Aligning ontologies with subsumption and equivalence relations in Linked Data

A multi-strategy approach for the merging of multiple taxonomies

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.

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.

Procedure Based on Semantic Similarity for Merging Ontologies by Non-Redundant Knowledge Enrichment

The merging procedures of two ontologies are mostly related to the enrichment of one of the input ontologies, i.e. the knowledge of the aligned concepts from one ontology are copied into the other ontology. As a consequence, the resulting new ontology extends the original knowledge of the base ontology, but the unaligned concepts of the other ontology are not considered in the new extended ontology. On the other hand, there are experts-aided semi-automatic approaches to accomplish the task of including the knowledge that is left out from the resulting merged ontology and debugging the possible concept redundancy. With the aim of facing the posed necessity of including all the knowledge of the ontologies to be merged without redundancy, this article proposes an automatic approach for merging ontologies, which is based on semantic similarity measures and exhaustive searching along of the closest concepts. The authors' approach was compared to other merging algorithms, and good results are obtained in terms of completeness, relationships and properties, without creating redundancy.

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

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.