--- task_categories: - question-answering language: - en --- We present a dataset for graph-based question answering. The dataset consists of 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** - 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 is correct, i.e., candidate answer is a true answer to the given question; * **graph** - a shortest-path graph for a given 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](https://networkx.org/documentation/stable/reference/readwrite/generated/networkx.readwrite.json_graph.node_link_graph.html). Please see our [Github](https://github.com/uhh-lt/TextGraphs17-shared-task.git) for baselines, and useful code.