CASIA@V2: A MLN-based Question Answering System over Linked Data
Shizhu He, Yuanzhe Zhang, Kang Liu, Jun Zhao · CLEF (Working Notes) · 2014
We present a question answering system (CASIA@V2) over Linked Data (DBpedia), which translates natural language questions into structured queries automatically. Existing systems usually adopt a pipeline framework, which con- tains four major steps: 1) Decomposing the question and detecting candidate phrases; 2) mapping the detected phrases into semantic items of Linked Data; 3) grouping the mapped semantic items into semantic triples; and 4) generat- ing the rightful SPARQL query. We present a jointly learning framework using Markov Logic Network(MLN) for phrase detection, phrases mapping to seman- tic items and semantic items grouping. We formulate the knowledge for resolving the ambiguities in three steps of QALD as first-order logic clauses in a MLN. We evaluate our approach on QALD-4 test dataset and achieve an F-measure score of 0.36, an average precision of 0.32 and an average recall of 0.40 over 50 questions.