Special Issue "Knowledge Graphs for Search and Recommendation"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 November 2021.

Special Issue Editors

Dr. Pierpaolo Basile
E-Mail Website
Guest Editor
Department of Computer Science, Università degli Studi di Bari Aldo Moro, 70121 Bari BA, Italy
Interests: natural language processing; entity linking; information retrieval; recommender systems
Dr. Annalina Caputo
E-Mail Website
Guest Editor
School of Computing, Dublin City University Glasnevin Campus, Glasnevin, Dublin 9, Ireland
Interests: Intelligent Information Access; Text Representation and Information Retrieval; Natural Language Processing

Special Issue Information

Dear Colleagues,

The MDPI Information journal invites submissions to a Special Issue on “Knowledge Graphs for Search and Recommendation”.

The availability of large publicly available knowledge graph resources, such as Freebase, DBpedia, Wikidata, Yago, and Babelnet, has fostered new research directions, tasks, and application, in search and recommendation systems. Knowledge graphs are at the forefront of applications that try to bridge the semantic gap between structured and unstructured information in order to open new possibilities to represent, visualize, query, interact, and in general, make sense of information.

More recently, search and recommendation systems have benefited from developments in deep learning and graph embeddings, which have inspired new approaches to semantic matching, feature extraction, cold-start and long-tail problems, algorithm transparency and interpretability, entity, facet, and exploratory search.

This Special Issue welcomes submissions that provide new perspectives, introduce new challenges and tasks, as well as overview articles on the use of knowledge graphs in information retrieval and recommendation systems. 

Dr. Pierpaolo Basile
Dr. Annalina Caputo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Knowledge graph representation and acquisition for search and recommendation
  • Knowledge graph embeddings for search and recommendation
  • Entity linking for search and recommendation
  • Knowledge graph-based query expansion
  • Knowledge graph-based information extraction
  • Question answering over knowledge graphs
  • Facet and exploratory search through knowledge graphs
  • Result diversification through knowledge graphs
  • Conversational systems based on knowledge graphs
  • Knowledge graphs for recommendation explanation and transparency
  • Entity and expert retrieval
  • Application of knowledge graphs to retrieval and recommendation on specific domains: cultural heritage and digital humanities, medical, legal, etc.

Published Papers (10 papers)

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Research

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Article
The Geranium Platform: A KG-Based System for Academic Publications
Information 2021, 12(9), 366; https://0-doi-org.brum.beds.ac.uk/10.3390/info12090366 - 08 Sep 2021
Viewed by 548
Abstract
Knowledge Graphs (KGs) have emerged as a core technology for incorporating human knowledge because of their capability to capture the relational dimension of information and of its semantic properties. The nature of KGs meets one of the vocational pursuits of academic institutions, which [...] Read more.
Knowledge Graphs (KGs) have emerged as a core technology for incorporating human knowledge because of their capability to capture the relational dimension of information and of its semantic properties. The nature of KGs meets one of the vocational pursuits of academic institutions, which is sharing their intellectual output, especially publications. In this paper, we describe and make available the Polito Knowledge Graph (PKG) –which semantically connects information on more than 23,000 publications and 34,000 authors– and Geranium, a semantic platform that leverages the properties of the PKG to offer advanced services for search and exploration. In particular, we describe the Geranium recommendation system, which exploits Graph Neural Networks (GNNs) to suggest collaboration opportunities between researchers of different disciplines. This work integrates the state of the art because we use data from a real application in the scholarly domain, while the current literature still explores the combination of KGs and GNNs in a prototypal context using synthetic data. The results shows that the fusion of these technologies represents a promising approach for recommendation and metadata inference in the scholarly domain. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Article
Populating Web-Scale Knowledge Graphs Using Distantly Supervised Relation Extraction and Validation
Information 2021, 12(8), 316; https://0-doi-org.brum.beds.ac.uk/10.3390/info12080316 - 06 Aug 2021
Viewed by 490
Abstract
In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses [...] Read more.
In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations. The designed system does not require any effort for adaptation to new languages and domains as it does not use any hand-labeled data, NLP analytics, and inference rules. Our experiments, performed on a popular academic benchmark, demonstrate that the suggested system boosts the performance of relation extraction by a wide margin, reporting error reductions of 50%, resulting in relative improvement of up to 100%. Furthermore, a web-scale experiment conducted to extend DBPedia with knowledge from Common Crawl shows that our system is not only scalable but also does not require any adaptation cost, while yielding a substantial accuracy gain. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Article
Collective and Informal Learning in the ViewpointS Interactive Medium
Information 2021, 12(5), 183; https://0-doi-org.brum.beds.ac.uk/10.3390/info12050183 - 23 Apr 2021
Cited by 1 | Viewed by 597
Abstract
Collective learning has been advocated to be at the source for innovation, particularly as serendipity seems historically to have been the driving force not only behind innovation, but also behind scientific discovery and artistic creation. Informal learning is well known to represent the [...] Read more.
Collective learning has been advocated to be at the source for innovation, particularly as serendipity seems historically to have been the driving force not only behind innovation, but also behind scientific discovery and artistic creation. Informal learning is well known to represent the most significant learning effects in humans, far better than its complement: formal learning with predefined objectives. We have designed an approach—ViewpointS—based on a digital medium—the ViewpointS Web Application—that enables and enhances the processes for sharing knowledge within a group and is equipped with metrics aimed at assessing collective and informal learning. In this article, we introduce by giving a brief state of the art about collective and informal learning, then outline our approach and medium, and finally, present and exploit a real-life experiment aimed at evaluating the ViewpointS approach and metrics. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Article
Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services
Information 2021, 12(4), 160; https://0-doi-org.brum.beds.ac.uk/10.3390/info12040160 - 09 Apr 2021
Viewed by 681
Abstract
Knowledge graphs are proving to be an increasingly important part of modern enterprises, and new applications of such enterprise knowledge graphs are still being found. In this paper, we report on the experience with the use of an automatic knowledge graph system called [...] Read more.
Knowledge graphs are proving to be an increasingly important part of modern enterprises, and new applications of such enterprise knowledge graphs are still being found. In this paper, we report on the experience with the use of an automatic knowledge graph system called Saffron in the context of a large financial enterprise and show how this has found applications within this enterprise as part of the “Conversation Concepts Artificial Intelligence” tool. In particular, we analyse the use cases for knowledge graphs within this enterprise, and this led us to a new extension to the knowledge graph system. We present the results of these adaptations, including the introduction of a semi-supervised taxonomy extraction system, which includes analysts in-the-loop. Further, we extend the kinds of relations extracted by the system and show how the use of the BERTand ELMomodels can produce high-quality results. Thus, we show how this tool can help realize a smart enterprise and how requirements in the financial industry can be realised by state-of-the-art natural language processing technologies. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Article
On Training Knowledge Graph Embedding Models
Information 2021, 12(4), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/info12040147 - 31 Mar 2021
Viewed by 701
Abstract
Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities [...] Read more.
Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, state-of-the-art approaches have mostly focused on new ways to represent embeddings interaction functions (i.e., scoring functions). In this paper, we argue that the choice of other training components such as the loss function, hyperparameters and negative sampling strategies can also have substantial impact on the model efficiency. This area has been rather neglected by previous works so far and our contribution is towards closing this gap by a thorough analysis of possible choices of training loss functions, hyperparameters and negative sampling techniques. We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Article
Research on Automatic Question Answering of Generative Knowledge Graph Based on Pointer Network
Information 2021, 12(3), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/info12030136 - 21 Mar 2021
Viewed by 782
Abstract
Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does [...] Read more.
Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Out-of-vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, including three parts of knowledge vocabulary construction, data pre-processing, and answer generation. In the word list construction, BiLSTM-CRF is used to identify the entity in the source text, finding the triples contained in the entity, counting the word frequency, and constructing it. In the part of data pre-processing, a pre-trained language model BERT combining word frequency semantic features is adopted to obtain word vectors. In the answer generation part, one combination of a vocabulary constructed by the knowledge graph and a pointer generator network(PGN) is proposed to point to the corresponding entity for generating answer. The experimental results show that the proposed method can achieve superior performance on WebQA datasets than other methods. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Article
Unsupervised DNF Blocking for Efficient Linking of Knowledge Graphs and Tables
Information 2021, 12(3), 134; https://0-doi-org.brum.beds.ac.uk/10.3390/info12030134 - 19 Mar 2021
Viewed by 477
Abstract
Entity Resolution (ER) is the problem of identifying co-referent entity pairs across datasets, including knowledge graphs (KGs). ER is an important prerequisite in many applied KG search and analytics pipelines, with a typical workflow comprising two steps. In the first ’blocking’ step, entities [...] Read more.
Entity Resolution (ER) is the problem of identifying co-referent entity pairs across datasets, including knowledge graphs (KGs). ER is an important prerequisite in many applied KG search and analytics pipelines, with a typical workflow comprising two steps. In the first ’blocking’ step, entities are mapped to blocks. Blocking is necessary for preempting comparing all possible pairs of entities, as (in the second ‘similarity’ step) only entities within blocks are paired and compared, allowing for significant computational savings with a minimal loss of performance. Unfortunately, learning a blocking scheme in an unsupervised fashion is a non-trivial problem, and it has not been properly explored for heterogeneous, semi-structured datasets, such as are prevalent in industrial and Web applications. This article presents an unsupervised algorithmic pipeline for learning Disjunctive Normal Form (DNF) blocking schemes on KGs, as well as structurally heterogeneous tables that may not share a common schema. We evaluate the approach on six real-world dataset pairs, and show that it is competitive with supervised and semi-supervised baselines. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Article
Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation
Information 2020, 11(8), 388; https://0-doi-org.brum.beds.ac.uk/10.3390/info11080388 - 08 Aug 2020
Cited by 3 | Viewed by 2019
Abstract
With the rapid increase in the popularity of big data and internet technology, sequential recommendation has become an important method to help people find items they are potentially interested in. Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data. [...] Read more.
With the rapid increase in the popularity of big data and internet technology, sequential recommendation has become an important method to help people find items they are potentially interested in. Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data. Although effective, the results may be unable to capture both the semantic-based preference and the complex transitions between items adequately. In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs). We further link items in interaction sequences with existing external knowledge base (KB) entities and integrate the GNN-based recommender with key-value memory networks (KV-MNs) to incorporate KB knowledge. Specifically, we set a key matrix to many relation embeddings that learned from KB, corresponding to many entity attributes, and set up a set of value matrices storing the semantic-based preferences of different users for the corresponding attribute. By using a hybrid of a GNN and KV-MN, each session is represented as the combination of the current interest (i.e., sequential preference) and the global preference (i.e., semantic-based preference) of that session. Extensive experiments on three public real-world datasets show that our method performs better than baseline algorithms consistently. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Review

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Review
Challenges, Techniques, and Trends of Simple Knowledge Graph Question Answering: A Survey
Information 2021, 12(7), 271; https://0-doi-org.brum.beds.ac.uk/10.3390/info12070271 - 30 Jun 2021
Viewed by 870
Abstract
Simple questions are the most common type of questions used for evaluating a knowledge graph question answering (KGQA). A simple question is a question whose answer can be captured by a factoid statement with one relation or predicate. Knowledge graph question answering (KGQA) [...] Read more.
Simple questions are the most common type of questions used for evaluating a knowledge graph question answering (KGQA). A simple question is a question whose answer can be captured by a factoid statement with one relation or predicate. Knowledge graph question answering (KGQA) systems are systems whose aim is to automatically answer natural language questions (NLQs) over knowledge graphs (KGs). There are varieties of researches with different approaches in this area. However, the lack of a comprehensive study to focus on addressing simple questions from all aspects is tangible. In this paper, we present a comprehensive survey of answering simple questions to classify available techniques and compare their advantages and drawbacks in order to have better insights of existing issues and recommendations to direct future works. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Review
A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions
Information 2021, 12(6), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/info12060232 - 28 May 2021
Cited by 2 | Viewed by 1312
Abstract
In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on [...] Read more.
In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users. Full article
(This article belongs to the Special Issue Knowledge Graphs for Search and Recommendation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Collective intelligence at the crossroads of factuality, subjectivity and serendipity
Authors: Philippe Lemoisson
Affiliation: CIRAD - Environnements et Sociétés UMR TETIS (Territoires Environnement Télédétection et Information Spatiale)
Abstract: According to G. M. Edelman, individual intelligence results from adaptive knowledge paths supported by our neuronal circuitry. This paper is about collective intelligence according to the ViewpointS paradigm. It is an attempt to assess collective intelligence by demonstrating the emergence of adaptive “knowledge paths” when the interactions of a community of people is mediated by a “knowledge graph” KG consisting of “resources” (human agents including the members of the community, artificial agents, numeric documents, topics or events) and authored connections between these resources called “viewpoints”. The connections are typed; they may be factual, subjective, or result from serendipity. The community of people (human agents) feed the KG by inputting resources and viewpoints and reversely exploit the KG for information search, based on a metric distance between resources. Each member exploits the viewpoints of the whole community to find information, and then reacts to his/her encounters by emitting new viewpoints. These information loops result in evolving the shared KG according to the members’ facts, beliefs and serendipitous browsing; this paradigm has been described and concept-proven in other publications. This paper proposes a real-life scenario addressing the question of collective intelligence through an experiment in three steps. We start with a community of people, factually connected through past events (factual viewpoints) and accepting to participate to the experiment. Step 1: each participant inputs 9 documents and 3 topics of interest which are linked more or less strongly to the documents (via subjective viewpoints). The participants evaluate their familiarity with all the topics (their own topics of interest, as well as the topics brought in by the others) on a Lickert scale. We compute the distances between the participants and the topics, and exhibit an initial knowledge map. Step 2: the participants freely browse into the shared KG and feedback to their encounters (subjective and serendipitous viewpoints). Step 3: the participants re-evaluate their familiarity with all the topics on the Lickert scale, and the distances between the participants and the topics are re-computed; we exhibit a final knowledge map. The hypothesis of collective intelligence induced by the experiment is discussed in relation with the emergence of knowledge paths within the final map.

Title: Hierarchical Classification of Semantic Answer Types For Short Text Questions
Authors: Remzi Çelebi
Affiliation: Institute of Data Science, Maastricht University
Abstract: Question answering systems have recently become integrated with many smart devices and search engines. One of the methods to filter irrelevant results and improve general search and retrieval performance in question answering systems is to predict the type of answer. Predicting granular answer types for a question from a big ontology is a greater challenge due to the large number of possible types. Here we present our approach to response type prediction using the datasets provided for the International Semantic Web Conference (ISWC 2020) SMART Task Challenge. The task consists of training data of questions, categories, and types from large ontologies, and the challenge participants are asked to provide the categories and types from the WikiData and the DBpedia ontologies questions in the test dataset. The DBpedia dataset contains 17,571 training questions and 4,393 test questions, and the WikiData dataset has 18,251 training questions and 4,571 test questions. We propose a 3-step approach to tackle the challenge task. We start by building a classifier that predicts the category of the types and build another classifier just for resource types. The second model will predict the most general (frequent) type for each question, ignoring the type hierarchy. For these two models, we use a multi-class text classification algorithm built-in fastai library. Next, we train a third classifier to find more specific types (sub-types) for each question based on the previous predicted general types. This problem is modeled as a binary classification where the given generic type and specific type match the question in the positive examples, but not for negative examples. On the DBpedia test set, we achieve a score of 0.62 with [email protected] metric and 0.61 with [email protected] metric.

Title: Unsupervised DNF Blocking for Efficient Linking of Knowledge Graphs and Tables
Authors: Mayank Kejriwal
Affiliation: Department of Industrial and Systems Engineering, and a Research Lead at the USC Information Sciences Institute
Abstract: Entity Resolution concerns identifying co-referent entity pairs across datasets, including knowledge graphs (KG). A typical workflow comprises two steps, and is an important step in an applied KG search and analytics pipeline. In the first step, a blocking method uses a one-many function called a blocking scheme to map entities to blocks. In the second step, entities sharing a block are paired and compared. Current Disjunctive Normal Form blocking scheme learners (DNF-BSLs) apply only to structurally homogeneous tables. We present an unsupervised algorithmic pipeline for learning DNF blocking schemes on Resource Description Framework (RDF) graph datasets, as well as structurally heterogeneous tables. Previous DNF-BSLs are admitted as special cases. We evaluate the pipeline on six real-world dataset pairs. Unsupervised results are shown to be competitive with supervised and semi-supervised baselines. To the best of our knowledge, this is the first unsupervised DNF-BSL that admits RDF graphs and structurally heterogeneous tables as inputs, thereby enabling many more applications of a heterogeneous nature.

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