Daniel M. Herzig

570 citations
16 papers · 252 · h-index 9

Impact in

Papers in

    • Semantic Web and Ontologies 12
    • Topic Modeling 3
    • Natural Language Processing Techniques 1
    • Web Data Mining and Analysis 6
    • Information Retrieval and Search Behavior 2
    • Library Science and Information Systems 1

Daniel M. Herzig

14 papers receiving 233 citations

Peers

Daniel M. Herzig
Comparison fields: 5 of 39
  • Computer Science Applications 43
  • Management Science and Operations Research 84
  • Artificial Intelligence 201
  • Information Systems 111
  • Signal Processing 25
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Fedor Borisyuk United States
Michiel Hildebrand Netherlands
Johanna Völker Germany
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Ricardo Usbeck Germany
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Citations per field
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Citations per year

Countries citing papers authored by Daniel M. Herzig

Since Specialization
Citations

This map shows the geographic impact of Daniel M. Herzig's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel M. Herzig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel M. Herzig more than expected).

Fields of papers citing papers by Daniel M. Herzig

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel M. Herzig. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel M. Herzig. The network helps show where Daniel M. Herzig may publish in the future.

Co-authors

The 17 scholars most cited alongside Daniel M. Herzig, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel M. Herzig Line = papers co-authored together Daniel M. Herzig links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201157
2 201939
3
Entity Search Evaluation over Structured Web Data
201131
4
Evaluating Ad-Hoc Object Retrieval
201029
5 201123
6
Use Cases of the Industrial Knowledge Graph at Siemens.
201820
7 201317
8 201215
9 201211
10 20143
11
Alexa, Ask Wikidata! Voice Interaction with Knowledge Graphs using Amazon Alexa.
20172
12
One query to bind them all
20112
13 20182
14
Multilingual Expert Search using Linked Open Data as Interlingual Representation.
20101
15
Planungsdaten schnell finden und einfach nutzen: Linked Open Data und semantische Suche im Einsatz für das KTBL-Datenangebot
20140
16 20130

About Daniel M. Herzig

Daniel M. Herzig is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Signal Processing and Computer Networks and Communications, having authored 16 papers that have together received 252 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (12 papers), Web Data Mining and Analysis (6 papers), Data Quality and Management (5 papers), Data Management and Algorithms (4 papers), Topic Modeling (3 papers), Information Retrieval and Search Behavior (2 papers), Library Science and Information Systems (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Computer Science Applications (43 citations), Management Science and Operations Research (84 citations), Artificial Intelligence (201 citations), Information Systems (111 citations) and Signal Processing (25 citations). Daniel M. Herzig has collaborated with scholars based in Germany, United Kingdom and Spain. Frequent co-authors include Peter Mika, Jeffrey Pound, Harry Halpin, Roi Blanco, Henry S. Thompson, Peter Haase, Thanh Tran, Andriy Nikolov, Thanh Tran and Günter Ladwig. Their work appears in journals such as Journal of Web Semantics, Semantic Web, ACM SIGIR Forum, Repository KITopen (Karlsruhe Institute of Technology) and SSRN Electronic Journal.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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