Distributed human computation framework for linked data co-reference resolution

Distributed 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

Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology

Wiljes C, Cimiano P. Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology. In: Proceedings of the Workshop on the Semantic Publishing (SePublica 2012). 2012: 48-59.The Web was designed to improve the way people work together. The Semantic Web extends the Web with a layer of Linked Data that offers new paths for scientific publishing and co-operation. Experimental raw data, released as Linked Data, could be discovered automatically, fostering its reuse and validation by scientists in different contexts and across the boundaries of disciplines. However, the technological barrier for scientists who want to publish and share their research data as Linked Data remains rather high. We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data. A key element of our methodology is the role of a scientific data curator, who is proficient in Linked Data technologies and works in close co-operation with the scientist

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Research on Linked Data and Co-reference Resolution

This project report details work carried out in collaboration between the University of Southampton and the Korea Institute of Science and Technology Information, focussing on an RDF dataset of academic authors and publications. Activities included the conversion of the dataset to produce Linked Data, the identification of co-references in and between datasets, and the development of an ontology mapping service to facilitate the integration of the dataset with an existing Semantic Web application, RKBExplorer.com

Co-evolution of RDF Datasets

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.Comment: 18 pages, 4 figures, Accepted in ICWE, 201

Research on Linked Data and Co-reference Resolution

This project report details work carried out in collaboration between the University of Southampton and the Korea Institute of Science and Technology Information, focussing on an RDF dataset of academic authors and publications. Activities included the conversion of the dataset to produce Linked Data, the identification of co-references in and between datasets, and the development of an ontology mapping service to facilitate the integration of the dataset with an existing Semantic Web application, RKBExplorer.com

Tris(ethyl-enedi-amine)-cobalt(II) dichloride.

The title compound, [Co(II)(C2H8N2)3]Cl2, was obtained unexpectedly as the product of an attempted solvothermal synthesis of cobalt selenide from the elements in the presence of NH4Cl in ethyl-enedi-amine solvent. The three chelate rings of the distorted octa-hedral [Co(C2H8N2)3](2+) complex cation adopt twisted conformations about their C-C bonds. The spread of cis-N-Co-N bond angles [80.17 (6)-98.10 (6)°] in the title compound is considerably greater than the equivalent data for [Co(III)(C2H8N2)3]Cl3 [Takamizawa et al. (2008 ▶). Angew. Chem. Int. Ed. 47, 1689-1692]. In the crystal, the components are linked by numerous N-H⋯Cl hydrogen bonds, generating a three-dimensional network in which the cationic complexes are stacked in columns along [010] and separated by columns of chloride anions

Molecular gas and star formation towards the IR dust bubble S24 and its environs

We present a multi-wavelength analysis of the infrared dust bubble S24, and its environs, with the aim of investigating the characteristics of the molecular gas and the interstellar dust linked to them, and analyzing the evolutionary status of the young stellar objects (YSOs) identified there. Using APEX data, we mapped the molecular emission in the CO(2-1), $^{13}$CO(2-1), C$^{18}$O(2-1), and $^{13}$CO(3-2) lines in a region of about 5'x 5' in size around the bubble. The cold dust distribution was analyzed using ATLASGAL and Herschel images. Complementary IR and radio data were also used.The molecular gas linked to the S24 bubble, G341.220-0.213, and G341.217-0.237 has velocities between -48.0 km sec$^{-1}$ and -40.0 km sec$^{-1}$. The gas distribution reveals a shell-like molecular structure of $\sim$0.8 pc in radius bordering the bubble. A cold dust counterpart of the shell is detected in the LABOCA and Herschel images.The presence of extended emission at 24 $\mu$m and radio continuum emission inside the bubble indicates that the bubble is a compact HII region. Part of the molecular gas bordering S24 coincides with the extended infrared dust cloud SDC341.194-0.221. A cold molecular clump is present at the interface between S24 and G341.217-0.237. As regards G341.220-0.213, the presence of an arc-like molecular structure at the northern and eastern sections of this IR source indicates that G341.220-0.213 is interacting with the molecular gas. Several YSO candidates are found to be linked to the IR extended sources, thus confirming their nature as active star-forming regions. The total gas mass in the region and the H$_2$ ambient density amount to 10300 M$_{\odot}$ and 5900 cm$^{-3}$, indicating that G341.220-0.213, G341.217-0.237, and the S24 HII region are evolving in a high density medium. A triggering star formation scenario is also investigated.Comment: 17 pages, 16 figures. Submitted to A&A. Revised according to the referee repor

Complete trails of co-authorship network evolution

The rise and fall of a research field is the cumulative outcome of its intrinsic scientific value and social coordination among scientists. The structure of the social component is quantifiable by the social network of researchers linked via co-authorship relations, which can be tracked through digital records. Here, we use such co-authorship data in theoretical physics and study their complete evolutionary trail since inception, with a particular emphasis on the early transient stages. We find that the co-authorship networks evolve through three common major processes in time: the nucleation of small isolated components, the formation of a tree-like giant component through cluster aggregation, and the entanglement of the network by large-scale loops. The giant component is constantly changing yet robust upon link degradations, forming the network's dynamic core. The observed patterns are successfully reproducible through a new network model