Special Issue "Knowledge Graphs for Search and Recommendation"
Deadline for manuscript submissions: 15 November 2021.
Interests: natural language processing; entity linking; information retrieval; recommender systems
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
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.
- 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.
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.