A distributed, semiotic-inductive and human-Oriented approach to web-scale knowledge retrieval

Web-scale knowledge retrieval can be enabled by distributed information retrieval, clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e.,gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface.

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.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.

Assimilation and contrast in web product reviews: devaluing the recommendation of a proficient but dissimilar reviewer

When viewing web-consumer reviews consumers encounter the reviewers in an anonymous environment. Although their interactions are only virtual they still exchange social information, e.g. often reviewers refer to their proficiency or consumption motives within the review texts. Do these social information harm the viewers’ perception of the recommended products? The present study addresses this question by applying the paradigm of social comparison (Mussweiler, 2003) to web-consumer reviews. In a laboratory experiment with a student sample (n = 120) we manipulated the perceived similarity between reviewer and viewer and the perceived proficiency of the reviewer. A measurement of achievement goals (Elliott & McGregor, 2001) and average number of hours of study prior to the experiment allowed to introduce the reviewer as high [low] in proficiency and similar [dissimilar] in achievement goals. As predicted, the viewer’s evaluation of the recommended products differed as a function of this social information. Contrasting with the reviewer led to devaluing the products recommended by a proficient but dissimilar reviewer. However, against our prediction social comparison with the reviewer did not affect the viewer`s self-evaluation. Whether social information in web-product reviews affects the viewer`s self-evaluation and induces both social comparison processes remains an open question. Future studies aim to address this by manipulating the informational focus of the viewer, rather than the perceived similarity between viewer and reviewer. So far, the present study extends the application of social comparison to consumption environments and contributes to the understanding of the virtual social identity.

Keeping citizens, companies, and organizations informed by Web KnowARR

For the main part, electronic government (or e-government for short) aims to put digital public services at disposal for citizens, companies, and organizations. To that end, in particular, e-government comprises the application of Information and Communications Technology (ICT) to support government operations and provide better governmental services (Fraga, 2002) as possible with traditional means. Accordingly, e-government services go further as traditional governmental services and aim to fundamentally alter the processes in which public services are generated and delivered, after this manner transforming the entire spectrum of relationships of public bodies with its citizens, businesses and other government agencies (Leitner, 2003). To implement this transformation, one of the most important points is to inform the citizen, business, and/or other government agencies faithfully and in an accessible way. This allows all the partaking participants of governmental affairs for a transition from passive information access to active participation (Palvia and Sharma, 2007). In addition, by a corresponding handling of the participants' data, a personalization towards these participants may even be accomplished. For instance, by creating significant user profiles as a kind of participants' tailored knowledge structures, a better-quality governmental service may be provided (i.e., expressed by individualized governmental services). To create such knowledge structures, thus known information (e.g., a social security number) can be enriched by vague information that may be accurate to a certain degree only. Hence, fuzzy knowledge structures can be generated, which help improve governmental-participants relationship. The Web KnowARR framework (Portmann and Thiessen, 2013; Portmann and Pedrycz, 2014; Portmann and Kaltenrieder, 2014), which I introduce in my presentation, allows just all these participants to be automatically informed about changes of Web content regarding a- respective governmental action. The name Web KnowARR thereby stands for a self-acting entity (i.e. instantiated form the conceptual framework) that knows or apprehends the Web. In this talk, the frameworks respective three main components from artificial intelligence research (i.e. knowledge aggregation, representation, and reasoning), as well as its specific use in electronic government will be briefly introduced and discussed.

The Web KnowARR Framework: Orchestrating Computational Intelligence with Graph Databases

This chapter presents fuzzy cognitive maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. The corresponding Web KnowARR framework incorporates findings from fuzzy logic. To this end, a first emphasis is particularly on the Web KnowARR framework along with a stakeholder management use case to illustrate the framework’s usefulness as a second focal point. This management form is to help projects to acceptance and assertiveness where claims for company decisions are actively involved in the management process. Stakeholder maps visually (re-) present these claims. On one hand, they resort to non-public content and on the other they resort to content that is available to the public (mostly on the Web). The Semantic Web offers opportunities not only to present public content descriptively but also to show relationships. The proposed framework can serve as the basis for the public content of stakeholder maps.

FORA - A Fuzzy Set Based Framework for Online Reputation Management

The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly exhibit a disastrous influence on the online reputation of organizations. Based on social Web data, this study describes the building of an ontology based on fuzzy sets. At the end of a recurring harvesting of folksonomies by Web agents, the aggregated tags are purified, linked, and transformed to a so-called fuzzy grassroots ontology by means of a fuzzy clustering algorithm. This self-updating ontology is used for online reputation analysis, a crucial task of reputation management, with the goal to follow the online conversation going on around an organization to discover and monitor its reputation. In addition, an application of the Fuzzy Online Reputation Analysis (FORA) framework, lesson learned, and potential extensions are discussed in this article.

Applying Fuzzy Ontologies to Implement the Social Semantic Web

Because the knowledge in the World Wide Web is continuously expanding, Web Knowledge Aggregation, Representation and Reasoning (abbreviated as KR) is becoming increasingly important. This article demonstrates how fuzzy ontologies can be used in KR to improve the interactions between humans and computers. The gap between the Social and Semantic Web can be reduced, and a Social Semantic Web may become possible. As an illustrative example, we demonstrate how fuzzy logic and KR can enhance technologies for cognitive cities. The underlying notion of these technologies is based on connectivism, which can be improved by incorporating the results of digital humanities research.