Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data

Musto, Cataldo; Lops, Pasquale; Basile, Pierpaolo; Gemmis, Marco de; Semeraro, Giovanni

doi:10.1145/2930238.2930249

Cited by 33 publications

(33 citation statements)

References 13 publications

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“…A method for personalised access to Linked Data has been suggested in [49] based on collaborative filtering that estimates the similarity between users, and produces resource recommendations from users with similar tastes. A graph-based recommendation methodology based on a personalised PageRank algorithm has been proposed in [50]. The approach in [51] allows the user to rate semantic associations represented as chains of relations to reveal interesting and unknown connections between entities for personalised recommendations.…”

Section: Text-based Semantic Data Browsersmentioning

confidence: 99%

Using knowledge anchors to facilitate user exploration of data graphs

Al-Tawil

Dimitrova

Thakker

2020

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This paper investigates how to facilitate users' exploration through data graphs. The prime focus is on knowledge utility, i.e. increasing a user's domain knowledge while exploring a data graph, which is crucial in the vast number of user-facing semantic web applications where the users are not experts in the domain. We introduce a highly unique exploration support mechanism underpinned by the subsumption theory for meaningful learning. A core algorithmic component for operationalising the subsumption theory for meaningful learning is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. The second key component is a subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion. The implementation of the algorithm is applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users' conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. We provide the first framework that adopts educational theories to inform data graph exploration for knowledge expansion which extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain.

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Section: Text-based Semantic Data Browsersmentioning

confidence: 99%

Using knowledge anchors to facilitate user exploration of data graphs

Al-Tawil

Dimitrova

Thakker

2020

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“…Several other approaches have been proposed afterwards such as a knowledge-based framework leveraging DBpedia for the crossdomain recommendation task [7,8], a content-based context-aware method able to adopt a semantic representation based on a combination of distributional semantics and entity linking techniques [18], a hybrid graph-based algorithm based on learning-to-rank method and path-based features extracted from heterogeneous information networks built upon DBpedia and collaborative information [21]. To the best of our knowledge, the only works dealing with automated feature selection from knowledge graphs are [17,24]. While the former analyzes the performance of a recommender system after a feature selection based on classical statistical methods such as Information Gain, Chi Squared etc., the latter adopts a technique based on ontological schema summarization.…”

Section: Related Workmentioning

confidence: 99%

Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

Bellini

Anelli

Noia

et al. 2017

Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems

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In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments, we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weight semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behaves in the presence of a few ratings from a user on the Movielens 1M dataset and compare results with BPRSLIM.

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“…These measures have been designed to work directly on LOD without considering the collaborative view of users. Based on the nature of the graph structure of DBpedia, graph-based approaches have been proposed [17,19]. For instance, Musto et al [17] presented apersonalized PageRank algorithm [7] using LOD-enabled features for the top-N recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the nature of the graph structure of DBpedia, graph-based approaches have been proposed [17,19]. For instance, Musto et al [17] presented apersonalized PageRank algorithm [7] using LOD-enabled features for the top-N recommendations. Nguyen et al [19] investigated SimRank [12] and PageRank, and their performance for computing similarity between entities in RDF graphs and investigated their usage to feed a content-based recommender system.…”

Section: Related Workmentioning

confidence: 99%

Factorization Machines Leveraging Lightweight Linked Open Data-Enabled Features for Top-N Recommendations

Piao

Breslin

2017

Lecture Notes in Computer Science

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Abstract. With the popularity of Linked Open Data (LOD) and the associated rise in freely accessible knowledge that can be accessed via LOD, exploiting LOD for recommender systems has been widely studied based on various approaches such as graph-based or using different machine learning models with LOD-enabled features. Many of the previous approaches require construction of an additional graph to run graphbased algorithms or to extract path-based features by combining useritem interactions (e.g., likes, dislikes) and background knowledge from LOD. In this paper, we investigate Factorization Machines (FMs) based on particularly lightweight LOD-enabled features which can be directly obtained via a public SPARQL Endpoint without any additional effort to construct a graph. Firstly, we aim to study whether using FM with these lightweight LOD-enabled features can provide competitive performance compared to a learning-to-rank approach leveraging LOD as well as other well-established approaches such as kNN-item and BPRMF. Secondly, we are interested in finding out to what extent each set of LOD-enabled features contributes to the recommendation performance. Experimental evaluation on a standard dataset shows that our proposed approach using FM with lightweight LOD-enabled features provides the best performance compared to other approaches in terms of five evaluation metrics. In addition, the study of the recommendation performance based on different sets of LOD-enabled features indicate that propertyobject lists and PageRank scores of items are useful for improving the performance, and can provide the best performance through using them together for FM. We observe that subject-property lists of items does not contribute to the recommendation performance but rather decreases the performance.

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Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data

Cited by 33 publications

References 13 publications

Using knowledge anchors to facilitate user exploration of data graphs

Using knowledge anchors to facilitate user exploration of data graphs

Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

Factorization Machines Leveraging Lightweight Linked Open Data-Enabled Features for Top-N Recommendations

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