Abstract
One of the most important prerequisites for achieving the Semantic Web vision is semantic annotation of data/resources. Semantic annotation enriches unstructured and/or semi-structured content with a context that is further linked to the structured domain-specific knowledge. In particular, ontology-based semantic annotators enable the selection of a specific ontology to annotate content. This paper presents results of an empirical study of recent ontology-based annotators, namely Stanbol, KIM, and SDArch. Specifically, we evaluated the robustness of these annotators with respect to specific features of ontology concepts such as the length of concepts' labels and their linguistic categories (e.g., prepositions and conjunctions). Our results show that although significantly correlated according to most of the conducted evaluations, tools still exhibit their unique features that could be a topic of new research.
Original language | English |
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Title of host publication | Proceedings of the 7th International Conference on Knowledge Capture |
Subtitle of host publication | "Knowledge Capture in the Age of Massive Web Data", K-CAP 2013 |
DOIs | |
Publication status | Published - 2 Sep 2013 |
Externally published | Yes |
Event | Knowledge Capture 2013 - Banff, Canada Duration: 23 Jun 2013 → 26 Jun 2013 Conference number: 7th http://www.k-cap.org/kcap13/kcap2013/index.html |
Conference
Conference | Knowledge Capture 2013 |
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Abbreviated title | K-CAP 2013 |
Country/Territory | Canada |
City | Banff |
Period | 23/06/13 → 26/06/13 |
Internet address |
Keywords
- Annotation tools
- Automatic semantic annotation
- Comparative analysis
- Empirical study