Factoid entity questions (FEQ), which seek answers in the form of a single entity from knowledge sources, such as DBpedia and Wikidata, constitute a substantial portion of user queries in search engines. This article introduces the knowledge graph embedding model for FEQ (KGE-FEQ) answering. Leveraging a textual knowledge graph derived from extensive text collections, KGE-FEQ encodes textual relationships between entities. The model employs a two-step process: (1) Triple Retrieval, where relevant triples are retrieved from the textual knowledge graph based on semantic similarities to the question, and (2) Answer Selection, where a knowledge graph embedding approach is utilized for answering the question. This involves positioning the embedding for the answer entity close to the embedding of the question entity, incorporating a vector representing the question and textual relations between entities. Extensive experiments evaluate the performance of the proposed approach, comparing KGE-FEQ to state-of-the-art baselines in FEQ answering and the most advanced open-domain question answering techniques applied to FEQs. The results show that KGE-FEQ outperforms existing methods across different datasets. Ablation studies highlights the effectiveness of KGE-FEQ when both the question and textual relations between entities are considered for answering questions.

The importance of entity retrieval, the task of retrieving a ranked list of related entities from big knowledge bases given a textual query, has been widely acknowledged in the literature. In this paper, we propose a novel entity retrieval method that addresses the important challenge that revolves around the need to effectively represent and model context in which entities relate to each other. Based on our proposed method, a model is firstly trained to retrieve and prune a subgraph of a textual knowledge graph that represents contextual relationships between entities. Secondly, a deep model is introduced to reason over the textual content of nodes, edges, and the given question and score and rank entities in the subgraph. We show experimentally that our approach outperforms state-of-the-art methods on a number of benchmarks for entity retrieval.