The paper is aimed to present a methodology of learning personalisation based on applying Resource Description Framework (RDF) standard model. Research results are two-fold: first, the results of systematic literature review on Linked Data, RDF “subject-predicate-object” triples, and Web Ontology Language (OWL) application in education are presented, and, second, RDF triples-based learning personalisation methodology is proposed. The review revealed that OWL, Linked Data, and triples-based RDF standard model could be successfully used in education. On the other hand, although OWL, Linked Data approach and RDF standard model are already well-known in scientific literature, only few authors have analysed its application to personalise learning process, but many authors agree that OWL, Linked Data and RDF-based learning personalisation trends should be further analysed. The main scientific contribution of the paper is presentation of original methodology to create personalised RDF triples to further development of corresponding OWL-based ontologies and recommender system. According to this methodology, RDF-based personalisation of learning should be based on applying students’ learning styles and intelligent technologies. The main advantages of this approach are analyses of interlinks between students’ learning styles according to Felder-Silverman learning styles model and suitable learning components (learning objects and learning activities). There are three RDF triples used while creating the methodology: “student’s learning style – requires – suitable learning objects”, “student’s learning style – requires – suitable learning activities”, and “suitable learning activities – require – suitable learning objects”. In the last triple, “suitable learning activities” being the object in the 2nd triple, becomes the subject in the 3rd triple. The methodology is based on applying pedagogically sound vocabularies of learning components (i.e. learning objects and learning activities), experts’ collective intelligence to identify learning objects and learning methods / activities that are most suitable for particular students, and intelligent technologies (i.e. ontologies and recommender system). This methodology based on applying personalised RDF triples is aimed at improving learning quality and effectiveness.

/pdf/application-of-resource-description-framework-to-personalise-s31t21qirv.pdf

Application of Resource Description Framework to Personalise Learning: Systematic Review and Methodology

Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL).

/pdf/building-ontology-driven-tutoring-models-for-intelligent-34uiwz2yuz.pdf

Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

More and more weakly structured, and irregular data sources are becoming available every day. The schema of these sources is useful for a number of tasks, such as query answering, exploration and summarization. However, although semantic web data might contain schema information, in many cases this is completely missing or partially defined. In this paper, we present a survey of the state of the art on schema information extraction approaches. We analyze and classify these approaches into three families: (1) approaches that exploit the implicit structure of the data, without assuming that some explicit statements on the schema are provided in the dataset; (2) approaches that use the explicit schema statements contained in the dataset to complement and enrich the schema, and (3) those that discover structural patterns contained in a dataset. We compare these studies in terms of their approach, advantages and limitations. Finally we discuss the problems that remain open.

A survey on semantic schema discovery

The rapid explosion of linked data has resulted into many weakly structured and incomplete data sources, where typing information might be missing. On the other hand, type information is essential for a number of tasks such as query answering, integration, summarization and partitioning. Existing approaches for type discovery, either completely ignore type declarations available in the dataset (implicit type discovery approaches), or rely only on existing types, in order to complement them (explicit type enrichment approaches). Implicit type discovery approaches are based on instance grouping, which requires an exhaustive comparison between the instances. This process is expensive and not incremental. Explicit type enrichment approaches on the other hand, are not able to identify new types and they can not process data sources that have little or no schema information. In this paper, we present HInT, the first incremental and hybrid type discovery system for RDF datasets, enabling type discovery in datasets where type declarations are missing. To achieve this goal, we incrementally identify the patterns of the various instances, we index and then group them to identify the types. During the processing of an instance, our approach exploits its type information, if available, to improve the quality of the discovered types by guiding the classification of the new instance in the correct group and by refining the groups already built. We analytically and experimentally show that our approach dominates in terms of efficiency, competitors from both worlds, implicit type discovery and explicit type enrichment while outperforming them in most of the cases in terms of quality.

https://dl.acm.org/doi/pdf/10.1145/3468791.3468808

HInT: Hybrid and Incremental Type Discovery for Large RDF Data Sources

The rise of knowledge graphs as a medium for storing and organizing large amounts of data has spurred research interest in automated methods for reasoning with and extracting information from this representation of data. One area which seems to receive less attention is that of inducing a class taxonomy from such graphs. Ontologies, which provide the axiomatic foundation on which knowledge graphs are built, are often governed by a set of class subsumption axioms. These class subsumptions form a class taxonomy which hierarchically organizes the type classes present in the knowledge graph. Manually creating and curating these class taxonomies oftentimes requires expert knowledge and is time costly, especially in large-scale knowledge graphs. Thus, methods capable of inducing the class taxonomy from the knowledge graph data automatically are an appealing solution to the problem. In this paper, we propose a simple method for inducing class taxonomies from knowledge graphs that is scalable to large datasets. Our method borrows ideas from tag hierarchy induction methods, relying on class frequencies and co-occurrences, such that it requires no information outside the knowledge graph’s triple representation. We demonstrate the use of our method on three real-world datasets and compare our results with existing tag hierarchy induction methods. We show that our proposed method outperforms existing tag hierarchy induction methods, although both perform well when applied to knowledge graphs.

/pdf/a-simple-method-for-inducing-class-taxonomies-in-knowledge-3pmtry29j9.pdf

A Simple Method for Inducing Class Taxonomies in Knowledge Graphs

The growing presence of Resource Description Framework (RDF) as a data representation format on the web brings opportunity to develop new approaches to data analysis. One of important tasks is learning categories of data. Although RDF-based data is equipped with properties indicating its type and subject, building categories based on similarity of entities contained in the data provides a number of benefits. It mimics an experience-based learning process, leads to construction of an extensional-based hierarchy of categories, and allows to determine degrees of membership of entities to the identified categories. Such a process is addressed in the paper.

Learning Categories from Linked Open Data

The Resource Description Framework (RDF) proposed as a part of Semantic Web becomes an important way of representing data and information on the web. Its intrinsic feature of high connectivity has a direct impact on the ability to express semantics of data, as well as development of methods for extraction of information and knowledge embedded in the RDF data.In this paper, we proposed a procedure to construct a model of data that has been experienced, i.e., discovered and collected, by software agents and systems. The process is based on processing previously constructed concepts of data. The model is represented as a hierarchy that contains definitions of concepts, their instances, as well as relations found in the data. Each concept definition is represented as a set of features. The features are relations between a given concept definition and definitions of other concepts. The proposed methodology allows for determining importance of features and their degrees of contribution to definitions. This reflects realistic, real-life connections and dependencies existing between generalized concepts.

Modelling of Experienced-Based Data in Linked Data Environment

Linked Open Data (LOD) consists of numerous data stores that are highly interconnected. LOD stores use Resource Description Framework (RDF) as a data representation format. A graph-based nature of RDF brings an opportunity to develop new approaches for accumulating data from multiple sources characterized by different levels of confidence in them. Recently, a participatory learning mechanism has been extended to cope with RDF. It is an attractive way of integrating new pieces of information with already known ones. Further, it has been recognized that pieces of information describing entities can have a disjunctive or conjunctive form. This paper uses an RDF-based participatory learning process to aggregate information obtained from multiple data stores. This process provides mechanisms that determine overall certainty in combined data based on levels of confidence in already known pieces of information and new ones. The behavior of such a process used for integrating information equipped with different levels of uncertainty is presented, and a simple case study is included.

Learning Processes Based on Data Sources with Certainty Levels in Linked Open Data

Graph-based data representation formats enable more advanced processing of data that leads to better utilization of information stored and available on the web. Intrinsic high connectedness of such representation provides a means to create methods and techniques that can assimilate new data and build knowledge-like data structures. Such procedures resemble a human-like way of dealing with information.

Clustering of Propositions Equipped with Uncertainty

Graph-based data formats are popular ways of representing information, while graph-processing engines and graph databases become preferable tools for handling data of different size. World Wide Web Consortium has introduced a graph-based data format called Resource Description Framework (RDF) as the part of its Semantic Web initiative. The intrinsic features of RDF, i.e., its interconnectivity and simplicity of expressing information as triples containing two entities connected by a property, provide new possibilities of analyzing and absorbing information.

Dynamic Analysis of Participatory Learning in Linked Open Data: Certainty and Adaptation

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