The abundance and ubiquity of RDF data (such as DBpedia and YAGO2) necessitate their effective and efficient retrieval. For this purpose, keyword search paradigms liberate users from understanding the RDF schema and the SPARQL query language. Popular RDF knowledge bases (e.g., YAGO2) also include spatial semantics that enable location-based search. In an earlier location-based keyword search paradigm, the user inputs a set of keywords, a query location, and a number of RDF spatial entities to be retrieved. The output entities should be geographically close to the query location and relevant to the query keywords. However, the results can be similar to each other, compromising query effectiveness. In view of this limitation, we integrate textual and spatial diversification into RDF spatial keyword search, facilitating the retrieval of entities with diverse characteristics and directions with respect to the query location. Since finding the optimal set of query results is NP-hard, we propose two approximate algorithms with guaranteed quality. Extensive empirical studies on two real datasets show that the algorithms only add insignificant overhead compared to non-diversified search, while returning results of high quality in practice (which is verified by a user evaluation study we conducted).

中文翻译：

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对RDF数据进行多元化的空间关键字搜索

RDF数据（如DBpedia和YAGO2）的数量众多且无处不在，因此需要有效而高效的检索。为此，关键字搜索范例使用户摆脱对RDF架构和SPARQL查询语言的了解。流行的RDF知识库（例如YAGO2）还包括启用基于位置的搜索的空间语义。在较早的基于位置的关键字搜索范例中，用户输入一组关键字，一个查询位置以及要检索的多个RDF空间实体。输出实体在地理位置上应靠近查询位置并与查询关键字相关。但是，结果可能彼此相似，从而降低了查询效率。鉴于这一局限性，我们将文本和空间多样化集成到RDF空间关键字搜索中，便于检索具有关于查询位置的不同特征和方向的实体。由于找到最佳查询结果集是NP难的，因此我们提出了两种具有保证质量的近似算法。对两个真实数据集的大量实证研究表明，与非多元化搜索相比，该算法仅增加了微不足道的开销，同时在实践中返回了高质量的结果（已通过我们进行的用户评估研究得到了验证）。