Vito Bellini, Eugenio Di Sciascio, Francesco Maria Donini, Claudio Pomo, Azzurra Ragone, Angelo Schiavone
{"title":"A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencoders","authors":"Vito Bellini, Eugenio Di Sciascio, Francesco Maria Donini, Claudio Pomo, Azzurra Ragone, Angelo Schiavone","doi":"10.1007/s10844-023-00830-z","DOIUrl":null,"url":null,"abstract":"<p>Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of <i>(i)</i> three recommendation tasks (books, music, movies); <i>(ii)</i> three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; <i>(iii)</i> two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"14 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-023-00830-z","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of (i) three recommendation tasks (books, music, movies); (ii) three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; (iii) two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model.
知识图谱(KG)已经证明了其作为高质量信息源在数据整合、搜索、文本摘要和个性化等不同任务中的优势。另一个因采用知识图谱而受益的著名研究领域是推荐系统(RS)。向 RS 输入来自 KG 的数据可以提高推荐的准确性、多样性和新颖性,并为创建可用于解释的可解释模型铺平道路。将 KG 与 RS 结合起来的这种可能性提出了一个问题,即这种添加是否可以即插即用的方式进行--也适用于推荐领域--还是每种组合都需要仔细评估。为了研究这个问题,我们考虑了以下所有可能的组合:(i) 三项推荐任务(书籍、音乐、电影);(ii) 使用来自 KG 的数据(特别是我们将详细讨论的语义感知深度学习模型)的三种推荐模型,与不添加 KG 的三种基线模型进行比较;(iii) 网络上免费提供的两种主要百科全书式 KG:DBpedia 和 Wikidata。在大量实验评估的支持下,我们展示了各种组合在准确性和多样性方面的最终结果,突出说明了知识的注入并不总能带来回报。此外,我们还展示了根据推荐领域和学习模型,KG 的选择和其中的数据形式对结果的影响。
期刊介绍:
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.