We present new approaches to the problems of named entity linking and question answering over linked data. Named entity linking consists in linking a name in a text to an entity in a knowledge base that represents its denotation. Question answering over linked data consists in interpreting a natural language question in terms of a SPARQL query that can be evaluated over a given RDF dataset.
We model both problems as statistical learning problems and present undirected probabilistic graphical models for both problems. Inference is done via approximative methods using Markov Chain Monte Carlo Method and in particular using the Metropolis Hastings algorithm. Parameter optimization is performed via a Learning-to-Rank Approach based on SampleRank.
We present our model structure for both problems as well as the features used, and discuss our results on standard bechmarking datasets.
Philipp Cimiano is the head of the Semantic Computing Group at Bielefeld University. He is also affiliated with the Cognitive Interaction Technology Excellence Cluster. Before joining the University of Bielefeld, he was an assistant professor at Delft University of Technology (2008-2009) and a postdoctoral researcher at the Institute AIFB of Universität Kalsruhe (TH). Philipp is mainly interested in topics at the intersection between knowledge representation and text processing including: text mining, computational semantics, information retrieval, question answering, ontology learning, ontology localization, etc. He is editorial board member of the Semantic Web Journal.