Non-taxonomic relations between concepts appear as a major building block in common ontology definitions. In fact, their definition consumes much of the time needed for engineering an ontology. We here describe

We present a novel approach to the automatic acquisition of taxonomic relations. The main difference to earlier approaches is that we do not only consider one single source of evidence, i.e. a specific algorithm or approach, but examine the possibility of learning taxonomic relations by considering various and heterogeneous forms of evidence. In particular, we derive these different evidences by using well-known NLP techniques and resources and combine them via two simple strategies. Our approach shows very promising results compared to other results from the literature. The main aim of the work presented in this paper is (i) to gain insight into the behaviour of different approaches to learn taxonomic relations, (ii) to provide a first step towards combining these different approaches, and (iii) to establish a baseline for further research.

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.

Without the proliferation of formal semantic annotations, the Semantic Web is certainly doomed to failure. In earlier work we presented a new paradigm to avoid this: the ’Self Annotating Web’, in which globally available knowledge is used to annotate resources such as web pages. In particular, we presented a concrete method instantiating this paradigm, called PANKOW (Pattern-based ANnotation through Knowledge On the Web). In PANKOW, a named entity to be annotated is put into several linguistic patterns that convey competing semantic meanings. The patterns that are matched most often on the Web indicate the meaning of the named entity — leading to automatic or semi-automatic annotation. In this paper we present C-PANKOW (Context-driven PANKOW), which alleviates several shortcomings of PANKOW. First, by downloading abstracts and processing them off-line, we avoid the generation of large number of linguistic patterns and correspondingly large number of Google queries. Second, by linguistically analyzing and normalizing the downloaded abstracts, we increase the coverage of our pattern matching mechanism and overcome several limitations of the earlier pattern generation process. Third, we use the annotation context in order to distinguish the significance of a pattern match for the given annotation task. Our experiments show that C-PANKOW inherits all the advantages of PANKOW (no training required etc.), but in addition it is far more efficient and effective.

Using an object-relational data model and an XML datatype, we show how a relevant subset of XML documents and their implied structure can be mapped onto database structures. Besides straight-forward mappings, there are some XML structures that cannot be easily mapped onto database structures. These structures would sometimes result in large database schemas and sparsely populated databases. As a consequence, such XML document fragments should be mapped onto database attributes of type XML and kept as is. The XML datatype implementation should support evaluating path expressions and fulltext operations. We present an algorithm that finds a type of optimal mapping based on the XML Document Type Definition (DTD) and statistics. The statistics are derived from sample XML document sets and some knowledge about queries on XML document collections.

Creating metadata by annotating documents is one of the major techniques for putting machine understandable data on the Web. Though there exist many tools for annotating web pages, few of them fully support the creation of semantically interlinked metadata, such as necessary for a truely Semantic Web. In this paper, we present an ontology-based annotation environment, OntoAnnotate, which offers comprehensive support for the creation of semantically interlinked metadata by human annotators.

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We present sppc, a high-performance system for intelligent text extraction and navigation from German free text documents. The main purpose of sppc is to extract as much linguistic structure as possible for performing domain-specific processing. sppc consists of a set of domain-independent shallow core components which are realized by means of cascaded weighted finite state machines and generic dynamic tries. All extracted information is represented uniformly in one data structure (called the text chart) in a highly compact and linked form in order to support indexing and navigation through the set of solutions. German

We present 1 sppc, a high-performance system for intelligent extraction of structured data from free text documents. sppc consists of a set of domain-adaptive shallow core components which are realized by means of cascaded weighted finite state machines and generic dynamic tries. The system has been fully implemented for German which includes morphological and on-line compound analysis, e#cient POS-filtering, high performance named entity recognition and chunk parsing based on a novel divideand -conquer strategy. The whole approach proved to be very useful for processing of free word order languages like German. sppc has a good performance (more than 6000 words per second on standard PC environments) and achieves high linguistic coverage. Especially for the divide-and-conquer parsing strategy we obtained an f-measure of 87.14% on unseen data. Key words: natural language processing, shallow free text processing, German language, finite-state technology, information extract...

: Recently, the idea of semantic portals on the Web or on the intranet has gained popularity.