Knowledge representation through graphs

Due to the increasing amount of data, knowledge aggregation, representation and reasoning are highly important for companies. In this paper, knowledge aggregation is presented as the first step. In the sequel, successful knowledge representation, for instance through graphs, enables knowledge-based reasoning. There exist various forms of knowledge representation through graphs; some of which allow to handle uncertainty and imprecision by invoking the technology of fuzzy sets. The paper provides an overview of different types of graphs stressing their relationships and their essential features.

Fuzzy knowledge representation in cognitive cities

This paper gives an insight into cognitive computing for smart cities, resulting in cognitive cities. Cognitive cities and cognitive computing research with the underlying concepts of knowledge graphs and fuzzy cognitive maps are presented and supported by existing tools (i.e., IBM Watson and Google Now) and intended tools (meta-app). The paper illustrates FCM as a suiting instrument to represent information/knowledge in a city environment driven by human-technology interaction, enforcing the concept of cognitive cities. A proposed paper prototype combines the findings of the paper and shows the next step in the implementation of the proposed meta-app.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

The FORA Framework - A Fuzzy Grassroots Ontology for Online Reputation Management

Online reputation management deals with monitoring and influencing the online record of a person, an organization or a product. The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly have a disastrous influence on the online reputation of some of the entities. This dissertation can be split into three parts: In the first part, possible fuzzy clustering applications for the Social Semantic Web are investigated. The second part explores promising Social Semantic Web elements for organizational applications,while in the third part the former two parts are brought together and a fuzzy online reputation analysis framework is introduced and evaluated. Theentire PhD thesis is based on literature reviews as well as on argumentative-deductive analyses.The possible applications of Social Semantic Web elements within organizations have been researched using a scenario and an additional case study together with two ancillary case studies—based on qualitative interviews. For the conception and implementation of the online reputation analysis application, a conceptual framework was developed. Employing test installations and prototyping, the essential parts of the framework have been implemented.By following a design sciences research approach, this PhD has created two artifacts: a frameworkand a prototype as proof of concept. Bothartifactshinge on twocoreelements: a (cluster analysis-based) translation of tags used in the Social Web to a computer-understandable fuzzy grassroots ontology for the Semantic Web, and a (Topic Maps-based) knowledge representation system, which facilitates a natural interaction with the fuzzy grassroots ontology. This is beneficial to the identification of unknown but essential Web data that could not be realized through conventional online reputation analysis. Theinherent structure of natural language supports humans not only in communication but also in the perception of the world. Fuzziness is a promising tool for transforming those human perceptions intocomputer artifacts. Through fuzzy grassroots ontologies, the Social Semantic Web becomes more naturally and thus can streamline online reputation management.

Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management

A social Semantic Web empowers its users to have access to collective Web knowledge in a simple manner, and for that reason, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. This chapter presents Fuzzy Cognitive Maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. With this in mind, a conceptual framework for Web knowledge aggregation, representation, and reasoning is introduced along with a use case, in which the importance of investigative searching for online privacy and reputation is highlighted. Thereby it is demonstrated how a user can establish a positive online presence.

Patterns of regional brain hypometabolism associated with knowledge of semantic features and categories in Alzheimer's disease

The study of semantic memory in patients with Alzheimer's disease (AD) has raised important questions about the representation of conceptual knowledge in the human brain. It is still unknown whether semantic memory impairments are caused by localized damage to specialized regions or by diffuse damage to distributed representations within nonspecialized brain areas. To our knowledge, there have been no direct correlations of neuroimaging of in vivo brain function in AD with performance on tasks differentially addressing visual and functional knowledge of living and nonliving concepts. We used a semantic verification task and resting 18-fluorodeoxyglucose positron emission tomography in a group of mild to moderate AD patients to investigate this issue. The four task conditions required semantic knowledge of (1) visual, (2) functional properties of living objects, and (3) visual or (4) functional properties of nonliving objects. Visual property verification of living objects was significantly correlated with left posterior fusiform gyrus metabolism (Brodmann's area [BA] 37/19). Effects of visual and functional property verification for non-living objects largely overlapped in the left anterior temporal (BA 38/20) and bilateral premotor areas (BA 6), with the visual condition extending more into left lateral precentral areas. There were no associations with functional property verification for living concepts. Our results provide strong support for anatomically separable representations of living and nonliving concepts, as well as visual feature knowledge of living objects, and against distributed accounts of semantic memory that view visual and functional features of living and nonliving objects as distributed across a common set of brain areas.

Sparse representation based action and gesture recognition

In this paper we present a solution to the problem of action and gesture recognition using sparse representations. The dictionary is modelled as a simple concatenation of features computed for each action or gesture class from the training data, and test data is classified by finding sparse representation of the test video features over this dictionary. Our method does not impose any explicit training procedure on the dictionary. We experiment our model with two kinds of features, by projecting (i) Gait Energy Images (GEIs) and (ii) Motion-descriptors, to a lower dimension using Random projection. Experiments have shown 100% recognition rate on standard datasets and are compared to the results obtained with widely used SVM classifier.

Reassembling a system from the sensor to cerebral representation: the olfactory system in vitro.

An odorant's code is represented by activity in a dispersed ensemble of olfactory sensory neurons in the nose, activation of a specific combination of groups of mitral cells in the olfactory bulb and is considered to be mapped at divergent locations in the olfactory cortex. We present here an in vitro model of the mammalian olfactory system developed to gain easy access to all stations of the olfactory pathway. Mouse olfactory epithelial explants are cocultured with a brain slice that includes the olfactory bulb and olfactory cortex areas and maintains the central olfactory pathway intact and functional. Organotypicity of bulb and cortex is preserved and mitral cell axons can be traced to their target areas. Calcium imaging shows propagation of mitral cell activity to the piriform cortex. Long term coculturing with postnatal olfactory epithelial explants restores the peripheral olfactory pathway. Olfactory receptor neurons renew and progressively acquire a mature phenotype. Axons of olfactory receptor neurons grow out of the explant and rewire into the olfactory bulb. The extent of reinnervation exhibits features of a postlesion recovery. Functional imaging confirms the recovery of part of the peripheral olfactory pathway and shows that activity elicited in olfactory receptor neurons or the olfactory nerves is synaptically propagated into olfactory cortex areas. This model is the first attempt to reassemble a sensory system in culture, from the peripheral sensor to the site of cortical representation. It will increase our knowledge on how neuronal circuits in the central olfactory areas integrate sensory input and counterbalance damage.

A Conceptual Model to Combine Creativity Techniques with Fuzzy Cognitive Maps for Enhanced Knowledge Management

This chapter introduces a conceptual model to combine creativity techniques with fuzzy cognitive maps (FCMs) and aims to support knowledge management methods by improving expert knowledge acquisition and aggregation. The aim of the conceptual model is to represent acquired knowledge in a manner that is as computer-understandable as possible with the intention of developing automated reasoning in the future as part of intelligent information systems. The formal represented knowledge thus may provide businesses with intelligent information integration. To this end, we introduce and evaluate various creativity techniques with a list of attributes to define the most suitable to combine with FCMs. This proposed combination enables enhanced knowledge management through the acquisition and representation of expert knowledge with FCMs. Our evaluation indicates that the creativity technique known as mind mapping is the most suitable technique in our set. Finally, a scenario from stakeholder management demonstrates the combination of mind mapping with FCMs as an integrated system.