Johanna Völker

Text2Onto: a framework for ontology learning and data-driven change discovery

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 efficiency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.

Ontology learning and reasoning: dealing with uncertainty and inconsistency

Ontology learning aims at generating domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques. It is inherent to the ontology learning process that the acquired ontologies represent uncertain and possibly contradicting knowledge. From a logical perspective, the learned ontologies are potentially inconsistent knowledge bases, that as such do not allow for meaningful reasoning. In this paper, we present an approach to generating consistent OWL ontologies from automatically generated or enriched ontology models, which takes into account the uncertainty of the acquired knowledge. We illustrate and evaluate the application of our approach with two experiments in the scenarios of consistent evolution of learned ontologies and enrichment of ontologies with disjointness axioms.

Automatic evaluation of ontologies (AEON)

OntoClean is a unique approach towards the formal evaluation of ontologies, as it analyses the intensional content of concepts. Although it is well documented in numerous publications, and its importance is widely acknowledged, it is still used rather infrequently due to the high costs for applying OntoClean, especially on tagging concepts with the correct meta-properties. In order to facilitate the use of OntoClean and to enable proper evaluation of it in real-world cases, we provide AEON , a tool which automatically tags concepts with appropriate OntoClean meta-properties. The implementation can be easily expanded to check the concepts for other abstract meta-properties, thus providing for the first time tool support in order to enable intensional ontology evaluation for concepts. Our main idea is using the web as an embodiment of objective world knowledge, where we search for patterns indicating concepts meta-properties. We get an automatic tagging of the ontology, thus reducing costs tremendously. Moreover, AEON lowers the risk of having subjective taggings. As part of the evaluation we report our experiences from creating a middle-sized OntoClean-tagged reference ontology.

Acquisition of OWL DL Axioms from Lexical Resources

State-of-the-art research on automated learning of ontologies from text currently focuses on inexpressive ontologies. The acquisition of complex axioms involving logical connectives, role restrictions, and other expressive features of the Web Ontology Language OWL remains largely unexplored. In this paper, we present a method and implementation for enriching inexpressive OWL ontologies with expressive axioms which is based on a deep syntactic analysis of natural language definitions. We argue that it can serve as a core for a semi-automatic ontology engineering process supported by a methodology that integrates methods for both ontology learning and evaluation. The feasibility of our approach is demonstrated by generating complex class descriptions from Wikipedia definitions and from a fishery glossary provided by the Food and Agriculture Organization of the United Nations.

Deployment of RDFa, Microdata, and Microformats on the Web A Quantitative Analysis

More and more websites embed structured data describing for instance products, reviews, blog posts, people, organizations, events, and cooking recipes into their HTML pages using markup standards such as Microformats, Microdata and RDFa. This development has accelerated in the last two years as major Web companies, such as Google, Facebook, Yahoo!, and Microsoft, have started to use the embedded data within their applications. In this paper, we analyze the adoption of RDFa, Microdata, and Microformats across the Web. Our study is based on a large public Web crawl dating from early 2012 and consisting of 3 billion HTML pages which originate from over 40 million websites. The analysis reveals the deployment of the different markup standards, the main topical areas of the published data as well as the different vocabularies that are used within each topical area to represent data. What distinguishes our work from earlier studies, published by the large Web companies, is that the analyzed crawl as well as the extracted data are publicly available. This allows our findings to be verified and to be used as starting points for further domain-specific investigations as well as for focused information extraction endeavors.

A Kernel Revision Operator for Terminologies -- Algorithms and Evaluation

Revision of a description logic-based ontology deals with the problem of incorporating newly received information consistently. In this paper, we propose a general operator for revising terminologies in description logic-based ontologies. Our revision operator relies on a reformulation of the kernel contraction operator in belief revision. We first define our revision operator for terminologies and show that it satisfies some desirable logical properties. Second, two algorithms are developed to instantiate the revision operator. Since in general, these two algorithms are computationally too hard, we propose a third algorithm as a more efficient alternative. We implemented the algorithms and provide evaluation results on their efficiency, effectiveness and meaningfulness in the context of two application scenarios: Incremental ontology learning and mapping revision.

Learning Disjointness for Debugging Mappings between Lightweight Ontologies

Dealing with heterogeneous ontologies by means of semantic mappings has become an important area of research and a number of systems for discovering mappings between ontologies have been developed. Most of these systems rely on general heuristics for finding mappings, hence are bound to fail in many situations. Consequently, automatically generated mappings often contain logical inconsistencies that hinder a sensible use of these mappings. In previous work, we presented an approach for debugging mappings between expressive ontologies that eliminates inconsistencies by means of diagnostic reasoning. A shortcoming of this method was its need for expressive class definitions. More specifically, the applicability of this method critically relies on the existence of a high-quality disjointness axiomatization. This paper deals with the application of the debugging approach to mappings between lightweight ontologies that do not contain any or very few disjointness axioms, as it is the case for most of todays practical ontologies. After discussing different approaches to deal with the absence of disjointness axioms we propose the application of supervised machine learning for detecting disjointness in a fully automatic manner. We present a detailed evaluation of our approach to learning disjointness and its impact on mapping debugging. The results show that debugging automatically created mappings with the help of learned disjointness axioms significantly improves the overall quality of these mappings.

Integrated metamodeling and diagnosis in OWL 2

Ontological metamodeling has a variety of applications yet only very restricted forms are supported by OWL 2 directly. We propose a novel encoding scheme enabling class-based metamodeling inside the domain ontology with full reasoning support through standard OWL 2 reasoning systems. We demonstrate the usefulness of our method by applying it to the OntoClean methodology. En passant, we address performance problems arising from the inconsistency diagnosis strategy originally proposed for OntoClean by introducing an alternative technique where sources of conflicts are indicated by means of marker predicates.

Inductive learning of disjointness axioms

The tremendous amounts of linked data available on the web are a valuable resource for a variety of semantic applications. However, these applications often need to face the challenges posed by flawed or underspecified representations. The sheer size of these data sets, being one of their most appealing features, is at the same time a hurdle on the way towards more accurate data because this size and the dynamics of the data often hinder manual maintenance and quality assurance. Schemas or ontologies constraining, e.g., the possible instantiations of classes and properties, could facilitate the automated detection of undesired usage patterns or incorrect assertions, but only few knowledge repositories feature schema-level knowledge of sufficient expressivity. In this paper, we present several approaches to enriching learned or manually engineered ontologies with disjointness axioms, an important prerequisite for the applicability of logical approaches to knowledge base debugging. We describe the strengths and weaknesses of these approaches and report on a detailed evaluation based on the DBpedia dataset.

AEON - An approach to the automatic evaluation of ontologies

Although ontologies occupy a central place in the Semantic Web and related research domains, there are currently not many fully fledged ontology engineering methodologies available. In this paper, we want to present an integrated methodology for ontology engineering from scratch, inspired by various scientific disciplines, in particular database semantics and natural language processing.OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.