2024/07/15 by Parastoo Jafarzadeh, Faezeh Ensan, Mahdiyar Ali Akbar Alavi +1
Computer Science · #Advanced Graph Neural Networks #Multimodal Machine Learning Applications #Topic Modeling
paper · doi:10.1145/3678003
openalex publication_date 2024/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/27
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.