EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities

Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events

International Conferences of Bibliometrics

Conferences are deeply connected to research fields, in this case bibliometrics. As such, they are a venue to present and discuss current and innovative research, and play an important role for the scholarly community. In this article, we provide an overview on the history of conferences in bibliometrics. We conduct an analysis to list the most prominent conferences that were announced in the newsletter by ISSI, the International Society for Scientometrics and Informetrics. Furthermore, we describe how conferences are connected to learned societies and journals. Finally, we provide an outlook on how conferences might change in future

International Conferences of Bibliometrics

Conferences are deeply connected to research fields, in this case bibliometrics. As such, they are a venue to present and discuss current and innovative research, and play an important role for the scholarly community. In this article, we provide an overview on the history of conferences in bibliometrics. We conduct an analysis to list the most prominent conferences that were announced in the newsletter by ISSI, the International Society for Scientometrics and Informetrics. Furthermore, we describe how conferences are connected to learned societies and journals. Finally, we provide an outlook on how conferences might change in future

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade

The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics

Scholarly event characteristics in four fields of science : a metrics-based analysis

One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences.However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution.In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics.In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy.This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work.Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org. © 2020, The Author(s)

A comprehensive quality assessment framework for scientific events

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories—events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics’ values coincide with the intuitive agreement of the community on its “top conferences”. Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors

A comprehensive quality assessment framework for scientific events

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories—events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics’ values coincide with the intuitive agreement of the community on its “top conferences”. Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors. © 2020, The Author(s)

Processing Analytical Queries in the AWESOME Polystore [Information Systems Architectures]

Modern big data applications usually involve heterogeneous data sources and analytical functions, leading to increasing demand for polystore systems, especially analytical polystore systems. This paper presents AWESOME system along with a domain-specific language ADIL. ADIL is a powerful language which supports 1) native heterogeneous data models such as Corpus, Graph, and Relation; 2) a rich set of analytical functions; and 3) clear and rigorous semantics. AWESOME is an efficient tri-store middle-ware which 1) is built on the top of three heterogeneous DBMSs (Postgres, Solr, and Neo4j) and is easy to be extended to incorporate other systems; 2) supports the in-memory query engines and is equipped with analytical capability; 3) applies a cost model to efficiently execute workloads written in ADIL; 4) fully exploits machine resources to improve scalability. A set of experiments on real workloads demonstrate the capability, efficiency, and scalability of AWESOME

Dark Matter Indirect Detection and Collider Search: the Good and the Bad

In this work I aim to point out some theoretical issues and caveats in DM search. In the first chapters I review the evidence for DM existence, the DM candidates and the different kinds of DM experimental search. The bulk of the work investigates three different topics. In the first topic, concerning neutrino from the Sun, I show the fact that evaporation does not allow to probe part of the parameter space, in the low mass range. In the second one, I show that, like in the case of the detected positron excess, that could be explained both by DM or by astrophysical source, even a possible excess of antiprotons could suffer from the same kind of degeneracy. In the third part, I consider DM search at collider. I point out some problems about using the EFT low-energy approximation at LHC, arising from the fact that the experimental bounds and the average energy of collisions at LHC are of the same order of magnitude. Afterward, to take this fact into account, I propose a method to rescale experimental bounds, and I review an alternative way of analyzing experimental results, that is using Simplified Models. Finally, I also show which is the part of the parameter space for both Simplified Models and EFT giving the DM the right relic abundance, in the case of thermal freeze-out

Using Knowledge Graphs to enhance the utility of Curated Document Databases

The research presented in this thesis is directed at the generation, maintenance and query ing of Curated Document Databases (CDDs) stored as literature knowledge graphs. Liter ature knowledge graphs are graphs where the vertices represent documents and concepts; and the edges provided links between concepts, and concepts and documents. The central motivation for the work was to provide CDD administrators with a useful mechanism for creating and maintaining literature knowledge graph represented CDDs, and for end users to utilise them. The central research question is “What are some appropriate techniques that can be used for generating, maintaining and utilizing literature knowledge graphs to support the concept of CDDs?”. The thesis thus addresses three issues associated with literature knowledge graphs: (i) their construction, (ii) their maintenance so that their utility can be continued, and (iii) the querying of such knowledge graphs. With respect to the first issue, the Open Information Extraction for Knowledge Graph Construction (OIE4KGC) approach is proposed founded on the idea of using open information extrac tion. Two open information extraction tools were compared, the RnnOIE tool and the Leolani tool. The RnnOIE tool was found to be effective for generation of triples from clinical trial documents. With respect to the second issue two approaches are proposed for maintaining knowledge graph represented CDDs; the CN approach and the Knowledge Graph And BERT Ranking (GRAB-Rank) approach. The first proposed approach used a feature vector representation; and the second a unique hybrid domain specific document embedding. The hybrid domain-specific document embedding combines a Bidirectional En coder Representations from Transformers embedding with a knowledge graph embedding. This proposed embedding was used for document representation in a LETOR model. The idea was to rank a set of potential documents. The Grab-Rank embedding based LETOR approach was found to be effective. For the third identified issue the standard solution is to represent both the query to be addressed and the documents in the knowledge graph in a manner that will allow the documents to be ranked with respect to the query. The solution proposed for this was to utilize a hybrid embedding for query resolution. Two forms of embedding are utilized for query resolution: (i) a Continuous Bag-Of-Words embedding was combined with graph embedding and (ii) for the second BERT and Sci-BERT em bedding were combined with graph embedding. The evaluation indicates that the CBOW embedding combined with graph embedding was found to be effective