Overview
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Publications
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(2022): Intelligenza artifiziala per la lingua rumantscha. In: Wissensplatz, S. 8-9. Available online at https://www.fhgr.ch/fhgr/medien-und-oeffentlichkeit/publikationen/wissensplatz/februar-2022/, last checked on 10.02.2022
Abstract: Nizzegiar la digitalisaziun per rinforzar la lingua rumantscha sin plaun naziunal, quai è la motivaziun da «Translatur-ia». Cun quest project da perscrutaziun mussa la Scola auta spezialisada dal Grischun ils potenzials e las sfidas dal svilup d’applicaziuns linguisticas.
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(2022): Slot Filling for Extracting Reskilling and Upskilling Options from the Web. 27th International Conference on Natural Language & Information Systems (NLDB). Universitat Politècnica de València. Valencia,17. Juni, 2022. Available online at https://www.youtube.com/watch?v=rIhhKjJAMnY&t=2608s, last checked on 24.11.2022
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(2022): Building Knowledge Graphs and Recommender Systems for Suggesting Reskilling and Upskilling Options from the Web. In: Information 13. Available online at https://doi.org/10.3390/info13110510, last checked on 24.11.2022
Abstract: As advances in science and technology, crisis, and increased competition impact labor markets, reskilling and upskilling programs emerged to mitigate their effects. Since information on continuing education is highly distributed across websites, choosing career paths and suitable upskilling options is currently considered a challenging and cumbersome task. This article, therefore, introduces a method for building a comprehensive knowledge graph from the education providers’ Web pages. We collect educational programs from 488 providers and leverage entity recognition and entity linking methods in conjunction with contextualization to extract knowledge on entities such as prerequisites, skills, learning objectives, and course content. Slot filling then integrates these entities into an extensive knowledge graph that contains close to 74,000 nodes and over 734,000 edges. A recommender system leverages the created graph, and background knowledge on occupations to provide a career path and upskilling suggestions. Finally, we evaluate the knowledge extraction approach on the CareerCoach 2022 gold standard and draw upon domain experts for judging the career paths and upskilling suggestions provided by the recommender system.
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(2022) : Career Coach. Automatische Wissensextraktion und Expertensystem für personalisierte Re- und Upskilling Vorschläge In: Forster, Michael; Alt, Sharon; Hanselmann, Marcel; Deflorin, Patricia (Hg.): Digitale Transformation an der Fachhochschule Graubünden: Case Studies aus Forschung und Lehre: Chur: FH Graubünden Verlag, S. 11-18
Abstract: CareerCoach entwickelt Methoden zur automatischen Extraktion von Fortbildungsangeboten. Das System analysiert die Webseiten von Bildungsanbietenden und integriert deren Angebote in einen zentralen Wissensgrafen, der innovative Dienstleistungen wie semantische Suchen und Expertensysteme unterstützt.
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(2021): Translaziun Rumantscha cun intelligenza artifiziala (Einblicke in die Forschung). Available online at https://www.fhgr.ch/fileadmin/publikationen/forschungsbericht/fhgr-Einblicke_in_die_Forschung_2021.pdf, last checked on 28.05.2021
Abstract: Translatur-ia beabsichtigt Anwendungen, welche im Zusammenhang mit der rätoromanischen Sprache stehen (z. B. Übersetzungsdienstleistungen), durch die Entwicklung und Einbindung von Computertechnologie zu unterstützen. Wir demonstrieren, dass die Schaffung solcher Technologien durchaus realistisch ist.
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(2021): Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities. In: Future Internet 13. Available online at doi.org/10.3390/fi13030059, last checked on 18.05.2021
Abstract: Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.
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(2020): Optimierung von Unternehmensbewertungen durch automatisierte Wissensidentifikation, -extraktion und -integration. In: Information. Wissenschaft & Praxis 71, S. 321-325. Available online at https://doi.org/10.1515/iwp-2020-2119, last checked on 30.10.2020
Abstract: Unternehmensbewertungen in der Biotech-Branche, Pharmazie und Medizintechnik stellen eine anspruchsvolle Aufgabe dar, insbesondere bei Berücksichtigung der einzigartigen Risiken, denen Biotech-Startups beim Eintritt in neue Märkte ausgesetzt sind. Unternehmen, die auf globale Bewertungsdienstleistungen spezialisiert sind, kombinieren daher Bewertungsmodelle und Erfahrungen aus der Vergangenheit mit heterogenen Metriken und Indikatoren, die Einblicke in die Leistung eines Unternehmens geben. Dieser Beitrag veranschaulicht, wie automatisierte Wissensidentifikation, -extraktion und -integration genutzt werden können, um (i) zusätzliche Indikatoren zu ermitteln, die Einblicke in den Erfolg eines Unternehmens in der Produktentwicklung geben und um (ii) arbeitsintensive Datensammelprozesse zur Unternehmensbewertung zu unterstützen.
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(2020): Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion. English translation of the article: "Optimierung von Unternehmensbewertungen durch automatisierte Wissensidentifikation, -extraktion und -integration". Information - Wissenschaft und Praxis 71 (5-6):321-325. Available online at https://arxiv.org/abs/2010.09249, last checked on 18.05.2021
Abstract: Performing company valuations within the domain of biotechnology, pharmacy and medical technology is a challenging task, especially when considering the unique set of risks biotech start-ups face when entering new markets. Companies specialized in global valuation services, therefore, combine valuation models and past experience with heterogeneous metrics and indicators that provide insights into a company's performance. This paper illustrates how automated knowledge discovery, extraction and data fusion can be used to (i) obtain additional indicators that provide insights into the success of a company's product development efforts, and (ii) support labor-intensive data curation processes. We apply deep web knowledge acquisition methods to identify and harvest data on clinical trials that is hidden behind proprietary search interfaces and integrate the extracted data into the industry partner's company valuation ontology. In addition, focused Web crawls and shallow semantic parsing yield information on the company's key personnel and respective contact data, notifying domain experts of relevant changes that get then incorporated into the industry partner's company data.
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(2019) : Introducing orbis. An extendable evaluation pipeline for named entity linking performance drill‐down analyses In: Blake, Catherine; Brown, Cecelia (Hg.): 82nd Annual Meeting of The Association for Information Science: Proceedings, 56: ASIS&T 2019: Melbourne, Australia, 19.-23. Oktober: Somerset, NJ, USA: John Wiley & Sons, Ltd, S. 468-471. Available online at doi.org/10.1002/pra2.49, last checked on 21.05.2021
Abstract: Most current evaluation tools are focused solely on benchmarking and comparative evaluations thus only provide aggregated statistics such as precision, recall and F1-measure to assess overall system performance. They do not offer comprehensive analyses up to the level of individual annotations. This paper introduces Orbis, an extendable evaluation pipeline framework developed to allow visual drill-down analyses of individual entities, computed by annotation services, in the context of the text they appear in, in reference to the entities specified in the gold standard.
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(2019) : Name Variants for Improving Entity Discovery and Linking In: Eskevich, Maria; Melo, Gerard de; Fäth, Christian; McCrae, John P.; Buitelaar, Paul; Chiarcos, Christian; Klimek, Bettina; Dojchinovski, Milan (Hg.): 2nd Conference onLanguage, Data and Knowledge: LDK 2019: Leipzig, 20.-23. Mai: Saarbrücken/Wadern: Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing (OASIcs), S. 14:1-14:15. Available online at https://doi.org/10.4230/OASIcs.LDK.2019.14, last checked on 21.05.2021
Abstract: Identifying all names that refer to a particular set of named entities is a challenging task, as quite often we need to consider many features that include a lot of variation like abbreviations, aliases, hypocorism, multilingualism or partial matches. Each entity type can also have specific rules for name variances: people names can include titles, country and branch names are sometimes removed from organization names, while locations are often plagued by the issue of nested entities. The lack of a clear strategy for collecting, processing and computing name variants significantly lowers the recall of tasks such as Named Entity Linking and Knowledge Base Population since name variances are frequently used in all kind of textual content. This paper proposes several strategies to address these issues. Recall can be improved by combining knowledge repositories and by computing additional variances based on algorithmic approaches. Heuristics and machine learning methods then analyze the generated name variances and mark ambiguous names to increase precision. An extensive evaluation demonstrates the effects of integrating these methods into a new Named Entity Linking framework and confirms that systematically considering name variances yields significant performance improvements.
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(2019) : Improving Named Entity Linking Corpora Quality In: Angelova, Galia; Mitkov, Ruslan; Nikolova, Ivelina; Temnikova, Irina (Hg.): Natural Language Processing in a Deep Learning World: Proceedings: International Conference Recent Advances in Natural Language Processing (RANLP 2019): Varna, Bulgaria, 2.-4. September: Bulgaria: Ltd., Shoumen, S. 1328-1337. Available online at https://doi.org/10.26615/978-954-452-056-4_152, last checked on 21.05.2021
Abstract: Gold standard corpora and competitive evaluations play a key role in benchmarking named entity linking (NEL) performance and driving the development of more sophisticated NEL systems. The quality of the used corpora and the used evaluation metrics are crucial in this process. We, therefore, assess the quality of three popular evaluation corpora, identifying four major issues which affect these gold standards: (i) the use of different annotation styles, (ii) incorrect and missing annotations, (iii) Knowledge Base evolution, (iv) and differences in annotating co-occurrences. This paper addresses these issues by formalizing NEL annotations and corpus versioning which allows standardizing corpus creation, supports corpus evolution, and paves the way for the use of lenses to automatically transform between different corpus configurations. In addition, the use of clearly defined scoring rules and evaluation metrics ensures a better comparability of evaluation results.
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(2018) : Framing Named Entity Linking Error Types In: Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Hasida, Koiti; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Moreno, Asuncion; Odijk, Jan; Piperidis, Stelios; Tokunaga, Takenobu (Hg.): Eleventh International Conference on Language Resources and Evaluation: Conference Proceedings. In collaboration with Sara Goggi and Hélène Mazo: LREC '18: Miyazaki, Japan, 7.-12. Mai: Paris: European Language Resources Association (ELRA), S. 266-271. Available online at https://www.aclweb.org/anthology/L18-1040/, last checked on 21.05.2021
Abstract: Named Entity Linking (NEL) and relation extraction forms the backbone of Knowledge Base Population tasks. The recent rise of large open source Knowledge Bases and the continuous focus on improving NEL performance has led to the creation of automated benchmark solutions during the last decade. The benchmarking of NEL systems offers a valuable approach to understand a NEL system’s performance quantitatively. However, an in-depth qualitative analysis that helps improving NEL methods by identifying error causes usually requires a more thorough error analysis. This paper proposes a taxonomy to frame common errors and applies this taxonomy in a survey study to assess the performance of four well-known Named Entity Linking systems on three recent gold standards.
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(2018): On the Importance of Drill-Down Analysis for Assessing Gold Standards and Named Entity Linking Performance. SEMANTiCS 2018: 14th International Conference on Semantic Systems. In: Procedia Computer Science 137, S. 33-42. Available online at https://doi.org/10.1016/j.procs.2018.09.004, last checked on 21.05.2021
Abstract: Rigorous evaluations and analyses of evaluation results are key towards improving Named Entity Linking systems. Nevertheless, most current evaluation tools are focused on benchmarking and comparative evaluations. Therefore, they only provide aggregated statistics such as precision, recall and F1-measure to assess system performance and no means for conducting detailed analyses up to the level of individual annotations. This paper addresses the need for transparent benchmarking and fine-grained error analysis by introducing Orbis, an extensible framework that supports drill-down analysis, multiple annotation tasks and resource versioning. Orbis complements approaches like those deployed through the GERBIL and TAC KBP tools and helps developers to better understand and address shortcomings in their Named Entity Linking tools. We present three uses cases in order to demonstrate the usefulness of Orbis for both research and production systems: (i) improving Named Entity Linking tools; (ii) detecting gold standard errors; and (iii) performing Named Entity Linking evaluations with multiple versions of the included resources.
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(2018) : Mining and Leveraging Background Knowledge for Improving Named Entity Linking In: Akerkar, Rajendra; Ivanović, Mirjana; Kim, Sang-Wook; Manolopoulos, Yannis; Rosati, Riccardo; Savić, Miloš; Badica, Costin; Radovanović, Miloš (Hg.): Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, Article No.: 27: WIMS '18: Novi Sad, Serbia, 25.-27. Juni: New York, NY, USA: Association for Computing Machinery (ACM). Available online at doi.org/10.1145/3227609.3227670, last checked on 21.05.2021
Abstract: Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.
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(2018): Optimizing Information Acquisition and Decision Making Processes with Natural Language Processing, Machine Learning and Visual Analytics. 3rd SwissText Analytics Conference. Winterthur, 12.-13. Juni, 2018. Available online at https://youtu.be/YicWN1rEn7M, last checked on 28.05.2021
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(2017) : Mitigating linked data quality issues in knowledge-intense information extraction methods In: Akerkar, Rajendra; Cuzzocrea, Alfredo; Cao, Jannong; Hacid, Mohand-Said (Hg.): Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, Article No.: 17: WIMS '17: Amantea, Italy, 19.-22. Juni: New York, NY, USA: Association for Computing Machinery (ACM). Available online at https://doi.org/10.1145/3102254.3102272, last checked on 21.05.2021
Abstract: Advances in research areas such as named entity linking and sentiment analysis have triggered the emergence of knowledge-intensive information extraction methods that combine classical information extraction with background knowledge from the Web. Despite data quality concerns, linked data sources such as DBpedia, GeoNames and Wikidata which encode facts in a standardized structured format are particularly attractive for such applications. This paper addresses the problem of data quality by introducing a framework that elaborates on linked data quality issues relevant to different stages of the background knowledge acquisition process, their impact on information extraction performance and applicable mitigation strategies. Applying this framework to named entity linking and data enrichment demonstrates the potential of the introduced mitigation strategies to lessen the impact of different kinds of data quality problems. An industrial use case that aims at the automatic generation of image metadata from image descriptions illustrates the successful deployment of knowledge-intensive information extraction in real-world applications and constraints introduced by data quality concerns.
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(2015): Graph-Based Disambiguation for Named Entity Linking. Masterarbeit Information and Data Management. Hochschule für Technik und Wirtschaft HTW Chur, Chur. Schweizerisches Institut für Informationswissenschaft (SII).
Abstract: Named Entity Recognition (NER) beschreibt den Prozess des Erkennens und Einordnens von in einem Text enthaltenen Entitäten. Im Gegensatz dazu ist unter Named Enti-ty Disambiguation (NED) oder -Linkung (NEL) ein Prozess zu verstehen, welcher die Verknüpfung dieser erkannten Entitäten mit der jeweils richtigen Ressource eines zugrundeliegenden Daten-Repositories zur Aufgabe hat. Durch die Notwendigkeit der bei-den Prozesse als Bestandteil des Tasks Information Extraction (IE) erhielten diese in den letzten Jahrzehnten sehr viel Aufmerksamkeit. Dies ist vor allem durch den Bedarf von Unternehmen begründet, aktuelle Einsichten in den Status ihrer Marken, Produkte, Stakeholder und Märkte zu erhalten. Das Forschungsprojekt versucht einen neuen Graphen-basierten Ansatz des NED-Tasks in die bereits existierende Software-Komponente Recognyze zu implementieren. Die Beziehung zwischen potentiellen Entitäten in einem Linked-Data Repository wird hierbei als Grundlage der Wahrscheinlichkeitsberechnung der Tatsächlichkeit einer potentiellen Entität im unbekannten und unstrukturierten Text gebraucht. Verglichen mit der bereits zu Beginn bestehenden Implementierung des NED-Tasks in Recognyze konnte durch die Verwendung dieses neuen Ansatzes die Qualität der Ergebnisse um 35% gesteigert werden. Gleichzeitig wurden Abhängigkeiten zu Sprache oder grammatikalischer Korrektheit entfernt, so dass die Lösung global einsetzbar ist. Dies zeigt, dass es durchaus möglich und zielführend ist, einen Graphen-basierten Ansatz im Kontext dieser Software-Komponente zu verwenden.
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(2015): Mensch versus Maschine. Ein Vergleich von manueller und computerunterstützter Inhaltsanalyse am Beispiel der nationalen Medienkritik. SGKM-Jahrestagung. Schweizerische Gesellschaft für Kommunikations- und Medienwissenschaft. Bern, 13. März, 2015
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(2015): Gemeckere auf Knopfdruck. Komplexitätsreduktion durch computerunterstützte Inhaltsanalyse nationaler Medienkritik. Gemeinsame Jahrestagung der Fachgruppen Computervermittelte Kommunikation und Soziologie der Medienkommunikation. Deutsche Gesellschaft für Publizistik- und Kommunikationswissenschaft. Berlin, 7. November, 2015