Quantitative Considerations about the Semantic Relationship of Entities in a Document Corpus

Schmidt, Andreas
Scholz, Steffen
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Providing suggestions for internet-users is an important task nowadays. So for example, when we enter a search string into the Google interface, it suggests further terms, based on previously formulated queries from other users having used the search engine before. In the context of an entity based search engine, entity-suggestion is also a very important task, when specifying the entities by the user. Additionally, this feature can also be utilized to suggest further entities, which are somehow related to already specified entities. If the suggestions are eligible the user can very quickly formulate his search desire. If the suggestions are based on the search corpus itself, new and previously unknown relationships between entities can be discovered along the way. The aim of this paper is a quantitative analysis of relationships between entities in a big document corpus under the aspect of providing suggestions for entities in real time.
Data, Text and Web Mining for Business Analytics, Autocompletion, Entity-based-Search-Engine, Semantic-Relationship
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