Enhancing Access to Contextual Information on Individuals, Families, and Corporate Bodies for Archival Collections

We will address the ongoing challenge of transforming description of and improving access to primary humanities resources via advanced technologies. The project will test the feasibility of using existing archival descriptions in new ways, in order to enhance access and understanding of cultural resources in archives, libraries, and museums. We will derive Encoded Archival Context-Corporate Bodies, Persons, and Families (EAC-CPF) records from existing archival findings aids from the Library of Congress (LoC) and three consortia, and name authority files from the LoC and the Getty Vocabulary Program. We will produce open-source software used in the derivation and creation of the EAC-CPF records and a prototype access system demonstrating their value to the archival community and the use of primary humanities resources. The Institute for Advanced Technology in the Humanities, Univ. of Virginia, will partner with the California Digital Library and the School of Information, UC Berkeley

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project

BIM semantic-enrichment for built heritage representation

In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities

Using Web Archives to Enrich the Live Web Experience Through Storytelling

Much of our cultural discourse occurs primarily on the Web. Thus, Web preservation is a fundamental precondition for multiple disciplines. Archiving Web pages into themed collections is a method for ensuring these resources are available for posterity. Services such as Archive-It exists to allow institutions to develop, curate, and preserve collections of Web resources. Understanding the contents and boundaries of these archived collections is a challenge for most people, resulting in the paradox of the larger the collection, the harder it is to understand. Meanwhile, as the sheer volume of data grows on the Web, storytelling is becoming a popular technique in social media for selecting Web resources to support a particular narrative or story . In this dissertation, we address the problem of understanding the archived collections through proposing the Dark and Stormy Archive (DSA) framework, in which we integrate storytelling social media and Web archives. In the DSA framework, we identify, evaluate, and select candidate Web pages from archived collections that summarize the holdings of these collections, arrange them in chronological order, and then visualize these pages using tools that users already are familiar with, such as Storify. To inform our work of generating stories from archived collections, we start by building a baseline for the structural characteristics of popular (i.e., receiving the most views) human-generated stories through investigating stories from Storify. Furthermore, we checked the entire population of Archive-It collections for better understanding the characteristics of the collections we intend to summarize. We then filter off-topic pages from the collections the using different methods to detect when an archived page in a collection has gone off-topic. We created a gold standard dataset from three Archive-It collections to evaluate the proposed methods at different thresholds. From the gold standard dataset, we identified five behaviors for the TimeMaps (a list of archived copies of a page) based on the page’s aboutness. Based on a dynamic slicing algorithm, we divide the collection and cluster the pages in each slice. We then select the best representative page from each cluster based on different quality metrics (e.g., the replay quality, and the quality of the generated snippet from the page). At the end, we put the selected pages in chronological order and visualize them using Storify. For evaluating the DSA framework, we obtained a ground truth dataset of hand-crafted stories from Archive-It collections generated by expert archivists. We used Amazon’s Mechanical Turk to evaluate the automatically generated stories against the stories that were created by domain experts. The results show that the automatically generated stories by the DSA are indistinguishable from those created by human subject domain experts, while at the same time both kinds of stories (automatic and human) are easily distinguished from randomly generated storie

MementoMap: A Web Archive Profiling Framework for Efficient Memento Routing

With the proliferation of public web archives, it is becoming more important to better profile their contents, both to understand their immense holdings as well as to support routing of requests in Memento aggregators. A memento is a past version of a web page and a Memento aggregator is a tool or service that aggregates mementos from many different web archives. To save resources, the Memento aggregator should only poll the archives that are likely to have a copy of the requested Uniform Resource Identifier (URI). Using the Crawler Index (CDX), we generate profiles of the archives that summarize their holdings and use them to inform routing of the Memento aggregator’s URI requests. Additionally, we use full text search (when available) or sample URI lookups to build an understanding of an archive’s holdings. Previous work in profiling ranged from using full URIs (no false positives, but with large profiles) to using only top-level domains (TLDs) (smaller profiles, but with many false positives). This work explores strategies in between these two extremes. For evaluation we used CDX files from Archive-It, UK Web Archive, Stanford Web Archive Portal, and Arquivo.pt. Moreover, we used web server access log files from the Internet Archive’s Wayback Machine, UK Web Archive, Arquivo.pt, LANL’s Memento Proxy, and ODU’s MemGator Server. In addition, we utilized historical dataset of URIs from DMOZ. In early experiments with various URI-based static profiling policies we successfully identified about 78% of the URIs that were not present in the archive with less than 1% relative cost as compared to the complete knowledge profile and 94% URIs with less than 10% relative cost without any false negatives. In another experiment we found that we can correctly route 80% of the requests while maintaining about 0.9 recall by discovering only 10% of the archive holdings and generating a profile that costs less than 1% of the complete knowledge profile. We created MementoMap, a framework that allows web archives and third parties to express holdings and/or voids of an archive of any size with varying levels of details to fulfil various application needs. Our archive profiling framework enables tools and services to predict and rank archives where mementos of a requested URI are likely to be present. In static profiling policies we predefined the maximum depth of host and path segments of URIs for each policy that are used as URI keys. This gave us a good baseline for evaluation, but was not suitable for merging profiles with different policies. Later, we introduced a more flexible means to represent URI keys that uses wildcard characters to indicate whether a URI key was truncated. Moreover, we developed an algorithm to rollup URI keys dynamically at arbitrary depths when sufficient archiving activity is detected under certain URI prefixes. In an experiment with dynamic profiling of archival holdings we found that a MementoMap of less than 1.5% relative cost can correctly identify the presence or absence of 60% of the lookup URIs in the corresponding archive without any false negatives (i.e., 100% recall). In addition, we separately evaluated archival voids based on the most frequently accessed resources in the access log and found that we could have avoided more than 8% of the false positives without introducing any false negatives. We defined a routing score that can be used for Memento routing. Using a cut-off threshold technique on our routing score we achieved over 96% accuracy if we accept about 89% recall and for a recall of 99% we managed to get about 68% accuracy, which translates to about 72% saving in wasted lookup requests in our Memento aggregator. Moreover, when using top-k archives based on our routing score for routing and choosing only the topmost archive, we missed only about 8% of the sample URIs that are present in at least one archive, but when we selected top-2 archives, we missed less than 2% of these URIs. We also evaluated a machine learning-based routing approach, which resulted in an overall better accuracy, but poorer recall due to low prevalence of the sample lookup URI dataset in different web archives. We contributed various algorithms, such as a space and time efficient approach to ingest large lists of URIs to generate MementoMaps and a Random Searcher Model to discover samples of holdings of web archives. We contributed numerous tools to support various aspects of web archiving and replay, such as MemGator (a Memento aggregator), Inter- Planetary Wayback (a novel archival replay system), Reconstructive (a client-side request rerouting ServiceWorker), and AccessLog Parser. Moreover, this work yielded a file format specification draft called Unified Key Value Store (UKVS) that we use for serialization and dissemination of MementoMaps. It is a flexible and extensible file format that allows easy interactions with Unix text processing tools. UKVS can be used in many applications beyond MementoMaps

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

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

Detecting, Modeling, and Predicting User Temporal Intention

The content of social media has grown exponentially in the recent years and its role has evolved from narrating life events to actually shaping them. Unfortunately, content posted and shared in social networks is vulnerable and prone to loss or change, rendering the context associated with it (a tweet, post, status, or others) meaningless. There is an inherent value in maintaining the consistency of such social records as in some cases they take over the task of being the first draft of history as collections of these social posts narrate the pulse of the street during historic events, protest, riots, elections, war, disasters, and others as shown in this work. The user sharing the resource has an implicit temporal intent: either the state of the resource at the time of sharing, or the current state of the resource at the time of the reader \clicking . In this research, we propose a model to detect and predict the user\u27s temporal intention of the author upon sharing content in the social network and of the reader upon resolving this content. To build this model, we first examine the three aspects of the problem: the resource, time, and the user. For the resource we start by analyzing the content on the live web and its persistence. We noticed that a portion of the resources shared in social media disappear, and with further analysis we unraveled a relationship between this disappearance and time. We lose around 11% of the resources after one year of sharing and a steady 7% every following year. With this, we turn to the public archives and our analysis reveals that not all posted resources are archived and even they were an average 8% per year disappears from the archives and in some cases the archived content is heavily damaged. These observations prove that in regards to archives resources are not well-enough populated to consistently and reliably reconstruct the missing resource as it existed at the time of sharing. To analyze the concept of time we devised several experiments to estimate the creation date of the shared resources. We developed Carbon Date, a tool which successfully estimated the correct creation dates for 76% of the test sets. Since the resources\u27 creation we wanted to measure if and how they change with time. We conducted a longitudinal study on a data set of very recently-published tweet-resource pairs and recording observations hourly. We found that after just one hour, ~4% of the resources have changed by ≥30% while after a day the change rate slowed to be ~12% of the resources changed by ≥40%. In regards to the third and final component of the problem we conducted user behavioral analysis experiments and built a data set of 1,124 instances manually assigned by test subjects. Temporal intention proved to be a difficult concept for average users to understand. We developed our Temporal Intention Relevancy Model (TIRM) to transform the highly subjective temporal intention problem into the more easily understood idea of relevancy between a tweet and the resource it links to, and change of the resource through time. On our collected data set TIRM produced a significant 90.27% success rate. Furthermore, we extended TIRM and used it to build a time-based model to predict temporal intention change or steadiness at the time of posting with 77% accuracy. We built a service API around this model to provide predictions and a few prototypes. Future tools could implement TIRM to assist users in pushing copies of shared resources into public web archives to ensure the integrity of the historical record. Additional tools could be used to assist the mining of the existing social media corpus by derefrencing the intended version of the shared resource based on the intention strength and the time between the tweeting and mining

Assessing the Prevalence and Archival Rate of URIs to Git Hosting Platforms in Scholarly Publications

The definition of scholarly content has expanded to include the data and source code that contribute to a publication. While major archiving efforts to preserve conventional scholarly content, typically in PDFs (e.g., LOCKSS, CLOCKSS, Portico), are underway, no analogous effort has yet emerged to preserve the data and code referenced in those PDFs, particularly the scholarly code hosted online on Git Hosting Platforms (GHPs). Similarly, Software Heritage is working to archive public source code, but there is value in archiving the surrounding ephemera that provide important context to the code while maintaining their original URIs. In current implementations, source code and its ephemera are not preserved, which presents a problem for scholarly projects where reproducibility matters. To quantify the scope of this issue, we analyzed the use of GHP URIs in the arXiv and PMC corpora. In total, there were 253,590 URIs to GitHub, SourceForge, Bitbucket, and GitLab repositories across the 2.64 million publications. Authors have increasingly included GHP URIs in scholarly publications and, in 2021, one in five arXiv publications included a GitHub URI. Next, we analyzed the archival coverage of scholarly GHP URIs in Web archives and Software Heritage. Overall, 79.15% of GHP URIs were archived in the Web archives while only 62.06% of GHP URIs were archived in Software Heritage. We used a machine learning classifier to identify other Open Access Data and Software (OADS) URIs outside of the four GHPs previously studied. We found almost 50,000 unique OADS hostnames and more non-GHP OADS URIs than GHP URIs. The prevalence of OADS URIs and vast number of unique hostnames points to the utility of a classifier to identify OADS URIs as opposed to manual enumeration. Lastly, we found a statistically significant relationship between the popularity of a GitHub repository as determined by engagement metrics and archival coverage indicating that less popular repositories less likely to be archived and, thus, more vulnerable to being unrecoverable. The growing use of GHPs in scholarly publications points to an urgent and growing need for dedicated efforts to archive their holdings in order to preserve research code and its scholarly ephemera