Explaining relationships between entities

Modern search engines are increasingly aiming to understand users’ intent in order to answer information needs more effectively by providing richer information than the traditional “ten blue links”. This information might include context about the entities present in the query, direct answers to questions that concern entities and more. A recent trend when answering queries that refer to a single entity is providing an additional panel that contains some basic information about the entity, along with links to other entities that are related to the initial entity. A problem that remains largely unexplored is how to provide an explanation of why two entities are related. In this work, we study the problem of explaining pre-defined relations of entity pairs with natural language sentences in the context of search engines. We propose a method that first extracts sentences that refer to each entity pair and then ranks the sentences by how well they describe the relation between the two entities. Our ranking module combines a rich set of features using state-of-the-art learning to rank algorithms. We evaluate our method on a dataset of entities and relations used by a commercial search engine. The experimental results demonstrate the effectiveness of our method, which can be efficiently applied in a search engine scenario.