Thinking outside the graph: scholarly knowledge graph construction leveraging natural language processing

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. The document-oriented workflows in science publication have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis. In this form, scientific knowledge remains locked in representations that are inadequate for machine processing. As long as scholarly communication remains in this form, we cannot take advantage of all the advancements taking place in machine learning and natural language processing techniques. Such techniques would facilitate the transformation from pure text based into (semi-)structured semantic descriptions that are interlinked in a collection of big federated graphs. We are in dire need for a new age of semantically enabled infrastructure adept at storing, manipulating, and querying scholarly knowledge. Equally important is a suite of machine assistance tools designed to populate, curate, and explore the resulting scholarly knowledge graph. In this thesis, we address the issue of constructing a scholarly knowledge graph using natural language processing techniques. First, we tackle the issue of developing a scholarly knowledge graph for structured scholarly communication, that can be populated and constructed automatically. We co-design and co-implement the Open Research Knowledge Graph (ORKG), an infrastructure capable of modeling, storing, and automatically curating scholarly communications. Then, we propose a method to automatically extract information into knowledge graphs. With Plumber, we create a framework to dynamically compose open information extraction pipelines based on the input text. Such pipelines are composed from community-created information extraction components in an effort to consolidate individual research contributions under one umbrella. We further present MORTY as a more targeted approach that leverages automatic text summarization to create from the scholarly article's text structured summaries containing all required information. In contrast to the pipeline approach, MORTY only extracts the information it is instructed to, making it a more valuable tool for various curation and contribution use cases. Moreover, we study the problem of knowledge graph completion. exBERT is able to perform knowledge graph completion tasks such as relation and entity prediction tasks on scholarly knowledge graphs by means of textual triple classification. Lastly, we use the structured descriptions collected from manual and automated sources alike with a question answering approach that builds on the machine-actionable descriptions in the ORKG. We propose JarvisQA, a question answering interface operating on tabular views of scholarly knowledge graphs i.e., ORKG comparisons. JarvisQA is able to answer a variety of natural language questions, and retrieve complex answers on pre-selected sub-graphs. These contributions are key in the broader agenda of studying the feasibility of natural language processing methods on scholarly knowledge graphs, and lays the foundation of which methods can be used on which cases. Our work indicates what are the challenges and issues with automatically constructing scholarly knowledge graphs, and opens up future research directions

A Personal Research Agent for Semantic Knowledge Management of Scientific Literature

The unprecedented rate of scientific publications is a major threat to the productivity of knowledge workers, who rely on scrutinizing the latest scientific discoveries for their daily tasks. Online digital libraries, academic publishing databases and open access repositories grant access to a plethora of information that can overwhelm a researcher, who is looking to obtain fine-grained knowledge relevant for her task at hand. This overload of information has encouraged researchers from various disciplines to look for new approaches in extracting, organizing, and managing knowledge from the immense amount of available literature in ever-growing repositories. In this dissertation, we introduce a Personal Research Agent that can help scientists in discovering, reading and learning from scientific documents, primarily in the computer science domain. We demonstrate how a confluence of techniques from the Natural Language Processing and Semantic Web domains can construct a semantically-rich knowledge base, based on an inter-connected graph of scholarly artifacts – effectively transforming scientific literature from written content in isolation, into a queryable web of knowledge, suitable for machine interpretation. The challenges of creating an intelligent research agent are manifold: The agent's knowledge base, analogous to his 'brain', must contain accurate information about the knowledge `stored' in documents. It also needs to know about its end-users' tasks and background knowledge. In our work, we present a methodology to extract the rhetorical structure (e.g., claims and contributions) of scholarly documents. We enhance our approach with entity linking techniques that allow us to connect the documents with the Linked Open Data (LOD) cloud, in order to enrich them with additional information from the web of open data. Furthermore, we devise a novel approach for automatic profiling of scholarly users, thereby, enabling the agent to personalize its services, based on a user's background knowledge and interests. We demonstrate how we can automatically create a semantic vector-based representation of the documents and user profiles and utilize them to efficiently detect similar entities in the knowledge base. Finally, as part of our contributions, we present a complete architecture providing an end-to-end workflow for the agent to exploit the opportunities of linking a formal model of scholarly users and scientific publications

RESEARCH ON INFORMATION RESOURCES AGGREGATION IN ACADEMIC TO SEMANTIC PUBLISHING

With the constant development of information and digitization, the proportion of digitization in scientific research publications is increasing day by day. On the one hand, the rapid growth of digital scientific research data and academic literature has provided many facilities for academic exchanges among scientific research users. On the basis of systematically combing the relevant theories of semantic publishing and information resource integration, this paper summarizes the current situation of information resource aggregation in academic journals and the significance of digital resource aggregation. Secondly, this paper illustrates the important role of semantic information resource integration in semantic publishing of academic journals. Taking Elsevier semantic publishingmodel as an example, it focuses on the resource query and resource utilization under semantic publishing. Final adoption with the comparison of web of science database and the analysis and evaluation of the results of resource aggregation verify the feasibility of the semantic based digitalresource aggregation method in the digital publication of academic journals.Keywords: Semantic Publishing; Semantic Web, Digital Resource, and Aggregation elsevi

Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Challenges as enablers for high quality linked data: Insights from the semantic publishing challenge

While most challenges organized so far in the Semantic Web domain are focused on comparing tools with respect to different criteria such as their features and competencies, or exploiting semantically enriched data, the Semantic Web Evaluation Challenges series, co-located with the ESWC Semantic Web Conference, aims to compare them based on their output, namely the produced dataset. The Semantic Publishing Challenge is one of these challenges. Its goal is to involve participants in extracting data from heterogeneous sources on scholarly publications, and producing Linked Data that can be exploited by the community itself. This paper reviews lessons learned from both (i) the overall organization of the Semantic Publishing Challenge, regarding the definition of the tasks, building the input dataset and forming the evaluation, and (ii) the results produced by the participants, regarding the proposed approaches, the used tools, the preferred vocabularies and the results produced in the three editions of 2014, 2015 and 2016. We compared these lessons to other Semantic Web Evaluation Challenges. In this paper, we (i) distill best practices for organizing such challenges that could be applied to similar events, and (ii) report observations on Linked Data publishing derived from the submitted solutions. We conclude that higher quality may be achieved when Linked Data is produced as a result of a challenge, because the competition becomes an incentive, while solutions become better with respect to Linked Data publishing best practices when they are evaluated against the rules of the challenge

Graph Data-Models and Semantic Web Technologies in Scholarly Digital Editing

This volume is based on the selected papers presented at the Workshop on Scholarly Digital Editions, Graph Data-Models and Semantic Web Technologies, held at the Uni- versity of Lausanne in June 2019. The Workshop was organized by Elena Spadini (University of Lausanne) and Francesca Tomasi (University of Bologna), and spon- sored by the Swiss National Science Foundation through a Scientific Exchange grant, and by the Centre de recherche sur les lettres romandes of the University of Lausanne. The Workshop comprised two full days of vibrant discussions among the invited speakers, the authors of the selected papers, and other participants.1 The acceptance rate following the open call for papers was around 60%. All authors – both selected and invited speakers – were asked to provide a short paper two months before the Workshop. The authors were then paired up, and each pair exchanged papers. Paired authors prepared questions for one another, which were to be addressed during the talks at the Workshop; in this way, conversations started well before the Workshop itself. After the Workshop, the papers underwent a second round of peer-review before inclusion in this volume. This time, the relevance of the papers was not under discus- sion, but reviewers were asked to appraise specific aspects of each contribution, such as its originality or level of innovation, its methodological accuracy and knowledge of the literature, as well as more formal parameters such as completeness, clarity, and coherence. The bibliography of all of the papers is collected in the public Zotero group library GraphSDE20192, which has been used to generate the reference list for each contribution in this volume. The invited speakers came from a wide range of backgrounds (academic, commer- cial, and research institutions) and represented the different actors involved in the remediation of our cultural heritage in the form of graphs and/or in a semantic web en- vironment. Georg Vogeler (University of Graz) and Ronald Haentjens Dekker (Royal Dutch Academy of Sciences, Humanities Cluster) brought the Digital Humanities research perspective; the work of Hans Cools and Roberta Laura Padlina (University of Basel, National Infrastructure for Editions), as well as of Tobias Schweizer and Sepi- deh Alassi (University of Basel, Digital Humanities Lab), focused on infrastructural challenges and the development of conceptual and software frameworks to support re- searchers’ needs; Michele Pasin’s contribution (Digital Science, Springer Nature) was informed by his experiences in both academic research, and in commercial technology companies that provide services for the scientific community. The Workshop featured not only the papers of the selected authors and of the invited speakers, but also moments of discussion between interested participants. In addition to the common Q&A time, during the second day one entire session was allocated to working groups delving into topics that had emerged during the Workshop. Four working groups were created, with four to seven participants each, and each group presented a short report at the end of the session. Four themes were discussed: enhancing TEI from documents to data; ontologies for the Humanities; tools and infrastructures; and textual criticism. All of these themes are represented in this volume. The Workshop would not have been of such high quality without the support of the members of its scientific committee: Gioele Barabucci, Fabio Ciotti, Claire Clivaz, Marion Rivoal, Greta Franzini, Simon Gabay, Daniel Maggetti, Frederike Neuber, Elena Pierazzo, Davide Picca, Michael Piotrowski, Matteo Romanello, Maïeul Rouquette, Elena Spadini, Francesca Tomasi, Aris Xanthos – and, of course, the support of all the colleagues and administrative staff in Lausanne, who helped the Workshop to become a reality. The final versions of these papers underwent a single-blind peer review process. We want to thank the reviewers: Helena Bermudez Sabel, Arianna Ciula, Marilena Daquino, Richard Hadden, Daniel Jeller, Tiziana Mancinelli, Davide Picca, Michael Piotrowski, Patrick Sahle, Raffaele Viglianti, Joris van Zundert, and others who preferred not to be named personally. Your input enhanced the quality of the volume significantly! It is sad news that Hans Cools passed away during the production of the volume. We are proud to document a recent state of his work and will miss him and his ability to implement the vision of a digital scholarly edition based on graph data-models and semantic web technologies. The production of the volume would not have been possible without the thorough copy-editing and proof reading by Lucy Emmerson and the support of the IDE team, in particular Bernhard Assmann, the TeX-master himself. This volume is sponsored by the University of Bologna and by the University of Lausanne. Bologna, Lausanne, Graz, July 2021 Francesca Tomasi, Elena Spadini, Georg Vogele

Ontology-driven document enrichment: principles, tools and applications

In this paper, we present an approach to document enrichment, which consists of developing and integrating formal knowledge models with archives of documents, to provide intelligent knowledge retrieval and (possibly) additional knowledge-intensive services, beyond what is currently available using “standard” information retrieval and search facilities. Our approach is ontology-driven, in the sense that the construction of the knowledge model is carried out in a top-down fashion, by populating a given ontology, rather than in a bottom-up fashion, by annotating a particular document. In this paper, we give an overview of the approach and we examine the various types of issues (e.g. modelling, organizational and user interface issues) which need to be tackled to effectively deploy our approach in the workplace. In addition, we also discuss a number of technologies we have developed to support ontology-driven document enrichment and we illustrate our ideas in the domains of electronic news publishing, scholarly discourse and medical guidelines

Knowledge-Based Decision Support for Integrated Water Resources Management with an application for Wadi Shueib, Jordan

This book takes a two-staged approach to contribute to the contemporary Integrated Water Resources Management (IWRM) research. First it investigates sub-basin-scale IWRM modelling and scenario planning. The Jordanian Wadi Shueib is used as exemplary case study. Then, it develops a framework to collaboratively manage planning and decision making knowledge on the basis of semantic web technologies. Future IWRM initiatives can benefit from the valuable insights achieved in the presented study