In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new... Show More

In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.

Properties are used to describe entities, and a part of them are likely to be clustered together to constitute an aspect. For example, first name, middle name and last name are usually gathered to... Show More

Properties are used to describe entities, and a part of them are likely to be clustered together to constitute an aspect. For example, first name, middle name and last name are usually gathered to describe a person's name. However, existing automated approaches to property clustering remain far from satisfactory for an open domain like Linked Data. In this paper, the authors firstly investigated the relatedness between properties using 13 different measures. Then, they employed seven clustering algorithms and two combination methods for property clustering. Based on a sample set of Linked Data, the authors empirically studied property clustering in Linked Data and found that a proper combination of different measures and clustering algorithms gave rise to the best result. Additionally, they reported how property clustering can improve user experience in an entity browsing system.

In this article, the authors propose a novel search model: Multi-Target Search (MT search in brief). MT search is a keyword-based search model on Semantic Associations in Linked Data. Each search... Show More

In this article, the authors propose a novel search model: Multi-Target Search (MT search in brief). MT search is a keyword-based search model on Semantic Associations in Linked Data. Each search contains multiple sub-queries, in which each sub-query represents a certain user need for a certain object in a group relationship. They first formularize the problem of association search, and then introduce their approach to discover Semantic Associations in large-scale Linked Data. Next, they elaborate their novel search model, the notion of Virtual Document they use to extract linguistic features, and the details of search process. The authors then discuss the way search results are organized and summarized. Quantitative experiments are conducted on DBpedia to validate the effectiveness and efficiency of their approach.

In this article, the authors design two metric sets considering Richness and Correctness based on a quasi-formal conceptual representation. They also design a novel metric set on overlapped... Show More

In this article, the authors design two metric sets considering Richness and Correctness based on a quasi-formal conceptual representation. They also design a novel metric set on overlapped instances of different KBs to make the metric results comparable. Finally, they use random sampling techniques to reduce human efforts for assessing the correctness. The authors evaluate three large Chinese KBs including DBpedia Chinese, Zhishi.me and SSCO comparatively, and further compare them with English KBs in terms of data set qualities. They also compare different versions of DBpedia and YAGO. The findings in these KBs not only give a detailed report of the current situation of extracted KBs, but also show the effectiveness of their methods in assessing the quality of Web-Scale KBs comparatively.

The study of paraconsistent reasoning with ontologies is especially important for the Semantic Web since knowledge is not always perfect within it. However, classical OWL reasoners cannot support... Show More

The study of paraconsistent reasoning with ontologies is especially important for the Semantic Web since knowledge is not always perfect within it. However, classical OWL reasoners cannot support reasoning with inconsistent ontologies. In this article, the authors present a plugin-based framework called prose to provide rich paraconsistent reasoning services for OWL ontologies, whose architecture contains the three following parts: a classical OWL reasoner, a multi-valued transformer, and an OWL API connecting with them. Within the proposed framework prose, they implement different multi-valued paraconsistent reasoning in the OWL. Moreover, they select three popular classical OWL reasoners and two typical kinds of reasoning services for users. As the authors excepted, prose does exactly enable current classical OWL reasoners to tolerate inconsistency in a simple and convenient way. Finally, they evaluate the three reasoners in a united framework (prose) and, as a result, those results can amend the analysis of the three reasoners on inconsistent ontologies.

Recently, the number and heterogeneity of life science datasets published on the Web have increased significantly. However, biomedical scientists face numerous serious difficulties finding, using... Show More

Recently, the number and heterogeneity of life science datasets published on the Web have increased significantly. However, biomedical scientists face numerous serious difficulties finding, using and publishing useful databases. To address these issues, the authors developed a Resource Description Framework-based database platform, called the RIKEN MetaDatabase (http://metadb.riken.jp), that allows biologists to develop, publish and integrate multiple databases easily. The platform manages the metadata of both research and individual data described using standardised vocabularies and ontologies, and has a simple browser-based graphical user interface to view data including tabular and graphical forms. The platform was released in April 2015, and 113 databases, including mammalian, plant, bioresource and image databases, with 26 ontologies have been published using this platform as of January 2017. This paper describes the technical knowledge obtained through the development and operation of the RIKEN MetaDatabase to accelerate life science data distribution.