Aligning ontologies with subsumption and equivalence relations in Linked Data

Zong, Nansu; Nam, So Hyun; Eom, Jae-Hong; Ahn, Jinhyun; Joe, Hyunwhan; Kim, Hong-Gee

doi:10.1016/j.knosys.2014.11.022

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…The linked data consists of a set of entities E = { e 1 , e 2 , …, e m } and a set of links L = { l 1 , l 2 , …, l n } that connect the entities. It should be noted that since we are searching entities, only the instances in Assertion Box (A-Box), containing extensional knowledge and assertions about individuals (instances) [32], are considered as entities in this paper. For example, with the A-Box of DBpedia Ontology, it can be learned that “DBpedia:Upper_trunk” is an instance of “DBpediaOntology:Nerve”, and thus “DBpedia:Upper_trunk” is considered as an entity.…”

Section: Inter-topic Search Based On Perankmentioning

confidence: 99%

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships

Zong

Lee

Ahn

et al. 2017

Computers in Biology and Medicine

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Objective The keyword-based entity search restricts search space based on the preference of search. When given keywords and preferences are not related to the same biomedical topic, existing biomedical Linked Data search engines fail to deliver satisfactory results. This research aims to tackle this issue by supporting an inter-topic search—improving search with inputs, keywords and preferences, under different topics. Methods This study developed an effective algorithm in which the relations between biomedical entities were used in tandem with a keyword-based entity search, Siren. The algorithm, PERank, which is an adaptation of Personalized PageRank (PPR), uses a pair of input: (1) search preferences, and (2) entities from a keyword-based entity search with a keyword query, to formalize the search results on-the-fly based on the index of the precomputed Individual Personalized PageRank Vectors (IPPVs). Results Our experiments were performed over ten linked life datasets for two query sets, one with keyword-preference topic correspondence (intra-topic search), and the other without (inter-topic search). The experiments showed that the proposed method achieved better search results, for example a 14% increase in precision for the inter-topic search than the baseline keyword-based search engine. Conclusion The proposed method improved the keyword-based biomedical entity search by supporting the inter-topic search without affecting the intra-topic search based on the relations between different entities.

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Section: Inter-topic Search Based On Perankmentioning

confidence: 99%

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships

Zong

Lee

Ahn

et al. 2017

Computers in Biology and Medicine

Self Cite

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“…The recent ontology-based data access (OBDA) technology is also partially related to this research, in the sense that it provides integration tools based on ontologies [38] to access transparently different data sources and allows end users to express complex information requirements with familiar and comprehensible terms. The work in [12] is an example of OBDA where they integrate concept browsing, facet-based search, and visual query manipulation to allow the user to express their information needs and hide the query language syntax.…”

Section: Related Workmentioning

confidence: 99%

Statistically-driven generation of multidimensional analytical schemas from linked data

Nebot

Berlanga

2016

Knowledge-Based Systems

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The ever-increasing Linked Data (LD) initiative has given place to open, large amounts of semi-structured and rich data published on the Web. However, effective analytical tools that aid the user in his/her analysis and go beyond browsing and querying are still lacking. To address this issue, we propose the automatic generation of multidimensional analytical stars (MDAS). The success of the multidimensional (MD) model for data analysis has been in great part due to its simplicity. Therefore, in this paper we aim at automatically discovering MD conceptual patterns that summarize LD. These patterns resemble the MD star schema typical of relational data warehousing. The underlying foundations of our method is a statistical framework that takes into account both concept and instance data. We present an implementation that makes use of the statistical framework to generate the MDAS. We have performed several experiments that assess and validate the statistical approach with two well-known and large LD sets.

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“…Currently, schema integration, including taxonomy merging, is regarded as a vital, yet challenging task. Various schema integration approaches have been proposed to deal with the problem [9][10][11]. The process of taxonomies merging can be symmetric or asymmetric with respect to the input taxonomies.…”

Section: Introductionmentioning

confidence: 99%

A multi-strategy approach for the merging of multiple taxonomies

Mao

Yang

et al. 2020

Journal of Information Science

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Taxonomy merging is an important work to provide a uniform schema for several heterogeneous taxonomies. Previous studies primarily focus on merging two taxonomies in a specific domain, while the merging of multiple taxonomies has been neglected. This article proposes a taxonomy merging approach to automatically merge multiple source taxonomies into a target taxonomy in an asymmetric manner. The approach adopts a strategy of breaking up the whole into parts to decrease the complexity of merging multiple taxonomies and employs a block-based method to reduce the scale of measuring semantic relations between concept pairs. In addition, for the problem of multiple inheritance, a method of topical coverage is proposed. Experiments conducted on synthetic and real-world scenarios indicate that the proposed merging approach is feasible and effective to merge multiple taxonomies. In particular, the proposed approach works well in the aspects of limiting the semantic redundancy and establishing high-quality hierarchical relations between concepts.

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Aligning ontologies with subsumption and equivalence relations in Linked Data

Cited by 15 publications

References 12 publications

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships

Statistically-driven generation of multidimensional analytical schemas from linked data

A multi-strategy approach for the merging of multiple taxonomies

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