1,721,251 research outputs found
Using linked data in purposive social networks
No full textThe Web has provided a platform for people to collaborate by using collective intelligence. Messaging boards, Q&A forums are some examples where people broadcast their issues and other people provide solutions. Such communities are defined as a Purposive Social Network (PSN) in this thesis. PSN is a community where people with similar interest and varied expertise come together, use collective intelligence to solve common problems in the community and build tools for common purpose. Usually, Q&A forums are closed or semi-open. The data are controlled by the websites. Difficulties in the search and discovery of information is an issue. People searching for answers or experts in a website can only see results from its own network, while losing a whole community of experts in other websites. Another issue in Q&A forums is not getting any response from the community. There is a long tail of questions that get no answer.The thesis introduces the Suman system that utilises Semantic Web (SW) and Linked Data technologies to solve above challenges. SW technologies are used to structure the community data so it can be decentralized and used across platforms. Linked Data helps to find related information about linked resources. The Suman system uses available tools to solve name entity disambiguation problem and add semantics to the PSN data. It uses a novel combination of semantic keyword search with traditional text search techniques to find similar questions with answers for unanswered questions to expand the query term with added semantics and uses crowd sourced data to rank the results. Furthermore, the Suman system also recommends experts who can answer those questions. This helps to narrow down the long tail of unanswered questions in such communities.The Suman system is designed using the Design Science methodology and evaluated by users in two experiments. The results were statistically analysed to show that the keywords generated by the Suman system were rated higher than the original keywords from the websites. It also showed that the participants agreed with the algorithm rating for answers provided by the Suman system. StackOverflow and Reddit are used as an example of PSN and to build an application as a proof of concept of the Suman system
Linked data in crowdsourcing purposive social network
Full text linkInternet is an easy medium for people to collaborate and crowdsourcing is an efficient feature of social web where people with common interest and expertise come together to solve specific problems by collective thinking and create a community. It can also be used to filter out important information from large data, remove spams, and gamification techniques are used to reward the users for their contribution and keep a sustainable environment for the growth of the community. Semantic web technologies can be used to structure the community data so it can be combined, decentralized and be used across platform. Using such tools knowledge can be enhanced and easily discovered and merged together. This paper discusses the concept of a purposive social network where people with similar interest and varied expertise come together, use crowdsourcing technique to solve a common problem and build tools for common purpose. The StackOverflow website is chosen to study the purposive network, different network ties and roles of user is studied. Linked Data is used for name disambiguation of keywords and topics for easier search and discovery of experts in a field and provide useful information that is otherwise unavailable in the website
Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum
No full textSingh, Priyanka, Kalunke, Raviraj M., Shukla, Anurag, Tzfadia, Oren, Thulasiram, Hirekodathakallu V., Giri, Ashok P. (2020): Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum. Phytochemistry (112451) 177: 1-11, DOI: 10.1016/j.phytochem.2020.112451, URL: http://dx.doi.org/10.1016/j.phytochem.2020.11245
Cosmology dependence of galaxy cluster scaling relations
No full textThe abundance of galaxy clusters as a function of mass and redshift is a well known powerful cosmological probe, which relies on underlying modelling assumptions on the mass-observable relations (MOR). Some of the MOR parameters can be constrained directly from multi-wavelength observations, as the normalization at some reference cosmology, the mass-slope, the redshift evolution, and the intrinsic scatter. However, the cosmology dependence of MORs cannot be tested with multi-wavelength observations alone. We use magneticum simulations to explore the cosmology dependence of galaxy cluster scaling relations. We run fifteen hydrodynamical cosmological simulations varying Ωm, Ωb, h0, and σ8 (around a reference cosmological model). The MORs considered are gas mass, baryonic mass, gas temperature, Y and velocity dispersion as a function of virial mass. We verify that the mass and redshift slopes and the intrinsic scatter of the MORs are nearly independent of cosmology with variations significantly smaller than current observational uncertainties. We show that the gas mass and baryonic mass sensitively depends only on the baryon fraction, velocity dispersion, and gas temperature on h0, and Y on both baryon fraction and h0. We investigate the cosmological implications of our MOR parametrization on a mock catalogue created for an idealized eROSITA-like experiment. We show that our parametrization introduces a strong degeneracy between the cosmological parameters and the normalization of the MOR. Finally, the parameter constraints derived at different overdensity (Δ500c), for X-ray bolometric gas luminosity, and for different subgrid physics prescriptions are shown in the appendix
Distributed human computation framework for linked data co-reference resolution
Full text linkDistributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud
Fig. 2 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum
No full textFig. 2. Putative camphor biosynthesis pathway. Schematic representation of the putative camphor biosynthesis pathway in O. kilimandscharicum. Genes highlighted in red were cloned and functionally characterized [isopentynyl diphosphate (IPP), dimethylallyl pyrophosphate (DMAPP), geranyl diphosphate (GPP), bornyl diphosphate (BPP), geranyl diphosphate synthase (gpps), bornyl diphosphate synthase (bpps), bornyl diphosphate diphosphatase (bppd), borneol dehydrogenase (bdh)]. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)Published as part of Singh, Priyanka, Kalunke, Raviraj M., Shukla, Anurag, Tzfadia, Oren, Thulasiram, Hirekodathakallu V. & Giri, Ashok P., 2020, Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum, pp. 1-11 in Phytochemistry (112451) 177 on page 4, DOI: 10.1016/j.phytochem.2020.112451, http://zenodo.org/record/829593
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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