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ORCIDMichael Färber 0001
Michael Faerber 0001 – Michael Farber 0001
Person information
- affiliation: Technical University (TU) Dresden, Germany
- affiliation (former): KIT, Germany
- affiliation (former): University of Freiburg, Germany
Other persons with the same name
- Michael Färber 0002
![[0000-0003-1634-9525]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-1634-9525")
— Universität Innsbruck, Austria (and 1 more) - Michael Faerber 0003 (aka: Michael Färber 0003) — Intel Mobile Communications GmbH, Munich, Germany
- Michael Farber — disambiguation page
- Michael Farber 0002 — Queen Mary University of London, School of Mathematical Sciences, UK (and 3 more)
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2020 – today
- 2024
[j14]Michael Straub, Johannes Reber, Tarek Saier, Robert Borkowski, Shi Li, Dmitry Khomchenko, André Richter, Michael Färber, Tobias Käfer, René Bonk:
ML approaches for OTDR diagnoses in passive optical networks - event detection and classification: ways for ODN branch assignment. J. Opt. Commun. Netw. 16(7): 43 (2024)- [j13]Michael Färber, Lazaros Tampakis:
Analyzing the impact of companies on AI research based on publications. Scientometrics 129(1): 31-63 (2024) - [j12]Alberto Gómez-Espés, Michael Färber, Adam Jatowt:
Benefits of international collaboration in computer science: a case study of China, the European Union, and the United States. Scientometrics 129(2): 1155-1171 (2024) - [c78]Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid, Hinrich Schütze:
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network. ACL (Findings) 2024: 3987-4001 - [c77]Bolei Ma, Ercong Nie, Shuzhou Yuan, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich Schütze:
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks. EACL (1) 2024: 2685-2702 - [c76]Tarek Saier, Mayumi Ohta, Takuto Asakura, Michael Färber:
HyperPIE: Hyperparameter Information Extraction from Scientific Publications. ECIR (2) 2024: 254-269 - [c75]Michael Färber, Benjamin Zagoruiko, Markus Wambach:
KITspotlight: A System for Spotlighting Researchers in the Media. ICWE 2024: 385-388 - [c74]Shuzhou Yuan, Michael Färber:
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism. NAACL-HLT (Findings) 2024: 920-933 - [c73]Chen Shao, Elias G. Giacoumidis, Shi Li, Jialei Li, Michael Färber, Tobias Käfer, André Richter:
A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON. OFC 2024: 1-3 - [c72]Juwal Regev, Adam Jatowt, Michael Färber:
Future Timelines: Extraction and Visualization of Future-related Content From News Articles. WSDM 2024: 1082-1085 - [i39]Bolei Ma, Ercong Nie, Shuzhou Yuan, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich Schütze:
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks. CoRR abs/2401.16589 (2024) - [i38]Shuzhou Yuan, Ercong Nie, Bolei Ma, Michael Färber:
Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers. CoRR abs/2402.11700 (2024) - [i37]Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid, Hinrich Schütze:
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network. CoRR abs/2402.11709 (2024) - [i36]Ercong Nie, Shuzhou Yuan, Bolei Ma, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich Schütze:
Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models. CoRR abs/2402.18397 (2024) - [i35]Nicholas Popovic, Michael Färber:
Embedded Named Entity Recognition using Probing Classifiers. CoRR abs/2403.11747 (2024) - [i34]Zhan Qu, Daniel Gomm, Michael Färber:
GreeDy and CoDy: Counterfactual Explainers for Dynamic Graphs. CoRR abs/2403.16846 (2024) - [i33]Shuzhou Yuan, Michael Färber:
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism. CoRR abs/2404.06911 (2024) - [i32]Chen Shao, Elias G. Giacoumidis, Shi Li, Jialei Li, Michael Färber, Tobias Käfer, André Richter:
A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON. CoRR abs/2405.00720 (2024) - [i31]Chen Shao, Elias G. Giacoumidis, Patrick Matalla, Jialei Li, Shi Li, Sebastian Randel, André Richter, Michael Färber, Tobias Käfer:
Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network. CoRR abs/2405.02609 (2024) - [i30]Chen Shao, Elias G. Giacoumidis, Syed Moktacim Billah, Shi Li, Jialei Li, Prashasti Sahu, André Richter, Tobias Käfer, Michael Faerber:
Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey. CoRR abs/2405.09557 (2024) - [i29]Raphael Gruber, Abdelrahman Abdallah, Michael Färber, Adam Jatowt:
ComplexTempQA: A Large-Scale Dataset for Complex Temporal Question Answering. CoRR abs/2406.04866 (2024) - [i28]Michael Färber, David Lamprecht, Yuni Susanti:
AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning. CoRR abs/2407.18735 (2024) - [i27]Yuni Susanti, Michael Färber:
Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-based Causal Discovery. CoRR abs/2407.18752 (2024) - 2023
- [j11]Michael Färber, Melissa Coutinho, Shuzhou Yuan:
Biases in scholarly recommender systems: impact, prevalence, and mitigation. Scientometrics 128(5): 2703-2736 (2023) - [c71]David Faragó, Michael Färber, Christian Petrov:
A Full-fledged Commit Message Quality Checker Based on Machine Learning. COMPSAC 2023: 788-799 - [c70]Florian Pickelmann, Michael Färber, Adam Jatowt:
Ablesbarkeitsmesser: A System for Assessing the Readability of German Text. ECIR (3) 2023: 288-293 - [c69]Adam Jatowt, Calvin Gehrer, Michael Färber:
Automatic Hint Generation. ICTIR 2023: 117-123 - [c68]Tarek Saier, Youxiang Dong, Michael Färber:
CoCon: A Data Set on Combined Contextualized Research Artifact Use. JCDL 2023: 47-50 - [c67]Tarek Saier, Johan Krause, Michael Färber:
unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network. JCDL 2023: 66-70 - [c66]Kristian Noullet, Ayoub Ourgani, Michael Färber:
A Full-Fledged Framework for Combining Entity Linking Systems and Components. K-CAP 2023: 148-156 - [c65]Michael Färber, Nicholas Popovic:
Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings. RANLP 2023: 331-335 - [c64]Shuzhou Yuan, Michael Färber:
Evaluating Generative Models for Graph-to-Text Generation. RANLP 2023: 1256-1264 - [c63]Michael Färber, David Lamprecht:
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph. ISWC (Posters/Demos/Industry) 2023 - [c62]Michael Färber, David Lamprecht, Johan Krause, Linn Aung, Peter Haase:
SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples. ISWC 2023: 94-112 - [i26]Daniel Bogdoll, Jonas Hendl, Felix Schreyer, Nishanth Gowda, Michael Färber, J. Marius Zöllner:
Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets. CoRR abs/2301.02200 (2023) - [i25]Michael Färber, Melissa Coutinho, Shuzhou Yuan:
Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation. CoRR abs/2301.07483 (2023) - [i24]Tarek Saier, Johan Krause, Michael Färber:
unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network. CoRR abs/2303.14957 (2023) - [i23]Tarek Saier, Youxiang Dong, Michael Färber:
CoCon: A Data Set on Combined Contextualized Research Artifact Use. CoRR abs/2303.15193 (2023) - [i22]Shuzhou Yuan, Michael Färber:
Evaluating Generative Models for Graph-to-Text Generation. CoRR abs/2307.14712 (2023) - [i21]Michael Färber, Nicholas Popovic:
Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings. CoRR abs/2308.03519 (2023) - [i20]Michael Färber, Jannik Schwade, Adam Jatowt:
Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election. CoRR abs/2308.03531 (2023) - [i19]Michael Färber, David Lamprecht, Johan Krause, Linn Aung, Peter Haase:
SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples. CoRR abs/2308.03671 (2023) - [i18]David Faragó, Michael Färber, Christian Petrov:
A Full-fledged Commit Message Quality Checker Based on Machine Learning. CoRR abs/2309.04797 (2023) - [i17]Michael Färber, Lazaros Tampakis:
Analyzing the Impact of Companies on AI Research Based on Publications. CoRR abs/2310.20444 (2023) - [i16]Michael Färber, David Lamprecht:
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph. CoRR abs/2310.20475 (2023) - [i15]Tarek Saier, Mayumi Ohta, Takuto Asakura, Michael Färber:
HyperPIE: Hyperparameter Information Extraction from Scientific Publications. CoRR abs/2312.10638 (2023) - 2022
- [j10]Tarek Saier, Michael Färber, Tornike Tsereteli:
Cross-lingual citations in English papers: a large-scale analysis of prevalence, usage, and impact. Int. J. Digit. Libr. 23(2): 179-195 (2022) - [j9]Michael Färber, Lin Ao:
The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings. Quant. Sci. Stud. 3(1): 51-98 (2022) - [c61]Michael Färber, Nicolas Weber:
When to Use Which Neural Network? Finding the Right Neural Network Architecture for a Research Problem. SDU@AAAI 2022 - [c60]Leopold Müller, Lars Böcking, Michael Färber:
Safety Aware Reinforcement Learning by Identifying Comprehensible Constraints in Expert Demonstrations. SafeAI@AAAI 2022 - [c59]Igor Shapiro, Tarek Saier, Michael Färber:
Sequence Labeling for Citation Field Extractionfrom Cyrillic Script References. SDU@AAAI 2022 - [c58]Igor Shapiro, Tarek Saier, Michael Färber:
Sequence Labeling for Citation Field Extractionfrom Cyrillic Script References. SDU@AAAI 2022 - [c57]Michael Färber, Christoph Braun, Nicholas Popovic, Tarek Saier, Kristian Noullet:
Which Publications' Metadata Are in Which Bibliographic Databases? A System for Exploration. BIR@ECIR 2022: 39-44 - [c56]Anna Nguyen, Daniel Hagenmayer, Tobias Weller, Michael Färber:
Explaining Convolutional Neural Networks by Tagging Filters. CIKM Workshops 2022 - [c55]Tarek Saier, Meng Luan, Michael Färber:
A Blocking-Based Approach to Enhance Large-Scale Reference Linking. ULITE@JCDL 2022: 16-25 - [c54]Chifumi Nishioka, Michael Färber, Tarek Saier:
How does author affiliation affect preprint citation count?: analyzing citation bias at the institution and country level. JCDL 2022: 28:1-28:8 - [c53]Nicholas Popovic, Michael Färber:
Few-Shot Document-Level Relation Extraction. NAACL-HLT 2022: 5733-5746 - [c52]Nicholas Popovic, Walter Laurito, Michael Färber:
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities. SemEval@NAACL 2022: 1687-1694 - [c51]Michael Färber, David Lamprecht:
The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models. ISWC (Posters/Demos/Industry) 2022 - [i14]Nicholas Popovic, Walter Laurito, Michael Färber:
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities. CoRR abs/2203.05325 (2022) - [i13]Chifumi Nishioka, Michael Färber, Tarek Saier:
How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level. CoRR abs/2205.02033 (2022) - [i12]Nicholas Popovic, Michael Färber:
Few-Shot Document-Level Relation Extraction. CoRR abs/2205.02048 (2022) - [i11]Tanja Aue, Adam Jatowt, Michael Färber:
Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis. CoRR abs/2212.11765 (2022) - 2021
- [j8]Jiexin Wang, Adam Jatowt, Michael Färber, Masatoshi Yoshikawa:
Improving question answering for event-focused questions in temporal collections of news articles. Inf. Retr. J. 24(1): 29-54 (2021) - [j7]Michael Färber, David Lamprecht:
The data set knowledge graph: Creating a linked open data source for data sets. Quant. Sci. Stud. 2(4): 1324-1355 (2021) - [c50]Michael Färber, Alexander Albers, Felix Schüber:
Identifying Used Methods and Datasets in Scientific Publications. SDU@AAAI 2021 - [c49]Michael Färber, Frederic Bartscherer:
Media Bias Everywhere? A Vision for Dealing with the Manipulation of Public Opinion. BIAS 2021: 9-13 - [c48]Michael Färber, Ann-Kathrin Leisinger:
Recommending Datasets for Scientific Problem Descriptions. CIKM 2021: 3014-3018 - [c47]Adam Jatowt, I-Chen Hung, Michael Färber, Ricardo Campos, Masatoshi Yoshikawa:
Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation. ECIR (1) 2021: 254-269 - [c46]Anna Nguyen, Adrian Oberföll, Michael Färber:
Right for the Right Reasons: Making Image Classification Intuitively Explainable. ECIR (2) 2021: 327-333 - [c45]Kristian Noullet, Samuel Printz, Michael Färber:
CLiT: Combining Linking Techniques for Everyone. ESWC (Satellite Events) 2021: 88-92 - [c44]Anna Nguyen, Franz Krause, Daniel Hagenmayer, Michael Färber:
Quantifying Explanations of Neural Networks in E-Commerce Based on LRP. ECML/PKDD (5) 2021: 251-267 - [c43]Michael Färber, Ann-Kathrin Leisinger:
DataHunter: A System for Finding Datasets Based on Scientific Problem Descriptions. RecSys 2021: 749-752 - [c42]Michael Färber, Vinzenz Zinecker, Isabela Bragaglia Cartus, Sebastian Celis, Maria Duma:
C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations. WWW (Companion Volume) 2021: 441-445 - [i10]Anna Nguyen, Daniel Hagenmayer, Tobias Weller, Michael Färber:
Explaining Convolutional Neural Networks by Tagging Filters. CoRR abs/2109.09389 (2021) - [i9]Tarek Saier, Michael Färber, Tornike Tsereteli:
Cross-Lingual Citations in English Papers: A Large-Scale Analysis of Prevalence, Usage, and Impact. CoRR abs/2111.05097 (2021) - [i8]Michael Färber, Anna Steyer:
Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text. CoRR abs/2112.00160 (2021) - [i7]Michael Färber, Alexander Klein:
Are Investors Biased Against Women? Analyzing How Gender Affects Startup Funding in Europe. CoRR abs/2112.00859 (2021) - 2020
- [j6]Michael Färber, Adam Jatowt:
Citation recommendation: approaches and datasets. Int. J. Digit. Libr. 21(4): 375-405 (2020) - [j5]Tarek Saier, Michael Färber:
unarXive: a large scholarly data set with publications' full-text, annotated in-text citations, and links to metadata. Scientometrics 125(3): 3085-3108 (2020) - [c41]Michael Färber, Timo Klein, Joan Sigloch:
Neural Citation Recommendation: A Reproducibility Study. BIR@ECIR 2020: 66-74 - [c40]Michael Färber, Victoria Burkard, Adam Jatowt, Sora Lim:
A Multidimensional Dataset Based on Crowdsourcing for Analyzing and Detecting News Bias. CIKM 2020: 3007-3014 - [c39]Tarek Saier, Michael Färber:
Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. ECIR (1) 2020: 220-233 - [c38]Jiexin Wang, Adam Jatowt, Michael Färber, Masatoshi Yoshikawa:
Answering Event-Related Questions over Long-Term News Article Archives. ECIR (1) 2020: 774-789 - [c37]Tarek Saier, Michael Färber:
A Large-Scale Analysis of Cross-lingual Citations in English Papers. ICADL 2020: 122-138 - [c36]Michael Färber, Benjamin Scheer, Frederic Bartscherer:
Who's Behind That Website? Classifying Websites by the Degree of Commercial Intent. ICWE 2020: 130-145 - [c35]Michael Färber, Ashwath Sampath:
HybridCite: A Hybrid Model for Context-Aware Citation Recommendation. JCDL 2020: 117-126 - [c34]Michael Färber:
Analyzing the GitHub Repositories of Research Papers. JCDL 2020: 491-492 - [c33]Chifumi Nishioka, Michael Färber:
Trends of Publications' Citations and Altmetrics Based on Open Access Types. JCDL 2020: 503-504 - [c32]Anna Nguyen, Tobias Weller, Michael Färber, York Sure-Vetter:
Making Neural Networks FAIR. KGSWC 2020: 29-44 - [c31]Sora Lim, Adam Jatowt, Michael Färber, Masatoshi Yoshikawa:
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing. LREC 2020: 1478-1484 - [c30]Kristian Noullet, Rico Mix, Michael Färber:
KORE 50DYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase. LREC 2020: 2389-2395 - [c29]Boulos El Asmar, Syrine Chelly, Nour Azzi, Lynn Nassif, Jana El Asmar, Michael Färber:
AWARE: A Situational Awareness Framework for Facilitating Adaptive Behavior of Autonomous Vehicles in Manufacturing. ISWC (2) 2020: 651-666 - [i6]Michael Färber, Ashwath Sampath:
HybridCite: A Hybrid Model for Context-Aware Citation Recommendation. CoRR abs/2002.06406 (2020) - [i5]Michael Färber, Adam Jatowt:
Citation Recommendation: Approaches and Datasets. CoRR abs/2002.06961 (2020) - [i4]Anna Nguyen, Adrian Oberföll, Michael Färber:
Right for the Right Reason: Making Image Classification Robust. CoRR abs/2007.11924 (2020)
2010 – 2019
- 2019
- [c28]Michael Färber, Agon Qurdina, Lule Ahmedi:
Identifying Twitter Bots Using a Convolutional Neural Network. CLEF (Working Notes) 2019 - [c27]Tarek Saier, Michael Färber:
Bibliometric-Enhanced arXiv: A Data Set for Paper-Based and Citation-Based Tasks. BIR@ECIR 2019: 14-26 - [c26]Michael Färber, Adam Jatowt:
Finding Temporal Trends of Scientific Concepts. BIR@ECIR 2019: 132-139 - [c25]Michael Färber, Ashwath Sampath, Adam Jatowt:
PaperHunter: A System for Exploring Papers and Citation Contexts. ECIR (2) 2019: 246-250 - [c24]Michael Färber, Ashwath Sampath:
Determining How Citations Are Used in Citation Contexts. TPDL 2019: 380-383 - [c23]Michael Färber, Chifumi Nishioka, Adam Jatowt:
ScholarSight: Visualizing Temporal Trends of Scientific Concepts. JCDL 2019: 438-439 - [c22]Michael Färber, Agon Qurdina, Lule Ahmedi:
Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks. SemEval@NAACL-HLT 2019: 1032-1036 - [c21]Michael Färber:
The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data. ISWC (2) 2019: 113-129 - [c20]Chifumi Nishioka, Michael Färber:
Evaluating the Availability of Open Citation Data. BIRNDL@SIGIR 2019: 123-129 - [c19]Victor Anthony Arrascue Ayala, Polina Koleva, Anas Alzogbi, Matteo Cossu, Michael Färber, Patrick Philipp, Guilherme Schievelbein, Io Taxidou, Georg Lausen:
Relational schemata for distributed SPARQL query processing. SBD@SIGMOD 2019: 3:1-3:6 - [i3]Michael Färber:
Linked Crunchbase: A Linked Data API and RDF Data Set About Innovative Companies. CoRR abs/1907.08671 (2019) - 2018
- [j4]Michael Färber, Frederic Bartscherer, Carsten Menne, Achim Rettinger:
Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semantic Web 9(1): 77-129 (2018) - [j3]Michael Färber, Carsten Menne, Andreas Harth:
A Linked Data wrapper for CrunchBase. Semantic Web 9(4): 505-515 (2018) - [c18]Michael Färber, Alexander Thiemann, Adam Jatowt:
To Cite, or Not to Cite? Detecting Citation Contexts in Text. ECIR 2018: 598-603 - [c17]Michael Färber, Alexander Thiemann, Adam Jatowt:
CITEWERTs: A System Combining Cite-Worthiness with Citation Recommendation. ECIR 2018: 815-819 - [c16]Matteo Cossu, Michael Färber, Georg Lausen:
PRoST: Distributed Execution of SPARQL Queries Using Mixed Partitioning Strategies. EDBT 2018: 469-472 - [c15]I-Chen Hung, Michael Färber, Adam Jatowt:
Towards Recommending Interesting Content in News Archives. ICADL 2018: 142-146 - [c14]Michael Färber, Alexander Thiemann, Adam Jatowt:
A High-Quality Gold Standard for Citation-based Tasks. LREC 2018 - [i2]Matteo Cossu, Michael Färber, Georg Lausen:
PRoST: Distributed Execution of SPARQL Queries Using Mixed Partitioning Strategies. CoRR abs/1802.05898 (2018) - [i1]Michael Färber, Achim Rettinger:
Which Knowledge Graph Is Best for Me? CoRR abs/1809.11099 (2018) - 2017
- [b1]Michael Färber:
Semantic search for novel information. Karlsruhe Institute of Technology, Germany, Studies on the Semantic Web 31, IOS Press 2017, ISBN 978-3-89838-727-9, pp. 1-193 - [j2]Lei Zhang, Andreas Thalhammer, Achim Rettinger, Michael Färber, Aditya Mogadala, Ronald Denaux:
The xLiMe system: Cross-lingual and cross-modal semantic annotation, search and recommendation over live-TV, news and social media streams. J. Web Semant. 46-47: 20-30 (2017) - [c13]Victor Anthony Arrascue Ayala, Kemal Cagin Gülsen, Anas Alzogbi, Michael Färber, Marco Muñiz, Georg Lausen:
A Delay-Robust Touristic Plan Recommendation Using Real-World Public Transportation Information. RecTour@RecSys 2017: 9-17 - [c12]Lei Zhang, Maribel Acosta, Michael Färber, Steffen Thoma, Achim Rettinger:
BreXearch: Exploring Brexit Data Using Cross-Lingual and Cross-Media Semantic Search. ISWC (Posters, Demos & Industry Tracks) 2017 - 2016
- [j1]Michael Färber:
Using a semantic wiki for technology forecast and technology monitoring. Program 50(2): 225-242 (2016) - [c11]