Specifically, these methods embed n-ary relations and entities into a low-dimensional space without considering the specific order of entities and employ these embeddings to assess the plausibility of tuples. However, these existing methods overlook the importance of positions and roles and continue to utilize binary modeling ideas similar to those used in knowledge graphs. Several previous studies have focused on the link prediction task in knowledge hypergraphs. Loggen Sie sich ein, um Ihre Alerts zu aktualisieren und Neue anzulegen. At the same time, prior work has yet to focus on using position and role information in knowledge hypergraph modeling. The importance of considering roles and positions in the modeling of knowledge hypergraphs is clear from this example. All entities and their respective roles determine the meaning of the tuple. This definition was first introduced by Wen et al. They are represented by the entities Best Scorer, Season 07-08, and LeBron James, indicating that "LeBron James won the best scorer award in the 07-08 season." If LeBron James and Season 07-08 are swapped, the fact represented by the tuple would be inconsistent with reality. For example, in relation to SportAward, the roles of entities at positions 1, 2, and 3 are defined as Award, Season, and Winner, respectively. The role is distinct from the entity type and is defined by a particular relation at a specific position, serving as the semantic meaning of the entity in the tuple. The position represents the order of entities in a tuple. The entities in the tuple are arranged in a specific order, each occupying a unique position and fulfilling a particular role. 1, an oval represents a tuple, while a circle represents an entity. Our code is available at N-ary relations, which describe relationships between more than two entities, offer a more nuanced and expressive way to model complex semantics. In experimental results, PosKHG achieved an average improvement of 4.1% on MRR compared to other state-of-the-art knowledge hypergraph embedding methods. PosKHG achieves full expressiveness and high prediction efficiency. Additionally, PosKHG employs a relation matrix to capture the compatibility of both information with all associated entities and a scoring function to measure the plausibility of tuples made up of entities with specific roles and positions. PosKHG uses an embedding space with basis vectors to represent entities’ positional and role information through a linear combination, which allows for similar representations of entities with related roles and positions. To address this issue, we introduce PosKHG, a method that considers entities’ positions and roles within n-ary tuples. However, many current approaches simply extend binary relation methods from knowledge graphs to n-ary relations, which does not allow for capturing entity positional and role information in n-ary tuples. Link prediction in knowledge hypergraphs is essential for various knowledge-based applications, including question answering and recommendation systems.
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