Text and Data Mining (TDM)

Growing volumes of data and complex information requirements of users, for example within the scope of search processes, pose major challenges to scientists and information professionals. Therefore, new ideas and methods, for example, of Text and Data Mining (TDM), are needed that can be applied to analyze and retrieve knowledge within the scope of specific issues in connection with searching and analyzing patents. We therefore research into specific methods to retrieve and analyze large volumes of data (Big Data Analytics) and the semantic indexing of patent full texts, e.g., with the help of knowledge from ontologies and Linked Open Data (LOD).

Patent Mining and Semantic Enrichment

For the efficient analysis of patent documents, their unstructured full text sections must be further structured and semantically enriched in order to obtain high-quality results.  This is one of our focuses of activities. Another is on semantic indexing of the claims and the detailed description of a patent text by means of machine learning approaches. Such approaches not only serve to formulate more precise search queries but also to improve and develop extended methods to extract domain-specific entities and terminologies automatically.

Furthermore, the automatic semantic indexing of text documents also requires a semantic representation of essential parts of the document content, e.g., according to RDF/OWL standards. This regards both the representation of metadata and the display of relevant, automatically extracted entities and relations of a domain. The semantic indexing of information also enables interoperability with existing external sources, e.g., from the LOD cloud, or the integration and exploitation of specialized knowledge bases (e.g. bio-chemical information from ChEMBL) and semantic services in complex analysis workflows.

Semantic Search and Knowledge Graphs

In addition to established methods of information retrieval in the context of patent searches using Boolean logic, enhanced and complex search capabilities need to be developed using an entity or knowledge-based approach. Our aim is to investigate and employ different forms of semantic search using knowledge graphs. In addition to the use of domain-specific ontologies, relevant entities and their relations can be increasingly extracted and linked automatically by means of entity recognition and disambiguation applying, e.g., Deep Learning (DL) or other machine learning techniques.

Big Data Analytics

Rapidly and dynamically growing volumes of scientific and technical information require innovative and scalable methods for querying, analyzing and visualizing relevant information efficiently.  For this reason, we research and develop specific solutions for the analysis of large domain-specific patent collections in order to be able to answer complex questions, e.g., for the life-science domain. Together with new methods of the TDM, our goal is to identify complex knowledge from large amounts of data (e.g., technology trends).

2020

• Türker, Rima; Zhang, Lei; Alam, Mehwish; Sack, Harald Weakly Supervised Short Text Categorization Using World Knowledge In: The Semantic Web – ISWC 2020. 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part I / Pan, Jeff Z.; Tamma, Valentina; d’Amato, Claudia et al. (Hrsg.); Cham: Springer, 2020; 730 S.; ISBN 978-3-030-62418-7; Lecture Notes in Computer Science 12506, S. 584-600. DOI: 10.1007/978-3-030-62419-4_33
• Saad F., Aras H., Hackl-Sommer R. (2020) Improving Named Entity Recognition for Biomedical and Patent Data Using Bi-LSTM Deep Neural Network Models. In: Métais E., Meziane F., Horacek H., Cimiano P. (eds) Natural Language Processing and Information Systems. NLDB 2020. Lecture Notes in Computer Science, vol 12089. Springer, Cham. https://doi.org/10.1007/978-3-030-51310-8_3

2019

• Andersson, Linda [Hrsg.]; Aras, Hidir [Hrsg.];Piroi, Florina [Hrsg.];Hanbury, Allan [Hrsg.] Proceedings of the 1st Workshop on on Patent Text Mining and Semantic Technologies: PatentSemTech2019, Karlsruhe, Germany 2019. https://doi.org/10.34726/pst2019.
• Saad, Farag; Hackl-Sommer, René. Building a Semantic Model for Linking and Visualizing Patent Citations (SeMViPaC). In: iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services. Munich, Germany, December 2019 / Indrawan-Santiago, Maria; Pardede, Eric; Salvadori, Ivan Luiz et al. (Hrsg.); acm Association for Computing Machinery, 2020; 709 S. ISBN 978-1-4503-7179-7; S. 645–648. DOI: 10.1145/3366030.3366110
• Saad, Farag. Named Entity Recognition for Biomedical Patent Text using Bi-LSTM Variants. In: iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services. Munich, Germany, December 2019 / Indrawan-Santiago, Maria; Pardede, Eric; Salvadori, Ivan Luiz et al. (Hrsg.); acm Association for Computing Machinery, 2020; 709 S. ISBN 978-1-4503-7179-7; S. 617–621, DOI: 10.1145/3366030.3366104
• Sofean, Mustafa; Aras, Hidir; Alrifai, Ahmad. Analyzing Trending Technological Areas of Patents. In: Database and Expert Systems Applications. DEXA 2019 International Workshops BIOKDD, IWCFS, MLKgraphs and TIR, Linz, Austria, August 26–29, 2019, Proceedings / Anderst-Kotsis, Gabriele; Toja, A Min; Khlil, Ismail; Elloumi, Mourad et al. (Hrsg.); Cham: Springer, 2019; 237 S.; ISBN978-3-030-27683-6; Communications in Computer and Information Science, vol 1062, S. 141-146. DOI: 10.1007/978-3-030-27684-3_18
• Zhang, Lei; Wu, Tianxing; Xu, Liang; Wang, Meng; Qi, Guilin; Sack, Harald. Emerging Entity Discovery Using Web Sources. In: Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. 4th China Conference, CCKS 2019, Hangzhou, China, August 24–27, 2019, Revised Selected Papers / Zhu X., Qin B., Zhu X., Liu M., Qian L. (Hrsg.);  Singapur: Springer 2019; 225 S.; ISBN 978-981-15-1955-0; Communications in Computer and Information Science Vol 1134; S. 175-184. DOI: https://doi.org/10.1007/978-981-15-1956-7_16
• Aras, Hidir. Analyse von Patentdaten mittels Workflows und Big-Data-Technologien. Information – Wissenschaft & Praxis, Mai 2019; Vol. 70(2-3), S. 127–133; DOI: https://doi.org/10.1515/iwp-2019-2001
• Türker, Rima; Zhang, Lei; Koutraki, Maria; Sack, Harald. Knowledge Based Short Text Categorization Using Entity and Category Embeddings. In: The Semantic Web, 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019. Proceedings / Pascal Hitzler; Miriam Fernández; Krzysztof Janowicz; Amrapali Zaveri; Alasdair J.G. Gray; Vanessa Lopez; Armin Haller; Karl Hammar (Hrsg.); Cham: Springer 2019; 622 S.; ISBN 978-3-030-21347-3; Lecture Notes in Computer Science Vol. 11503, S. 346-362. DOI: https://doi.org/10.1007/978-3-030-21348-0_23

2018

• Türker, Rima; Zhang, Lei; Koutraki, Maria; Sack, Harald. TECNE: Knowledge Based Text Classification Using Network Embeddings. In: Proceedings of the EKAW 2018 Posters and Demonstrations Session, co-located with 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018), Nancy, France, November 12-16, 2018 / Cimiano, Philipp; Corby, Olivier (eds.). Published on CEUR-WS.org: 03-Dec-2018; CEUR workshop proceedings Vol. 2262; S. 53-56, http://ceur-ws.org/Vol-2262/ekaw-demo-18.pdf
• Türker, Rima; Zhang, Lei; Koutraki, Maria; Sack, Harald. “The Less Is More” for Text Classification. In: Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems, co-located with the 14th International Conference on Semantic Systems (SEMANTiCS 2018), Vienna, Austria, September 10-13, 2018 / Khalili, Ali; Koutraki, Maria (eds.). Published on CEUR-WS.org: 05-Sept-2018; CEUR workshop proceedings Vol. 2198, http://ceur-ws.org/Vol-2198/paper_122.pdf
• Sofean, Mustafa; Aras, Hidir. Technological areas detection and clustering for large-scale of patent texts. In: The International Conference on Big Data Analytics, Data Mining and Computational Interlligence 2018 (BigDaCI), July 17-20, 2018, Madrid, Spanien; iadis digital library.
• Sofean, Mustafa; Aras, Hidir; Alrifai, Ahmad. A Workflow-based Large-Scale Patent Mining and Analytics Framework. In: Information and Software Technologies. 24th International Conference, ICIST 2018, Vilnius, Lithuania, October 4-6, 2018. Damasevicius, Robertas; Vasiljeviene, Giedre (Hrsg.); Cham: Springer 2018; 615 S.; ISBN 978-3-319-99972-2; S. 210-223. https://link.springer.com/chapter/10.1007/978-3-319-99972-2_17
• Aras, Hidir; Türker, Rima; Geiss, Dieter; Milbradt, Max; Sack, Harald. Get Your Hands Dirty: Evaluating Word2Vec Models for Patent Data. In: Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems, co-located with the 14th International Conference on Semantic Systems (SEMANTICS 2018), Vienna, Austria, September 10-13, 2018 / Khalili, Ali; Koutraki, Maria (eds.). Published on CEUR-WS.org: 05-Sept-2018; CEUR workshop proceedings Vol. 2198, http://ceur-ws.org/Vol-2198/paper_123.pdf

2017

• Sofean, Mustafa. Automatic Segmentation of Big Data of Patent Texts. In: Big Data Analytics and Knowledge Discovery: 19th International Conference, DaWaK 2017, Lyon, France, August 28–31, 2017, Proceedings / Bellatreche, Ladjel, Chakravarthy, Sharma (Hrsg.); Cham: Springer International Publishing, 2017; 502 S.; ISBN 978-3-319-64282-6; Lecture Notes in Computer Science 10440, S. 343-351. DOI: https://doi.org/10.1007/978-3-319-64283-3_25
• Zhang, Lei; Acosta, Maribel; Färber, Michael; Thoma, Steffen; Rettinger, Achim. BreXearch: Exploring Brexit Data Using Cross-Lingual and Cross-Media Semantic Search. In: ISWC 2017 Posters and Demonstrations and Industry Tracks. Proceedings of the ISWC 2017. 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, / Nikitina, Nadeschda; Song, Dezhao; Fokoue, Achille; Haase, Peter. (Hrsg.); Published on CEUR-WS.org: 22-Oct-2017; CEUR Workshop Proceedings Vol. 1963.
• Wu, Tianxing; Zhang, Du; Zhang, Lei; Qi, Guilin. Cross-Lingual Taxonomy Alignment with Bilingual Knowledge Graph Embeddings. In: Semantic Technology. 7th Joint International Conference, JIST 2017, Cold Coast, QLD, Australia, November 10-12, 2017. Proceedings, Wang, Zhe, Turhan, Anni-Yasmin; Wang, Kewen; Zhang, Xiaowang (Hrsg.) Cham: Springer International Publishing, 2017; 365 S.; ISBN 978-3-319-70681-8; Lecture Notes in Computer Science 10675, S. 251-258. DOI: 10.1007/978-3-319-70682-5 http://www.springer.com/de/book/9783319706818
• Wu, Tianxing; Gao, Cong; Qi, Guilin; Zhang, Lei; Dong, Chuanqi; Liu, He; Zhang, Du. KG-Buddhism: The Chinese Knowledge Graph on Buddhism. In: Semantic Technology. 7th Joint International Conference, JIST 2017, Cold Coast, QLD, Australia, November 10-12, 2017. Proceedings, Wang, Zhe, Turhan, Anni-Yasmin; Wang, Kewen; Zhang, Xiaowang (Hrsg.) Cham: Springer International Publishing, 2017; 365 S.; ISBN 978-3-319-70681-8; Lecture Notes in Computer Science 10675, S. 259-267. DOI: 10.1007/978-3-319-70682-5 http://www.springer.com/de/book/9783319706818
• Hackl-Sommer, René; Schwantner, Michael. Patent Claim Structure Recognition. Archives of Data Science, Series A, 2017, v.  2(1), 15 S. http://www.archivesofdatascience.org/journals/series_a/publications. DOI: 10.5445/KSP/1000058749/17

 - 2016

• Sofean, M.
  Automatic Segmentation of Big Data of Patent Texts
  in: Proceedings of the International Conference on Big Data Analytics and Knowledge Discovery. DaWaK 2017. Springer  pp 343-351.
• Aras, H.; Hackl-Sommer, R.; Schwantner, M.; Sofean, M.
  Applications and Challenges of Text Mining with Patents. IPaMin@KONVENS (2014).
• Saad, F.; Nürnberger, A.
  Overview of prior-art cross-lingual information retrieval approaches
  in: World Patent Information Journal, Volume 34, Issue 4, 304-314 2012.
• Hackl-Sommer, R.
  Transparentes Ranking und Relevanz-Feedback im Patentretrieval
  Dissertation. University of Hildesheim. FIZ-KA-2010. Fachinformationszentrum Karlsruhe, 2010.
• Hackl-Sommer, R.; Mandl, T.; Womser-Hacker, C.
  Ad-Hoc mono-and bilingual retrieval experiments at the University of Hildesheim
  in: Workshop of the Cross-Language Evaluation Forum for European Languages. Springer, Berlin, Heidelberg, 2005.

• FIZ Special Corona: Applying Semantic Technologies for Knowledge Discovery from Patents – a case study for the COVID-19 pandemic