Purpose

Measuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a major role in similarity measurement methodology, the lack of semantic insight on the data may leave these approaches imperfect. The purpose of this paper is to incorporate data semantics into the measuring process.

Design/methodology/approach

The emerged linked open data (LOD) provide a practical solution to tackle such difficulty. Common methodologies consuming LOD mainly focused on using link attributes that provide some sort of semantic relations between data. In this work, methods for measuring semantic distances between resources using information gathered from LOD were proposed. Such distances were then applied to music recommendation, focusing on the effect of various weight and level settings.

Findings

This work conducted experiments using the MusicBrainz dataset and evaluated the proposed schemes for the plausibility of LOD on music recommendation. The experimental result shows that the proposed methods electively improved classic approaches for both linked data semantic distance (LDSD) and PathSim methods by 47 and 9.7%, respectively.

Originality/value

The main contribution of this work is to develop novel schemes for incorporating knowledge from LOD. Two types of knowledge, namely attribute and path, were derived and incorporated into similarity measurements. Such knowledge may reflect the relationships between resources in a semantic manner since the links in LOD carry much semantic information regarding connecting resources.

中文翻译：

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使用链接的开放数据测量语义距离及其在音乐推荐器系统中的应用

目的

由于缺乏可靠的信息以及与资源有关的各种可用信息，因此很难测量两种资源之间的相似性。已经设计出许多方法来解决这种困难。尽管基于内容的方法（在比较资源时采用了与资源相关的数据）在相似性度量方法中起了主要作用，但是对数据缺乏语义洞察力可能会使这些方法不完善。本文的目的是将数据语义纳入测量过程。

设计/方法/方法

出现的链接开放数据（LOD）提供了解决此类难题的实用解决方案。消耗LOD的常见方法主要集中在使用链接属性，这些属性在数据之间提供某种语义关系。在这项工作中，提出了使用从LOD收集的信息来测量资源之间语义距离的方法。然后将这种距离应用于音乐推荐，重点放在各种体重和水平设置的效果上。

发现

这项工作使用MusicBrainz数据集进行了实验，并评估了针对LOD在音乐推荐上的合理性而提出的方案。实验结果表明，所提出的方法分别选择性地将链接数据语义距离（LDSD）方法和PathSim方法的经典方法分别提高了47％和9.7％。

创意/价值

这项工作的主要贡献是开发新的计划，以整合来自LOD的知识。推导了两种类型的知识，即属性和路径，并将其合并到相似性度量中。由于LOD中的链接携带了大量有关连接资源的语义信息，因此此类知识可能以语义方式反映资源之间的关系。