Fuzzy Quantified Queries to Fuzzy RDF Databases

International audienceIn a relational database context, fuzzy quantified queries have been long recognized for their ability to express different types of imprecise and flexible information needs. In this paper, we introduce the notion of fuzzy quantified statements in a (fuzzy) RDF database context. We show how these statements can be defined and implemented in FURQL, which is a fuzzy extension of the SPARQL query language that we previously proposed. Then, we present some experimental results that show the feasibility of this approach

Expression and Efficient Processing of Fuzzy Queries in a Graph Database Context

International audienceGraph databases have aroused a large interest in the last years thanks to their large scope of potential applications (e.g. social networks, biomedical networks, data stemming from the web). In a similar way as what has already been proposed in relational databases, defining a language allowing a flexible querying of graph databases may greatly improve usability of data. This paper focuses on the notion of fuzzy graph database and describes a fuzzy query language that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. This language, called FUDGE, can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. The FUDGE language is implemented in a system, called SUGAR, that we present in this article. We also discuss implementation issues of the FUDGE language in SUGAR

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Expression and Efficient Processing of Fuzzy Queries in a Graph Database Context

International audienceGraph databases have aroused a large interest in the last years thanks to their large scope of potential applications (e.g. social networks, biomedical networks, data stemming from the web). In a similar way as what has already been proposed in relational databases, defining a language allowing a flexible querying of graph databases may greatly improve usability of data. This paper focuses on the notion of fuzzy graph database and describes a fuzzy query language that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. This language, called FUDGE, can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. The FUDGE language is implemented in a system, called SUGAR, that we present in this article. We also discuss implementation issues of the FUDGE language in SUGAR

Keyword-Based Querying for the Social Semantic Web

Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis

Learning Class Disjointness Axioms Using Grammatical Evolution

International audienceoday, with the development of the Semantic Web, LinkedOpen Data (LOD), expressed using the Resource Description Frame-work (RDF), has reached the status of “big data” and can be consideredas a giant data resource from which knowledge can be discovered. Theprocess of learning knowledge defined in terms of OWL 2 axioms fromthe RDF datasets can be viewed as a special case of knowledge discov-ery from data or “data mining”, which can be called “RDF mining”.The approaches to automated generation of the axioms from recordedRDF facts on the Web may be regarded as a case of inductive reasoningand ontology learning. The instances, represented by RDF triples, playthe role of specific observations, from which axioms can be extracted bygeneralization. Based on the insight that discovering new knowledge isessentially an evolutionary process, whereby hypotheses are generatedby some heuristic mechanism and then tested against the available evi-dence, so that only the best hypotheses survive, we propose the use ofGrammatical Evolution, one type of evolutionary algorithm, for miningdisjointness OWL 2 axioms from an RDF data repository such as DBpe-dia. For the evaluation of candidate axioms against the DBpedia dataset,we adopt an approach based on possibility theory