Entity Suggestion by Example using a Conceptual Taxonomy
This work addresses entity acquisition for applications like recommendation and query expansion, offering an incremental improvement over existing methods by incorporating taxonomy-based relevance models.
The paper tackles the problem of entity suggestion by example (ESbE), where users provide example entities to get related entities, by proposing a method that leverages conceptual taxonomies instead of relying on entity co-occurrences, resulting in significantly higher accuracy in evaluations with real datasets.
Entity suggestion by example (ESbE) refers to a type of entity acquisition query in which a user provides a set of example entities as the query and obtains in return some entities that best complete the concept underlying the given query. Such entity acquisition queries can be useful in many applications such as related-entity recommendation and query expansion. A number of ESbE query processing solutions exist in the literature. However, they mostly build only on the idea of entity co-occurrences either in text or web lists, without taking advantage of the existence of many web-scale conceptual taxonomies that consist of hierarchical isA relationships between entity-concept pairs. This paper provides a query processing method based on the relevance models between entity sets and concepts. These relevance models can be used to obtain the fine-grained concepts implied by the query entity set, and the entities that belong to a given concept, thereby providing the entity suggestions. Extensive evaluations with real data sets show that the accuracy of the queries processed with this new method is significantly higher than that of existing solutions.