SchenQL: A query language for bibliographic data with aggregations and domain-specific functions

@article{Kreutz2022SchenQLAQ,
  title={SchenQL: A query language for bibliographic data with aggregations and domain-specific functions},
  author={Christin Katharina Kreutz and Martin Blum and Ralf Schenkel},
  journal={2022 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
  year={2022},
  pages={1-5}
}
Current search interfaces of digital libraries are not suitable to satisfy complex or convoluted information needs directly, when it comes to cases such as "Find authors who only recently started working on a topic". They might offer possibilities to obtain this information only by requiring vast user interaction.We present SchenQL, a web interface of a domain-specific query language on bibliographic metadata, which offers information search and exploration by query formulation and navigation… 

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References

SHOWING 1-10 OF 19 REFERENCES

SchenQL: in-depth analysis of a query language for bibliographic metadata

TLDR
This paper presents and evaluates SchenQL, a simple and applicable query language that is accompanied by a prototypical GUI that is suitable for domain experts as well as casual users while still providing the possibility to answer complex information demands.

Ontology-Based Question Answering for Digital Libraries

TLDR
A principled framework for integrating structured metadata and unstructured resource content in a seamless manner which can then be flexibly queried using structured queries expressed in natural language is presented.

Understanding the Information Needs of Large-Scale Digital Library Users

TLDR
A comparative study of user search logs in two large-scale, domain-specific digital libraries functioning in the United States: the National Science Digital Library and Opening History demonstrates varying levels of use of advanced search options and substantial differences in the search query lengths, search query frequencies, and distribution of search categories in queries.

An Overview of Microsoft Academic Service (MAS) and Applications

TLDR
A knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation are demonstrated.

Cypher: An Evolving Query Language for Property Graphs

TLDR
This work describes Cypher 9, which is the first version of the language governed by the openCypher Implementers Group, and introduces the language by example, and provides a formal semantic definition of the core read-query features of Cypher, including its variant of the property graph data model.

A Framework for an Ego-centered and Time-aware Visualization of Relations in Arbitrary Data Repositories

TLDR
A visualization framework which presents the collection as a set of entities and relations (on the data level) using rating functions, which divides large relation networks into small graphs which resemble ego-centered networks.

Construction of the Literature Graph in Semantic Scholar

TLDR
This paper reduces literature graph construction into familiar NLP tasks, point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task.

Supporting academic search tasks through citation visualization and exploration

TLDR
A novel visual library search interface that takes advantage of the rich metadata available in academic collections and employs information visualization techniques to support search results evaluation, forward and backward citation exploration, and interactive query refinement.

DBLP - Some Lessons Learned

TLDR
A review of the evolution of DBLP, where persons play a central role, and discussion of person names may be applicable to many other data bases.

ArnetMiner: extraction and mining of academic social networks

TLDR
The architecture and main features of the ArnetMiner system, which aims at extracting and mining academic social networks, are described and a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues is proposed.