README.md

metadata

task_categories:
  - question-answering
language:
  - en

We present a dataset for graph-based question answering. The dataset consists of <question; candidate answer> pairs. For each candidate, we present a graph that is obtained by finding the shortest path between named entities mentioned in a question and a candidate answer. As a knowledge graph, we adopted Wikidata. Our dataset has the following fields:

• sample_id - an identifier for <question, candidate answer>;

• question - question text;

• questionEntity - comma-separated list of names (textual strings) for Wikidata concepts mentioned in a given question;

• answerEntity - a textual name of candidate answer (candidate is a concept from Wikidata) for the given question;

• groundTruthAnswerEntity - a textual name of ground truth answer (answer is a concept from Wikidata) for the given question;

• answerEntityId - a Wikidata id of candidate answer (see "answerEntity" column). Example: "Q2599";

• questionEntityId - a comma-separated list of Wikidata ids for concepts mentioned in a given question (list of ids for mentions from "questionEntity" column);

• groundTruthAnswerEntityId - a Wikidata id of ground truth answer (see "answerEntity" column). Example: "Q148234";

• correct - either "True" or "False". The field indicates whether a <question, answer candidate> is correct, i.e., candidate answer is a true answer to the given question;

• graph - a shortest-path graph for a given <question, candidate answer> pair. The graph is obtained by taking the shortest paths from all mentioned concepts ("questionEntityId" column) to a candidate answer("answerEntityId" column) in the knowledge graph of Wikidata. The graph is stored in "node-link" JSON format from NetworkX. You can import the graph using the node_link_graph.

Please see our Github for baselines, and useful code.