Abstract
Named Entity Disambiguation (NED) is the task of disambiguating named entities in a natural language text by linking them to their corresponding entities in a knowledge base such as DBpedia, which are already recognized. It is an important step in transforming unstructured text into structured knowledge. Previous work on this task has proven a strong impact of graph-based methods such as PageRank on entity disambiguation. Other approaches rely on distributional similarity between an article and the textual description of a candidate entity. However, the combined impact of these different feature groups has not been explored to a sufficient extent. In this paper, we present a novel approach that exploits an undirected probabilistic model to combine different types of features for named entity disambiguation. Capitalizing on Markov Chain Monte Carlo sampling, our model is capable of exploiting complementary strengths between both graph-based and textual features. We analyze the impact of these features and their combination on named entity disambiguation. In an evaluation on the GERBIL benchmark, our model compares favourably to the current state-of-the-art in 8 out of 14 data sets.
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Notes
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After re-normalizing the probabilities such that \(\sum _{s'_{ij}}\pi (s'_{ij}) = 1\).
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Our experiments show that a greedy approach that always prefers the state with the highest probability works best.
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Our results, GERBIL v1.2.2:
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State-of-the-art annotation systems’ results, GERBIL v1.2.2:
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Acknowledgements
This work was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).
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Hakimov, S., Horst, H.t., Jebbara, S., Hartung, M., Cimiano, P. (2016). Combining Textual and Graph-Based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_19
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