Entity Ranking for Queries with Modifiers Based on Knowledge Bases and Web Search Results
Entity Ranking for Queries with Modifiers Based on Knowledge Bases and Web Search Results
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DOI：
10.1587/transinf.2017edp7372
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发表时间：
2018-09
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通讯作者：
Wiradee Imrattanatrai;Makoto P. Kato;Katsumi Tanaka;Masatoshi Yoshikawa
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作者：
Wiradee Imrattanatrai;Makoto P. Kato;Katsumi Tanaka;Masatoshi Yoshikawa
本文提出了寻找给定查询的实体的排序列表的方法(例如通过识别对应的属性(例如，分别为修饰语“古代”和“京都”建立的日期和位置)，通过在查询中利用不同类型的修饰语，在查询中利用不同类型的修饰语(例如，分别为修饰语“古代”和“京都”建立的日期和位置)，来表示查询“京都的古代佛教寺庙”的“Kennin-ji”、“Tenryu-ji”或“Kinkaku-ji”。虽然大多数主要搜索引擎提供实体搜索功能，该功能基于用户的查询返回实体列表，但实体既不会针对各种搜索查询呈现，也不会以用户期望的顺序呈现。为了提高实体搜索的有效性，我们提出了两种实体排序方法。我们提出的第一种方法是基于Web的实体排名，它直接从响应于查询的Web搜索结果中找到相关实体，并将估计的相关性传播给其他实体。第二种提出的方法是基于属性的实体排名，其基于与查询中的修饰符相对应的属性来对实体进行排名。为此，我们提出了一种新的属性识别方法，该方法基于支持向量机，使用对不同类型修饰符有效的七个准则来识别一组相关的属性。实验结果表明，与单独使用每个准则相比，本文提出的属性识别方法可以预测更多的相关属性。此外，在结合使用基于Web和基于属性的实体排名方法时，我们在返回相关实体的排名列表方面取得了最佳性能。关键词：实体排名、属性识别、知识库、网络搜索
This paper proposes methods of finding a ranked list of entities for a given query (e.g. “Kennin-ji”, “Tenryu-ji”, or “Kinkaku-ji” for the query “ancient zen buddhist temples in kyoto”) by leveraging different types of modifiers in the query through identifying corresponding properties (e.g. established date and location for the modifiers “ancient” and “kyoto”, respectively). While most major search engines provide the entity search functionality that returns a list of entities based on users’ queries, entities are neither presented for a wide variety of search queries, nor in the order that users expect. To enhance the effectiveness of entity search, we propose two entity ranking methods. Our first proposed method is a Webbased entity ranking that directly finds relevant entities from Web search results returned in response to the query as a whole, and propagates the estimated relevance to the other entities. The second proposed method is a property-based entity ranking that ranks entities based on properties corresponding to modifiers in the query. To this end, we propose a novel property identification method that identifies a set of relevant properties based on a Support Vector Machine (SVM) using our seven criteria that are effective for different types of modifiers. The experimental results showed that our proposed property identification method could predict more relevant properties than using each of the criteria separately. Moreover, we achieved the best performance for returning a ranked list of relevant entities when using the combination of the Web-based and property-based entity ranking methods. key words: entity ranking, property identification, knowledge base, web search