Ontology understanding without tears: The summarization approach

Troullinou, Georgia; Kondylakis, Haridimos; Daskalaki, Evangelia; Plexousakis, Dimitris

doi:10.3233/sw-170264

Cited by 30 publications

(30 citation statements)

References 30 publications

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“…Summarising an ontology involves identifying the key concepts in an ontology [56]. An ontology summary should be concise, yet it needs to convey enough information to enable ontology understanding and to provide sufficient coverage of the entire ontology [57]. Centrality measures have been used in [52] to identify key concepts and produce RDF summaries.…”

Section: Identifying Key Entities In Data Graphsmentioning

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: Identifying Key Entities In Data Graphsmentioning

confidence: 99%

Using knowledge anchors to facilitate user exploration of data graphs

Al-Tawil

Dimitrova

Thakker

2020

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“…Here, we will follow an approach similar to [26], which imposes a convenient graphtheoretic view of RDF data that is closer to the way the users perceive their datasets. As such, we separate between the schema and the instances of an RDFS KB, represented in separate graphs (G S and G I , respectively).…”

Section: Preliminariesmentioning

confidence: 99%

“…Having a way to rank the schema nodes of an RDFS KB according to the perceived importance, we then focus on selecting the paths that link those nodes, aiming to produce a valid sub-schema graph. As the main problem of previous approaches [17,26] was the introduction of many additional nodes (besides the top-k ones), in this paper, we focus on selecting the paths that introduce the minimum number of additional nodes to the final summary graph. As such, we model the problem of linking the most important nodes as a variation of the well-known Graph Steiner-Tree problem (GSTP) [27].…”

Section: Linking Important Nodesmentioning

confidence: 99%

Exploring RDFS KBs Using Summaries

Troullinou

Kondylakis

Stefanidis

et al. 2018

Lecture Notes in Computer Science

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Ontology summarization aspires to produce an abridged version of the original data source highlighting its most important concepts. However, in an ideal scenario, the user should not be limited only to static summaries. Starting from the summary, s/he should be able to further explore the data source requesting more detailed information for a particular part of it. In this paper, we present a new approach enabling the dynamic exploration of summaries through two novel operations zoom and extend. Extend focuses on a specific subgraph of the initial summary, whereas zoom on the whole graph, both providing granular information access to the end-user. We show that calculating these operators is NPcomplete and provide approximations for their calculation. Then, we show that using extend, we can answer more queries focusing on specific nodes, whereas using global zoom, we can answer overall more queries. Finally, we show that the algorithms employed can efficiently approximate both operators.

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“…Relative Cardinality (RC) Whereas in the above approaches weights are empirically configured, we highlight relative cardinality [18,19], which is a way of automatically weighting edges for calculating weighted degree. In a class graph, the cardinality of an edge which represents a property connecting two classes is the number of the corresponding instances of the classes connected with that specific type of property.…”

Section: Centrality-based Measuresmentioning

confidence: 99%

Graph-Based Methods for Ontology Summarization: A Survey

Pouriyeh

Allahyari

Liu

et al. 2018

2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

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Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper, we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research.

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Ontology understanding without tears: The summarization approach

Cited by 30 publications

References 30 publications

Using knowledge anchors to facilitate user exploration of data graphs

Using knowledge anchors to facilitate user exploration of data graphs

Exploring RDFS KBs Using Summaries

Graph-Based Methods for Ontology Summarization: A Survey

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