Search Results
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
2020, Mulang’, Isaiah Onando, Singh, Kuldeep, Vyas, Akhilesh, Shekarpour, Saeedeh, Vidal, Maria-Esther, Lehmann, Jens, Auer, Sören, Huang, Zhisheng, Beek, Wouter, Wang, Hua, Zhou, Rui, Zhang, Yanchun
The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows ≈8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.
Mathematics in Wikidata
2021, Scharpf, Philipp, Schubotz, Moritz, Gipp, Bela, Kaffee, Lucie-Aimée, Razniewski, Simon, Hogan, Aidan
Documents from Science, Technology, Engineering, and Mathematics (STEM) disciplines usually contain a signicant amount of mathematical formulae alongside text. Some Mathematical Information Retrieval (MathIR) systems, e.g., Mathematical Question Answering (MathQA), exploit knowledge from Wikidata. Therefore, the mathematical information needs to be stored in items. In the last years, there have been efforts to define several properties and seed formulae together with their constituting identifiers into Wikidata. This paper summarizes the current state, challenges, and discussions related to this endeavor. Furthermore, some data mining methods (supervised formula annotation and concept retrieval) and applications (question answering and classification explainability) of the mathematical information are outlined. Finally, we discuss community feedback and issues related to integrating Mathematical Entity Linking (MathEL) into Wikidata and Wikipedia, which was rejected in 33% and 12% of the test cases, for Wikidata and Wikipedia respectively. Our long-term goal is to populate Wikidata, such that it can serve a variety of automated math reasoning tasks and AI systems.
When humans and machines collaborate: Cross-lingual Label Editing in Wikidata
2019, Kaffee, L.-A., Endris, K.M., Simperl, E.
The quality and maintainability of a knowledge graph are determined by the process in which it is created. There are different approaches to such processes; extraction or conversion of available data in the web (automated extraction of knowledge such as DBpedia from Wikipedia), community-created knowledge graphs, often by a group of experts, and hybrid approaches where humans maintain the knowledge graph alongside bots. We focus in this work on the hybrid approach of human edited knowledge graphs supported by automated tools. In particular, we analyse the editing of natural language data, i.e. labels. Labels are the entry point for humans to understand the information, and therefore need to be carefully maintained. We take a step toward the understanding of collaborative editing of humans and automated tools across languages in a knowledge graph. We use Wikidata as it has a large and active community of humans and bots working together covering over 300 languages. In this work, we analyse the different editor groups and how they interact with the different language data to understand the provenance of the current label data.
Detecting Cross-Language Plagiarism using Open Knowledge Graphs
2021, Stegmüller, Johannes, Bauer-Marquart, Fabian, Meuschke, Norman, Ruas, Terry, Schubotz, Moritz, Gipp, Bela, Zhang, Chengzhi, Mayr, Philipp, Lu, Wie, Zhang, Yi
Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application toWebscale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA’s performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available.
Baroque AI
2023, Worthington, Simon, Blümel, Ina
Publication prototype: A computational publishing and AI assisted writing course unit with students of the Open Knowledge class – at Hochschule Hannover with the Open Science Lab, TIB. The prototype publication exercise involves creating a fictional ‘exhibition catalogue’ drawing on Wikidata based cataloguing of seventeenth century painting deposited by the Bavarian State Painting Collections. The prototype demostrates how computational publishing can be used to bring together different distributed linked open data (LOD) sources. Additionally AI tools are used for assisted essay writing. Then both are encapsulated in a multi-format computational publication — allowing for asynchronous collaborative working. Distributed LOD sources include: Wikidata/base, Nextcloud, Thoth, Semantic Kompakkt, and TIB AV Portal. AI tools used for essay writing are — OpenAI and Perplexity. Eleven students completed the class unit which was carried out over March to April 2023. An open access OER guide to running the class, a template publication for use in the class are online on GitHub and designed for OER reuse. Full class information and resources are on Wikiversity. The open source software used is brought together in the ADA Pipeline.
Discovery and efficient reuse of technology pictures using Wikimedia infrastructures. A proposal
2016, Heller, Lambert, Blümel, Ina, Cartellieri, Simone, Wartena, Christian
Multimedia objects, especially images and figures, are essential for the visualization and interpretation of research findings. The distribution and reuse of these scientific objects is significantly improved under open access conditions, for instance in Wikipedia articles, in research literature, as well as in education and knowledge dissemination, where licensing of images often represents a serious barrier. Whereas scientific publications are retrievable through library portals or other online search services due to standardized indices there is no targeted retrieval and access to the accompanying images and figures yet. Consequently there is a great demand to develop standardized indexing methods for these multimedia open access objects in order to improve the accessibility to this material. With our proposal, we hope to serve a broad audience which looks up a scientific or technical term in a web search portal first. Until now, this audience has little chance to find an openly accessible and reusable image narrowly matching their search term on first try - frustratingly so, even if there is in fact such an image included in some open access article.
Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
2020, Sakor, Ahmad, Singh, Kuldeep, Patel, Anery, Vidal, Maria-Esther
The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from a natural language text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, there are still limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, the first joint entity and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for the linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is open source and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository (https://github.com/SDM-TIB/falcon2.0). We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.
Digital und partizipativ - Projekte in der Erinnerungskultur
2024, Fahrenkrog, Gabriele, Groh-Trautmann, Lisa
Die Erinnerungskultur an den Nationalsozialismus in Deutschland befindet sich im Umbruch. Doch wie kann eine lebendige Erinnerungskultur, die ein wichtige Grundlage für die Etablierung demokratischer Werte darstellt, gefördert werden? Die Digitalisierung und die Bereitstellung von offenen Daten eröffnen hierbei neue Wege. In Zusammenarbeit mit und gefördert durch verschiedene Stiftungen hat die TIB seit 2020 in mehreren Drittmittel-geförderten Projekten nach diesen neuen Wegen gesucht und Formen forschender Erinnerungsarbeit vor Ort erprobt. Anhand der TIB-Projekte “Orte des Gestapoterrors”, http://tib.eu/gestapo-terror-orte, “Remember Me!” rememberme-data.de und “WikiRemembrance” https://www.wikiremembrance.de/ soll exemplarisch aufgezeigt werden, wie Communities der Erinnerungskultur, Schülerinnen und Schüler aber auch andere Interessierte dazu befähigt werden können, selbst über den Umgang mit offenen Daten zu reflektieren, sie zu erzeugen, zu veröffentlichen und die Daten in neuen Kontexten zu verwenden. Zudem berichten wir, wie im Rahmen der Projekte in kollaborativen und partizipativen Arbeitsprozessen gemeinsam mit den Communities Daten der Öffentlichkeit zur Verfügung gestellt werden und welche Wege zur Vernetzung sich dafür als sinnvoll erwiesen haben. Im Verlauf der Projektarbeit hat sich gezeigt, dass sich unterschiedliche Zielgruppen, darunter Schülerinnen und Schüler, durch die Beschäftigung mit offenen Daten zu einem gemeinsamen forschenden Lernen über zeitgeschichtliche Themen vor Ort motivieren lassen und so erfahren, dass sie aktiv zu einer lebendigen Erinnerungskultur beitragen können. Die im Rahmen von “Orte des Gestapoterrors”, Remember Me! und “WikiRemembrance” erprobten digitalen Interaktionsformen lassen sich auch auf andere Projekte übertragen.