Petya Osenova

Ontological Modelling of Rumors

In this paper, we present on-going work pursued in the context of the Pheme project. There, the detection of rumors in social media is playing a central role in two use cases. In order to be able to store and to query for information on specific types of rumors that can be circulated in such media (but also in “classical” media), we started to build ontological models of rumors, disputed claims, misinformation and veracity. As rumors can be considered as unverified statements, which after a certain time can be classified as either erroneous information or as facts, there is a need to model also the temporal information associated with any statement. As we are dealing in first line with social media, our modelling work should also cover information diffusion networks and user online behavior, which can also help in classifying a statement as a rumor or a fact. We focus in this paper on the core of our rumor ontology.

A Multilingual Access Module to Legal Texts

The paper introduces a Multilingual Access Module. This module translates the user’s legislation query from its source language into the target language, and retrieves the detected texts that match the query. The service is demonstrated in its potential for two languages – English and Bulgarian, in both directions (English-to-Bulgarian and Bulgarian-to-English). The module consists of two submodules: Ontology-based and Statistical Machine Translation. Since both proposed submodules have some drawbacks, they are used in an integrated architecture, thus profiting from each other.

Knowledge Graph Extension for Word Sense Annotation

One of the most successful approaches to Word Sense Disambiguation (WSD) in the last decade has been the knowledge-based approach, which exploits lexical knowledge sources such as wordnets, ontologies, etc. The knowledge encoded in them is typically used as a sense inventory and as a relations bank. However, this type of information is rather sparse in terms of senses and the relations among them. In this paper we present a strategy for the enrichment of WSD knowledge bases with data-driven relations from a gold standard corpus (annotated with word senses, valency information, syntactic analyses, etc.). We focus on Bulgarian and English as use cases, but our approach is scalable to other languages as well. The results show that the addition of new knowledge improves accuracy on the WSD task in a statistically significant way.