A Knowledge Graph Embedding Model for Answering Factoid Entity Questions

Parastoo Jafarzadeh, Faezeh Ensan, Mahdiyar Ali Akbar Alavi, Fattane Zarrinkalam

Abstract:

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.