@article{Berven2020,

title = {A knowledge-graph platform for newsrooms},

author = {Arne Berven and Ole A. Christensen and Sindre Moldeklev and Andreas L. Opdahl and Kjetil A. Villanger },

url = {https://reader.elsevier.com/reader/sd/pii/S0166361520305558?token=F8A21A513C97BFF598C2755575B3C89174B3D404E2EDDD23EC37966A2754ACA1700011EBBCF52ADE2845ADBC12D40041},

doi = {https://doi.org/10.1016/j.compind.2020.103321},

year  = {2020},

date = {2020-12-01},

urldate = {2020-12-01},

journal = {Computers in Industry},

volume = {123},

number = {103321},

abstract = {Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the international market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowledge graphs, natural-language processing (NLP), and machine learning (ML) together to support journalists. Our focus is on combining existing data sources and computation and storage techniques into a flexible architecture for news journalism. The paper presents News Hunter along with plans and possibilities for future work.},

note = {Pre SFI},

keywords = {Computational journalism, Journalistic knowledge platforms, Knowledge graphs, Machine learning (ML), Natural-language processing (NLP), Newsroom systems, Ontology, OWL, RDF, Semantic technologies, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Al-Moslmi2020,

title = {Named entity extraction for knowledge graphs: A literature overview},

author = {Tareq Al-Moslmi and Marc Gallofré Ocaña and Andreas L. Opdahl and Csaba Veres

},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8999622},

doi = {10.1109/ACCESS.2020.2973928},

year  = {2020},

date = {2020-02-14},

urldate = {2020-02-14},

journal = {IEEE Access},

volume = {8},

pages = {32862-32881},

abstract = {An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.},

note = {Pre SFI},

keywords = {Knowledge graphs, Named-entity disambiguation, Named-entity extraction, Named-entity linking, Named-entity recognition, Natural-language processing, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}