Results of the 2016 ENtity Summarization Evaluation Campaign (ENSEC 2016)

Entities and their descriptions are becoming an important part of the datasets and knowledge graphs available on the Web. These descriptions can be used in concise representation (i.e., summaries) to help users understand the Web content (e.g., summaries generated from Google Knowledge Graph in Google Search). In the recent past, several systems emerged to tackle the problem of automatic summary generation for entity descriptions. Even though these proposed systems continuously push the boundaries, the problem is not yet resolved completely. Therefore, there is a need to support and encourage researchers in the community to participate in solving this important problem. ENSEC, the entity summarization evaluation campaign, is the first step taken towards realizing that goal, and we present the results of the systems participating in the campaign

Linked Data Entity Summarization

On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion