Dagmar Gromann

A Cross-Lingual Correcting and Completive Method for Multilingual Ontology Labels

Multilingual content in ontologies has one of the highest potentials for bridging linguistic borders on the Semantic Web. Human readability and automated linguistic processing of Multilingual Semantic Web resources depend on natural language content represented in labels . As there are currently no standards or best practices for labeling ontologies, existing labels are frequently highly condensed up to the point of losing their domain-specific expressivity. For instance, ellipses often used in labels pose a challenge to linguistic processing. Elided domain-specific elements challenge human users and machines alike. Thus, the proposed method expands condensed labels in four main processing steps by resolving complex natural language phenomena. It heavily relies on a cross-lingual comparison and employs idiosyncratic benefits of one language to process other languages.

Hashtag Processing for Enhanced Clustering of Tweets.

Rich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering.