Abstract. Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. It crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping.To this end, we have implemented a number of different statistical co-occurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items. We have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset as compared against the Gold Standard.Our systematic study shows excellent results of instance-based matching in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures.

This is a preprint of an article accepted to be published in a special issue of Scientometrics: Gläser, J., Scharnhorst, A. and Glänzel, W. (eds), Same data -different results? Towards a comparative approach to the identification of thematic structures in science. This is the last paper in the Synthesis section of the special issue on 'Same Data, Different Results'. We first provide a framework of how to describe and distinguish approaches to topic extraction from bibliographic data of scientific publications. We then compare solutions delivered by the different topic extraction approaches in this special issue, and explore where they agree and differ. This is achieved without reference to a ground truth, since we have to assume the existence of multiple, equally important, valid perspectives and want to avoid bias through the adoption of an ad-hoc yardstick. Instead, we apply different ways to quantitatively and visually compare solutions to explore their commonalities and differences and develop hypotheses about the origin of these differences. We conclude with a discussion of future work needed to develop methods for comparison and validation of topic extraction results, and express our concern about the lack of access to non-proprietary benchmark data sets to support method development in the field of scientometrics.

Document clustering is generally the first step for topic identification. Since many clustering methods operate on the similarities between documents, it is important to build representations of these documents which keep their semantics as much as possible and are also suitable for efficient similarity calculation. The metadata of articles in the Astro dataset contribute to a semantic matrix, which uses a vector space to capture the semantics of entities derived from these articles and consequently supports the contextual exploration of these entities in LittleAriadne. However, this semantic matrix does not allow to calculate similarities between articles directly. In this paper, we will describe in detail how we build a semantic representation for an article from the entities that are associated with it. Base on such semantic representations of articles, we apply two standard clustering methods, K-Means and the Louvain community detection algorithm, which leads to our two clustering solutions labelled as OCLC-31 (standing for K-Means) and OCLC-Louvain (standing for Louvain). In this paper, we will give the implementation details and a basic comparison with other clustering solutions that are reported in this special issue.Comment: Special Issue of Scientometrics: Same data - different results? Towards a comparative approach to the identification of thematic structures in scienc

PurposeThis paper draws on the perspective of social networks to examine when 3PLs initiate low-carbon supply chain integration (LCSCI) in decarbonising supply chains.Design/methodology/approachA questionnaire survey was conducted on a sample of 348 Chinese 3PLs. Stepwise regression was adopted to analyse the data.FindingsIt is found that LCSCI has a positive effect on firm performance. From the social network perspective, a larger scope of outsourcing increases 3PLs' embeddedness, which increases their chance of success in initiating LCSCI, especially for 3PLs with higher decarbonisation capabilities. Interestingly, although the pressure from government regulation can also motivate LCSCI, it is less effective for 3PLs with higher decarbonisation capabilities because they can be too embedded in the network to freely adapt to regulations.Research limitations/implicationsThis study has investigated 3PL-initiated LCSCI only from the view of 3PLs. A dyadic approach which includes the perception of customers would be desirable.Practical implicationsThe results highlight the critical role of 3PLs as supply chain orchestrators in decarbonising supply chains, and the effectiveness of LCSCI as a competitive strategy of 3PLs. Coercive pressures from government regulations are not constraints but resources for 3PLs in initiating LCSCI, especially in markets where the 3PLs have insufficient decarbonisation capabilities.Originality/valueThis study contributes to theories on 3PLs' interorganizational low-carbon initiatives, LCSCI, and the paradox of social networks in supply chains.

In this study, a rapid and specific assay for the detection of porcine circovirus type 3 (PCV3) was established using loop-mediated isothermal amplification (LAMP). Four primers were specifically designed to amplify PCV3. The LAMP assay was effectively optimized to amplify PCV3 by water bath at 60°C for 60 min. The detection limit was approximately 1 × 10 copy in this LAMP assay. Compared to porcine circovirus type 2 (PCV2), both gE and gD genes of pseudorabies virus (PRV) and porcine parvovirus (PPV), the LAMP assay showed a high specific detection of PCV3. A visible detection method was developed using SYBR Green I to recognize the results rapidly. Based on the detection of 20 clinical tissue samples, the LAMP assay was more practical and convenient than classical PCR due to its simplicity, high sensitivity, rapidity, specificity, visibility and cost efficiency.

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Published by the IEEE Computer Societyother controlled vocabularies) specific to fields, institutions, and even collections. The desire to make CH resources available to the general public (for example, see www. europeana.eu) increases the need to facilitate interoperability across different contexts.By providing representational standards such as the Simple Knowledge Organization System (SKOS; www.w3.org/2004/02/skos) and generic tool support, the Semantic Web community has taken a prominent role in this facilitation. Its ontology-matching branch aims at developing technology to produce alignments-that is, sets of semantic mappings bet ween elements from different vocabularies. 1 One can exploit alignments, for instance, to access a collection via thesauri it is not originally indexed with, to interconnect distributed, differently annotated collections on the object level, or to merge two thesauri to rationalize thesaurus maintenance.Unfortunately, our experience shows that existing matching tools often do not perform well in CH applications. 2 We believe part of the problem is that they strive for generality. To this end, we argue that the generation and evaluation of thesaurus alignments must take into account well-understood realworld application contexts and their specific requirements. Ontology MatchingOntology matching aims at determining the semantic relations bet ween elements of two given knowledge organization systems (for example, ontologies and thesauri). 1 The set of semantic relations usually comprises concept equivalence, hierarchical concept links

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