Augmenting Lightweight Domain Ontologies with Social Evidence Sources

Abstract

Abstract-Recent research shows the potential of utilizing data collected through Web 2.0 applications to capture changes in a domain's terminology. This paper presents an approach to augment corpus-based ontology learning by considering terms from collaborative tagging systems, social networking platforms, and micro-blogging services. The proposed framework collects information on the domain's terminology from domain documents and a seed ontology in a triple store. Data from social sources such as Delicious, Flickr, Technorati and Twitter provide an outside view of the domain and help incorporate external knowledge into the ontology learning process. The neural network technique of spreading activation is used to identify relevant new concepts, and to determine their positions in the extended ontology. Evaluating the method with two measures (PMI and expert judgements) demonstrates the significant benefits of social evidence sources for ontology learning.

Confidence Management for Learning Ontologies from Dynamic Web Sources

“…The underlying ontology learning components have been thoroughly evaluated and published in (Liu et al, 2005), (Weichselbraun et al, 2010a) and (Weichselbraun et al, 2010b). The framework presented in this paper extends this work by introducing dynamic data structures for confidence management, the source impact vector (SIV) and through mechanisms for its adaptation according to user feedback, as we detail next.…”

“…To collect evidence from social media we use the TagInfoService interface of the easy Web Retrieval Toolkit (www.semanticlab.net/index.php/eWRT) to get related terms for the label of a seed concept from sources such as Twitter, Flickr and Del.icio.us (Weichselbraun et al, 2010a). Querying the DBpedia SPARQL endpoint with the seed concept labels and specific properties such as dcterms:subject, or the use of Scarlet , constitute the structured data evidence sources.…”

Computing Semantic Association: Comparing Spreading Activation and Spectral Association for Ontology Learning

“…The original system [14] learns from domain text only and already includes spreading activation as the major building block to integrate evidence. Weichselbraun et al evaluate information from social media as additional evidence source [16] and present novel methods for learning non-taxonomic relations [17]. Finally, Wohlgenannt et al discusses data structures and algorithms for the fine-grained optimization of the system from feedback collected with games with a purpose [19].…”

“…In the ontology learning system used as the foundation and test bed of this paper spreading activation is an essential tool used for the selection of domain-relevant concept candidates from a big semantic network as well as for positioning the new concepts in the ontology [16]. Spreading activation helps us to pick the most relevant concepts and associations from a vast number of evidence (candidate concepts and relations) generated from heterogeneous input sources.…”