Concepts of Bitemporal Database Theory and the Evolution of Web-Documents

A vast amount of temporal information is provided on the Web. Even though many facts expressed in documents are time-related, the temporal properties of Web presentations have not received much attention. In database research, temporal databases have become a mainstream topic in recent years. In Web documents, temporal data may exist as meta data in the header and as user-directed data in the body of a document. Whereas temporal data can easily be identified in the semi-structured meta data, it is more difficult to determine temporal data and its role in the body. We propose procedures for maintaining temporal integrity of Web pages and outline different approaches of applying bitemporal data concepts for Web documents. In particular, we regard desirable functionalities of Web repositories and other Web-related tools that may support the Webmasters in managing the temporal data of their Web documents. Some properties of a prototype environment are described.

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.

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.