19,777 research outputs found

    Entity-centric knowledge discovery for idiosyncratic domains

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    Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods

    SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

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    We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities

    Advanced Methods for Entity Linking in the Life Sciences

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    The amount of knowledge increases rapidly due to the increasing number of available data sources. However, the autonomy of data sources and the resulting heterogeneity prevent comprehensive data analysis and applications. Data integration aims to overcome heterogeneity by unifying different data sources and enriching unstructured data. The enrichment of data consists of different subtasks, amongst other the annotation process. The annotation process links document phrases to terms of a standardized vocabulary. Annotated documents enable effective retrieval methods, comparability of different documents, and comprehensive data analysis, such as finding adversarial drug effects based on patient data. A vocabulary allows the comparability using standardized terms. An ontology can also represent a vocabulary, whereas concepts, relationships, and logical constraints additionally define an ontology. The annotation process is applicable in different domains. Nevertheless, there is a difference between generic and specialized domains according to the annotation process. This thesis emphasizes the differences between the domains and addresses the identified challenges. The majority of annotation approaches focuses on the evaluation of general domains, such as Wikipedia. This thesis evaluates the developed annotation approaches with case report forms that are medical documents for examining clinical trials. The natural language provides different challenges, such as similar meanings using different phrases. The proposed annotation method, AnnoMap, considers the fuzziness of natural language. A further challenge is the reuse of verified annotations. Existing annotations represent knowledge that can be reused for further annotation processes. AnnoMap consists of a reuse strategy that utilizes verified annotations to link new documents to appropriate concepts. Due to the broad spectrum of areas in the biomedical domain, different tools exist. The tools perform differently regarding a particular domain. This thesis proposes a combination approach to unify results from different tools. The method utilizes existing tool results to build a classification model that can classify new annotations as correct or incorrect. The results show that the reuse and the machine learning-based combination improve the annotation quality compared to existing approaches focussing on the biomedical domain. A further part of data integration is entity resolution to build unified knowledge bases from different data sources. A data source consists of a set of records characterized by attributes. The goal of entity resolution is to identify records representing the same real-world entity. Many methods focus on linking data sources consisting of records being characterized by attributes. Nevertheless, only a few methods can handle graph-structured knowledge bases or consider temporal aspects. The temporal aspects are essential to identify the same entities over different time intervals since these aspects underlie certain conditions. Moreover, records can be related to other records so that a small graph structure exists for each record. These small graphs can be linked to each other if they represent the same. This thesis proposes an entity resolution approach for census data consisting of person records for different time intervals. The approach also considers the graph structure of persons given by family relationships. For achieving qualitative results, current methods apply machine-learning techniques to classify record pairs as the same entity. The classification task used a model that is generated by training data. In this case, the training data is a set of record pairs that are labeled as a duplicate or not. Nevertheless, the generation of training data is a time-consuming task so that active learning techniques are relevant for reducing the number of training examples. The entity resolution method for temporal graph-structured data shows an improvement compared to previous collective entity resolution approaches. The developed active learning approach achieves comparable results to supervised learning methods and outperforms other limited budget active learning methods. Besides the entity resolution approach, the thesis introduces the concept of evolution operators for communities. These operators can express the dynamics of communities and individuals. For instance, we can formulate that two communities merged or split over time. Moreover, the operators allow observing the history of individuals. Overall, the presented annotation approaches generate qualitative annotations for medical forms. The annotations enable comprehensive analysis across different data sources as well as accurate queries. The proposed entity resolution approaches improve existing ones so that they contribute to the generation of qualitative knowledge graphs and data analysis tasks

    Cross-Domain information extraction from scientific articles for research knowledge graphs

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    Today’s scholarly communication is a document-centred process and as such, rather inefficient. Fundamental contents of research papers are not accessible by computers since they are only present in unstructured PDF files. Therefore, current research infrastructures are not able to assist scientists appropriately in their core research tasks. This thesis addresses this issue and proposes methods to automatically extract relevant information from scientific articles for Research Knowledge Graphs (RKGs) that represent scholarly knowledge structured and interlinked. First, this thesis conducts a requirements analysis for an Open Research Knowledge Graph (ORKG). We present literature-related use cases of researchers that should be supported by an ORKG-based system and their specific requirements for the underlying ontology and instance data. Based on this analysis, the identified use cases are categorised into two groups: The first group of use cases needs manual or semi-automatic approaches for knowledge graph (KG) construction since they require high correctness of the instance data. The second group requires high completeness and can tolerate noisy instance data. Thus, this group needs automatic approaches for KG population. This thesis focuses on the second group of use cases and provides contributions for machine learning tasks that aim to support them. To assess the relevance of a research paper, scientists usually skim through titles, abstracts, introductions, and conclusions. An organised presentation of the articles' essential information would make this process more time-efficient. The task of sequential sentence classification addresses this issue by classifying sentences in an article in categories like research problem, used methods, or obtained results. To address this problem, we propose a novel unified cross-domain multi-task deep learning approach that makes use of datasets from different scientific domains (e.g. biomedicine and computer graphics) and varying structures (e.g. datasets covering either only abstracts or full papers). Our approach outperforms the state of the art on full paper datasets significantly while being competitive for datasets consisting of abstracts. Moreover, our approach enables the categorisation of sentences in a domain-independent manner. Furthermore, we present the novel task of domain-independent information extraction to extract scientific concepts from research papers in a domain-independent manner. This task aims to support the use cases find related work and get recommended articles. For this purpose, we introduce a set of generic scientific concepts that are relevant over ten domains in Science, Technology, and Medicine (STM) and release an annotated dataset of 110 abstracts from these domains. Since the annotation of scientific text is costly, we suggest an active learning strategy based on a state-of-the-art deep learning approach. The proposed method enables us to nearly halve the amount of required training data. Then, we extend this domain-independent information extraction approach with the task of \textit{coreference resolution}. Coreference resolution aims to identify mentions that refer to the same concept or entity. Baseline results on our corpus with current state-of-the-art approaches for coreference resolution showed that current approaches perform poorly on scientific text. Therefore, we propose a sequential transfer learning approach that exploits annotated datasets from non-academic domains. Our experimental results demonstrate that our approach noticeably outperforms the state-of-the-art baselines. Additionally, we investigate the impact of coreference resolution on KG population. We demonstrate that coreference resolution has a small impact on the number of resulting concepts in the KG, but improved its quality significantly. Consequently, using our domain-independent information extraction approach, we populate an RKG from 55,485 abstracts of the ten investigated STM domains. We show that every domain mainly uses its own terminology and that the populated RKG contains useful concepts. Moreover, we propose a novel approach for the task of \textit{citation recommendation}. This task can help researchers improve the quality of their work by finding or recommending relevant related work. Our approach exploits RKGs that interlink research papers based on mentioned scientific concepts. Using our automatically populated RKG, we demonstrate that the combination of information from RKGs with existing state-of-the-art approaches is beneficial. Finally, we conclude the thesis and sketch possible directions of future work.Die Kommunikation von Forschungsergebnissen erfolgt heutzutage in Form von Dokumenten und ist aus verschiedenen Gründen ineffizient. Wesentliche Inhalte von Forschungsarbeiten sind für Computer nicht zugänglich, da sie in unstrukturierten PDF-Dateien verborgen sind. Daher können derzeitige Forschungsinfrastrukturen Forschende bei ihren Kernaufgaben nicht angemessen unterstützen. Diese Arbeit befasst sich mit dieser Problemstellung und untersucht Methoden zur automatischen Extraktion von relevanten Informationen aus Forschungspapieren für Forschungswissensgraphen (Research Knowledge Graphs). Solche Graphen sollen wissenschaftliches Wissen maschinenlesbar strukturieren und verknüpfen. Zunächst wird eine Anforderungsanalyse für einen Open Research Knowledge Graph (ORKG) durchgeführt. Wir stellen literaturbezogene Anwendungsfälle von Forschenden vor, die durch ein ORKG-basiertes System unterstützt werden sollten, und deren spezifische Anforderungen an die zugrundeliegende Ontologie und die Instanzdaten. Darauf aufbauend werden die identifizierten Anwendungsfälle in zwei Gruppen eingeteilt: Die erste Gruppe von Anwendungsfällen benötigt manuelle oder halbautomatische Ansätze für die Konstruktion eines ORKG, da sie eine hohe Korrektheit der Instanzdaten erfordern. Die zweite Gruppe benötigt eine hohe Vollständigkeit der Instanzdaten und kann fehlerhafte Daten tolerieren. Daher erfordert diese Gruppe automatische Ansätze für die Konstruktion des ORKG. Diese Arbeit fokussiert sich auf die zweite Gruppe von Anwendungsfällen und schlägt Methoden für maschinelle Aufgabenstellungen vor, die diese Anwendungsfälle unterstützen können. Um die Relevanz eines Forschungsartikels effizient beurteilen zu können, schauen sich Forschende in der Regel die Titel, Zusammenfassungen, Einleitungen und Schlussfolgerungen an. Durch eine strukturierte Darstellung von wesentlichen Informationen des Artikels könnte dieser Prozess zeitsparender gestaltet werden. Die Aufgabenstellung der sequenziellen Satzklassifikation befasst sich mit diesem Problem, indem Sätze eines Artikels in Kategorien wie Forschungsproblem, verwendete Methoden oder erzielte Ergebnisse automatisch klassifiziert werden. In dieser Arbeit wird für diese Aufgabenstellung ein neuer vereinheitlichter Multi-Task Deep-Learning-Ansatz vorgeschlagen, der Datensätze aus verschiedenen wissenschaftlichen Bereichen (z. B. Biomedizin und Computergrafik) mit unterschiedlichen Strukturen (z. B. Datensätze bestehend aus Zusammenfassungen oder vollständigen Artikeln) nutzt. Unser Ansatz übertrifft State-of-the-Art-Verfahren der Literatur auf Benchmark-Datensätzen bestehend aus vollständigen Forschungsartikeln. Außerdem ermöglicht unser Ansatz die Klassifizierung von Sätzen auf eine domänenunabhängige Weise. Darüber hinaus stellen wir die neue Aufgabenstellung domänenübergreifende Informationsextraktion vor. Hierbei werden, unabhängig vom behandelten wissenschaftlichen Fachgebiet, inhaltliche Konzepte aus Forschungspapieren extrahiert. Damit sollen die Anwendungsfälle Finden von verwandten Arbeiten und Empfehlung von Artikeln unterstützt werden. Zu diesem Zweck führen wir eine Reihe von generischen wissenschaftlichen Konzepten ein, die in zehn Bereichen der Wissenschaft, Technologie und Medizin (STM) relevant sind, und veröffentlichen einen annotierten Datensatz von 110 Zusammenfassungen aus diesen Bereichen. Da die Annotation wissenschaftlicher Texte aufwändig ist, kombinieren wir ein Active-Learning-Verfahren mit einem aktuellen Deep-Learning-Ansatz, um die notwendigen Trainingsdaten zu reduzieren. Die vorgeschlagene Methode ermöglicht es uns, die Menge der erforderlichen Trainingsdaten nahezu zu halbieren. Anschließend erweitern wir unseren domänenunabhängigen Ansatz zur Informationsextraktion um die Aufgabe der Koreferenzauflösung. Die Auflösung von Koreferenzen zielt darauf ab, Erwähnungen zu identifizieren, die sich auf dasselbe Konzept oder dieselbe Entität beziehen. Experimentelle Ergebnisse auf unserem Korpus mit aktuellen Ansätzen zur Koreferenzauflösung haben gezeigt, dass diese bei wissenschaftlichen Texten unzureichend abschneiden. Daher schlagen wir eine Transfer-Learning-Methode vor, die annotierte Datensätze aus nicht-akademischen Bereichen nutzt. Die experimentellen Ergebnisse zeigen, dass unser Ansatz deutlich besser abschneidet als die bisherigen Ansätze. Darüber hinaus untersuchen wir den Einfluss der Koreferenzauflösung auf die Erstellung von Wissensgraphen. Wir zeigen, dass diese einen geringen Einfluss auf die Anzahl der resultierenden Konzepte in dem Wissensgraphen hat, aber die Qualität des Wissensgraphen deutlich verbessert. Mithilfe unseres domänenunabhängigen Ansatzes zur Informationsextraktion haben wir aus 55.485 Zusammenfassungen der zehn untersuchten STM-Domänen einen Forschungswissensgraphen erstellt. Unsere Analyse zeigt, dass jede Domäne hauptsächlich ihre eigene Terminologie verwendet und dass der erstellte Wissensgraph nützliche Konzepte enthält. Schließlich schlagen wir einen Ansatz für die Empfehlung von passenden Referenzen vor. Damit können Forschende einfacher relevante verwandte Arbeiten finden oder passende Empfehlungen erhalten. Unser Ansatz nutzt Forschungswissensgraphen, die Forschungsarbeiten mit in ihnen erwähnten wissenschaftlichen Konzepten verknüpfen. Wir zeigen, dass aktuelle Verfahren zur Empfehlung von Referenzen von zusätzlichen Informationen aus einem automatisch erstellten Wissensgraphen profitieren. Zum Schluss wird ein Fazit gezogen und ein Ausblick für mögliche zukünftige Arbeiten gegeben
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