feedback provided by the user. We will approach SandS from the user's perspective and illustrate how users and their relationships can be modeled through a number of fuzzy stereotypical profiles. Additionally, context modeling in pervasive computing systems and especially in the Smart Home paradigm will be examined through appropriate representation of context cues in the overall interaction. Finally we will investigate how users and system services although using languages of different semantic expressiveness can inter-operate successfully thanks to appropriate knowledge-based expert mappings.

In this article, joint medical and data analysis expertise is brought to bear using fuzzy knowledge representation and 'intelligent' aggregation techniques to solve a difficult medical diagnosis problem, that of sleep apnea syndrome screening.

Obesity is associated with various diseases, particularly cardiovascular diseases, diabetes type 2, obstructive sleep apnea, certain types of cancer, osteoarthritis, and asthma. The knowledge of the obesity related cancer (ORC) domain is highly required to be represented in a structured and formalised shape. In this paper, we develop an ontology to represent ORC domain knowledge with its diseases, symptoms, diagnosis, and treatments. The proposed ontology is based on the Web Ontology Language (OWL 2) integrated with the fuzzy logic. The fuzzy developed ontology handles the overlapping concepts, ingesting more concepts, and copes with the linguistic domain variables, which were not possible using the regular ontologies. It allows the users to query the fuzzy Dl reasoner for element and answer them with the fuzzy ontology. By developing the fuzzy ORC ontology, it is expected to be a good practice for the ontologists and knowledge engineers.

Computer Interpretable Guidelines (CIG) have largely contributed to the simplification and dissemination of clinical guidelines. However, the formalization of CIG contents, especially care actions, is still an open issue. Actually, this information, which is the heart of the guideline, is still expressed as free text and therefore prevents the development of intelligent tools for assisting physicians defining treatments. In this paper, we introduce a framework for formalizing care actions using natural language processing techniques, Semantic Web technologies and medical standards.

The advent and wide proliferation of Social Web in the recent years has promoted the concept of social interaction as an important influencing factor of the way enterprises and organizations conduct business. Among the fields influenced is that of Enterprise Knowledge Management, where the increase and maintenance of the employees' active participation in the organization's knowledge management activities is pursued through the adoption of social computing approaches. In this paper we consider a prominent and increasingly applied such approach, namely enterprise microblogging, and we propose a novel way to exploit its knowledge generation and sharing capabilities in order to effectively capture and formalize enterprise knowledge that is vague. Application and empirical evaluation of the framework indicates significant potential towards this goal.

Balanced scorecard is a widely recognized tool to support decision making in business management. Unfortunately, current balanced scorecard-based systems present two drawbacks: they do not allow to define explicitly the semantics of the underlying knowledge and they are not able to deal with imprecision and vagueness. To overcome these limitations, in this paper we propose a semantic fuzzy expert system which implements a generic framework for the balanced scorecard. In our approach, knowledge about balanced scorecard variables is represented using an OWL ontology, therefore allowing reuse and sharing of the model among different companies. The ontology acts as the basis for the fuzzy expert system, which uses highly interpretable fuzzy IF-THEN rules to infer new knowledge. Results are valuable pieces of information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be customized to adapt to different scenarios.

Current paper investigates how user modeling, context awareness and content adaptation in Smart Home environments can be handled formally in order to capture the semantics that emerge from a newly introduced user experience: SandS is in fact a complete ecosystem of users within a social network, creating and exchanging content in the form of so-called recipes and developing a collective intelligence which adapts its operation through appropriately feedback provided by the user. We will approach SandS from the user's perspective and illustrate how users and their relationships can be modeled through a number of fuzzy stereotypical profiles. Additionally, context modeling in pervasive computing systems and especially in the Smart Home paradigm will be examined through appropriate representation of context cues in the overall interaction. Finally we will investigate how users and system services although using languages of different semantic expressiveness can inter-operate successfully thanks to appropriate knowledge-based expert mappings.

The advent and wide proliferation of Social Web in the recent years has promoted the concept of social interaction as an important influencing factor of the way enterprises and organizations conduct business. Among the fields influenced is that of Enterprise Knowledge Management, where the increase and maintenance of the employees' active participation in the organization's knowledge management activities is pursued through the adoption of social computing approaches. In this paper we consider a prominent and increasingly applied such approach, namely enterprise microblogging, and we propose a novel way to exploit its knowledge generation and sharing capabilities in order to effectively capture and formalize enterprise knowledge that is vague. Application and empirical evaluation of the framework indicates significant potential towards this goal.

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[48]) are based on a purely con-nectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter Gärdenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by Gärdenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs.