In this paper we present Text2Onto, a framework for ontology learning from textual resources. Three main features distinguish Text2Onto from our earlier framework TextToOnto as well as other state-of-the-art ontology learning frameworks. First, by representing the learned knowledge at a meta-level in the form of instantiated modeling primitives within a so called Probabilistic Ontology Model (POM), we remain independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism. Second, user interaction is a core aspect of Text2Onto and the fact that the system calculates a confidence for each learned object allows to design sophisticated visualizations of the POM. Third, by incorporating strategies for data-driven change discovery, we avoid processing the whole corpus from scratch each time it changes, only selectively updating the POM according to the corpus changes instead. Besides increasing e#ciency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.

Semantic Annotation is a basic technology for intelligent content and is beneficial in a wide range of contentoriented intelligent applications. In this paper we present our work in ontology-based semantic annotation, which is embedded in a scenario of a knowledge portal application. Starting with seemingly good and bad manual semantic annotation, we describe our experiences made within the KA²-initiative. The experiences gave us the starting point for developing an ergonomic and knowledge base-supported annotation tool. Furthermore, the annotation tool described are currently extended with mechanisms for semi-automatic information-extraction based annotation. Supporting the evolving nature of semantic content we additionally describe our idea of evolving ontologies supporting semantic annotation.

Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, eorts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We present a general architecture for discovering conceptual structures and engineering ontologies. Based on the architecture we propose a new approach to extend current approaches, who mostly focus on the semi-automatic acquisition of taxonomies, by the discovery of non-taxonomic conceptual relations. We use a generalized association rule algorithm that does not only detect relations between concepts, but also determines the appropriate level of abstraction at which to dene relations. 1 Introduction Ontologies 1 have shown their usefulness...

The focused access to knowledge resources like intranet documents plays a vital role in knowledge management and supports in general the shifting towards a Semantic Web. Ontologies act as a conceptual backbone for semantic document access by providing a common understanding and conceptualization of a domain. Building domain-specific ontologies is a time-consuming and expensive manual construction task. This paper describes our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. We present a comprehensive architecture and generic method for discovering a domain-tailored ontology from given intranet resources. 1 Introduction The amount of information available to corporate employees has grown drastically with the use of intranets. Unfortunately this growth of available information has made the access to useful or necessary information much more difficult due to the fact that the access is usually based on ...

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

... we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, KAON Text-To-Onto.

This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.

The core idea of the Semantic Web is to make information accessible to human and software agents on a semantic basis. Hence, Web sites may feed directly from the Semantic Web exploiting the underlying structures for human and machine access. We have developed a domain-independent approach for developing semantic portals, o/oov"## SEAL (SEmantic portAL), that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal. In this paper we focus on semanticsbased means that make semantic Web sites accessible from the outside, i.e. semantics-based browsing, semantic querying, querying with semantic similarity, and machine access to semantic information. In particular, we focus on methods for acquiring and structuring community information as well as methods for sharing information.

The core idea of the Semantic Web is to make information accessible to human and software agents on a semantic basis. Hence, web sites may feed directly from the Semantic Web exploiting the underlying structures for human and machine access. We have developed a generic approach for developing semantic portals, viz. SEAL (SEmantic portAL), that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal. In this paper, we discuss the role that semantic structures make for establishing communication between different agents in general. We elaborate on a number of intelligent means that make semantic web sites accessible from the outside, viz. semantics-based browsing, semantic querying and querying with semantic similarity, semantic personalization, and machine access to semantic information at a semantic portal. As a case study we refer to the AIFB web site --- a place that is increasingly driven by Semantic Web tec...