Ralf Krestel

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• Dr. rer. nat.
• Professor at Kiel University

The comment sections of online news platforms are an important space to indulge in political conversations andto discuss opinions. Although primarily meant as forums where readers discuss amongst each other, they can also spark a dialog with the journalists who authored the article. A small but important fraction of comments address the journalists...

Machine learning approaches have proven to be on or even above human-level accuracy for the task of offensive language detection. In contrast to human experts, however, they often lack the capability of giving explanations for their decisions. This article compares four different approaches to make offensive language detection explainable: an inter...

Hierarchical classification schemes are an effective and natural way to organize large document collections. However, complex schemes make the manual classification time-consuming and require domain experts. Current machine learning approaches for hierarchical classification do not exploit all the information contained in the hierarchical schemes....

Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order, which neglects that some of them are more relevant to users a...

Zusammenfassung Heutige Computeranwendungen sind in der Lage, große Mengen an Wissen zu speichern. Neben großen Sprachmodellen (LLMs), wie ChatGPT, finden auch Wissensgraphen, welche Fakten in Form von Tripeln speichern, Anwendung in modernen Informations- und Wissenssystemen. Diese Zusammenfassung eines Vortrags liefert eine Einführung in diese Te...

The multi-label automatic classification of scientific publications based on a pre-defined taxonomy, also called automatic subject indexing is a continuing research endeavor with significant cross-domain applicability. In this paper, we assess the performance of X-transformer and its variants with other extreme multi-label classification models for...

In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI algorithms enable humans to understand the underlying models and explain their behavior, leading to insights through which the models can be analyzed and improved beyond the accuracy metric by, e.g., debugging the lear...

As machine learning techniques are being increasingly employed for text processing tasks, the need for training data has become a major bottleneck for their application. Manual generation of large scale training datasets tailored to each task is a time consuming and expensive process, which necessitates their automated generation. In this work, we...

That norms matter for politics is a widely shared observation. Existing political science research on norm diffusion, norm localization, and contestations is, however, constrained due to methodological manageability of empirical data. To face this research challenge, we propose an interdisciplinary research collaboration between political and compu...

Creating art is often viewed as a uniquely human endeavor. In this paper, we introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art. Our approach is based on the StyleGAN neural network architecture, but incorporates a c...

In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI allows for improving models beyond the accuracy metric by, e.g., debugging the learned pattern and demystifying the AI's behavior. The widespread use of xAI brought new challenges. On the one hand, the number of publis...

Creating meaningful art is often viewed as a uniquely human endeavor. A human artist needs a combination of unique skills, understanding, and genuine intention to create artworks that evoke deep feelings and emotions. In this paper, we introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human pain...

When it comes to comprehending and analyzing multi-relational data, the semantics of relations are crucial. Polysemous relations between different types of entities, that represent multiple semantics, are common in real-world relational datasets represented by knowledge graphs. For numerous use cases, such as entity type classification, question an...

Named entity recognition (NER) is an important task that constitutes the basis for multiple downstream natural language processing tasks. Traditional machine learning approaches for NER rely on annotated corpora. However, these are only largely available for standard domains, e.g., news articles. Domain-specific NER often lacks annotated training d...

Digital libraries often contain many heterogeneous documents and cover a variety of topics. Computer generated virtual maps of such collections can help to get an overview and explore the data. The position of each document from the corpus on this virtual two-dimensional map is determined by its semantic similarity to the other documents. However,...

This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explai...

Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder–decoder deep neural network architecture that reduces costs further and models...

With the rise of research on toxic comment classification, more and more annotated datasets have been released. The wide variety of the task (different languages, different labeling processes and schemes) has led to a large amount of heterogeneous datasets that can be used for training and testing very specific settings. Despite recent efforts to c...

Patent examiners need to solve a complex information retrieval task when they assess the novelty and inventive step of claims made in a patent application. Given a claim, they search for prior art, which comprises all relevant publicly available information. This time-consuming task requires a deep understanding of the respective technical domain a...

Patent document collections are an immense source of knowledge for research and innovation communities worldwide. The rapid growth of the number of patent documents poses an enormous challenge for retrieving and analyzing information from this source in an effective manner. Based on deep learning methods for natural language processing, novel appro...

Knowledge graph embeddings that generate vector space representations of knowledge graph triples, have gained considerable popularity in past years. Several embedding models have been proposed that achieve state-of-the-art performance for the task of triple completion in knowledge graphs. Relying on the presumed semantic capabilities of the learned...

Visualisations are supposed to provide intuitive ways to explore large document collections. State-of-the-art approaches usually transform high-dimensional representations of documents into 2-dimensional vectors using dimensionality reduction algorithms. These vectors are then placed into a landscape hopefully retaining semantic information regardi...

Art-historic documents often contain multimodal data in terms of images of artworks and metadata, descriptions, or interpretations thereof. Most research efforts have focused either on image analysis or text analysis independently since the associations between the two modes are usually lost during digitization. In this work, we focus on the task o...

In our modern society, almost all events, processes, and decisions in a corporation are documented by internal written communication, legal filings, or business and financial news. The valuable knowledge in such collections is not directly accessible by computers as they mostly consist of unstructured text. This chapter provides an overview of corp...

In recent years, computer vision algorithms based on machine learning have seen rapid development. In the past, research mostly focused on solving computer vision problems such as image classification or object detection on images displaying natural scenes. Nowadays other fields such as the field of cultural heritage, where an abundance of data is...

Patent examiners need to solve a complex information retrieval task when they assess the novelty and inventive step of claims made in a patent application. Given a claim, they search for prior art, which comprises all relevant publicly available information. This time-consuming task requires a deep understanding of the respective technical domain a...

This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explai...

Many online news platforms provide comment sections for reader discussions below articles. While users of these platforms often read comments, only a minority of them regularly write comments. To encourage and foster more frequent engagement, we study the task of personalized recommendation of reader discussions to users. We present a neural networ...

Many online news platforms provide comment sections for reader discussions below articles. While users of these platforms often read comments, only a minority of them regularly write comments. To encourage and foster more frequent engagement, we study the task of personalized recommendation of reader discussions to users. We present a neural networ...

Convolutional neural networks (CNN) are getting more and more complex, needing enormous computing resources and energy. In this paper, we propose methods for conditional computation in the context of image classification that allows a CNN to dynamically use its channels and layers conditioned on the input. To this end, we combine light-weight gatin...

Language is dynamic and constantly evolving: both the usage context and the meaning of words change over time. Identifying words that acquired new meanings and the point in time at which new word senses emerged is elementary for word sense disambiguation and entity linking in historical texts. For example, cloud once stood mostly for the weather ph...

Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order, which neglects that some of them are more relevant to users a...

Modern transformer-based models with hundreds of millions of parameters, such as BERT, achieve impressive results at text classification tasks. This also holds for aggression identification and offensive language detection, where they consistently outperform less complex models, such as decision trees. While the complex models fit training data wel...

Many online discussion platforms use a content moderation process, where human moderators check user comments for offensive language and other rule violations. It is the moderator's decision which comments to remove from the platform because of violations and which ones to keep. Research so far focused on automating this decision process in the for...

Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order , which neglects that some of them are more relevant to users...

Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via their digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic t...

Comment sections of online news platforms are an essential space to express opinions and discuss political topics. In contrast to other online posts, news discussions are related to particular news articles, comments refer to each other, and individual conversations emerge. However, the misuse by spammers, haters, and trolls makes costly content mo...

In today’s modern society and global economy, decision making processes are increasingly supported by data. Especially in financial businesses it is essential to know about how the players in our global or national market are connected. In this work we compare different approaches for creating company relationship graphs. In our evaluation we see s...

Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such a...

Pre-training language representations on large text corpora, for example, with BERT, has recently shown to achieve impressive performance at a variety of downstream NLP tasks. So far, applying BERT to offensive language identification for German-language texts failed due to the lack of pre-trained, German-language models. In this paper, we fine-tun...

Named entity recognition (NER) plays an important role in many information retrieval tasks, including automatic knowledge graph construction. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as art historical archives, the recognitio...

Named entity recognition (NER) plays an important role in many information retrieval tasks, including automatic knowledge graph construction. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as art historical archives, the recognitio...

Accessible and reusable datasets are a necessity to accomplish repeatable research. This requirement poses a problem particularly for web science, since scraped data comes in various formats and can change due to the dynamic character of the web. Further, usage of web data is typically restricted by copyright-protection or privacy regulations, whic...

Purpose Patent offices and other stakeholders in the patent domain need to classify patent applications according to a standardized classification scheme. The purpose of this paper is to examine the novelty of an application it can then be compared to previously granted patents in the same class. Automatic classification would be highly beneficial,...

The large amount of heterogeneous data in these email corpora renders experts' investigations by hand infeasible. Auditors or journalists, e.g., who are looking for irregular or inappropriate content or suspicious patterns, are in desperate need for computer-aided exploration tools to support their investigations. We present our Beacon system for t...

Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task's challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dat...

Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task's challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dat...

Content-based recommendation of books and other media is usually based on semantic similarity measures. While metadata can be compared easily, measuring the semantic similarity of narrative literature is challenging. Keyword-based approaches are biased to retrieve books of the same series or do not retrieve any results at all in sparser libraries....

Social media platforms receive massive amounts of user-generated content that may include offensive text messages. In the context of the GermEval task 2018, we propose an approach for fine-grained classification of offensive language. Our approach comprises a Naive Bayes classifier, a neural network, and a rule-based approach that categorize tweets...

A patent examiner needs domain-specific knowledge to classify a patent application according to its field of invention. Standardized classification schemes help to compare a patent application to previously granted patents and thereby check its novelty. Due to the large volume of patents, automatic patent classification would be highly beneficial t...

Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics. Today, moderators ensure respectful and informative discussions by deleting not only insults, defamation, and hate speech, but also unverifiable facts. This process has to be transparent and comprehensive in order to keep the communit...

Social media platforms allow users to share and discuss their opinions online. However, a minor- ity of user posts is aggressive, thereby hinders respectful discussion, and — at an extreme level — is liable to prosecution. The automatic identification of such harmful posts is important, be- cause it can support the costly manual moderation of onlin...

Nowadays, more and more large datasets exhibit an intrinsic graph structure. While there exist special graph databases to handle ever increasing amounts of nodes and edges, visualising this data becomes infeasible quickly with growing data. In addition, looking at its structure is not sufficient to get an overview of a graph dataset. Indeed, visual...

The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention o...

The distributional hypothesis states that similar words tend to have similar contexts in which they occur. Word embedding models exploit this hypothesis by learning word vectors based on the local context of words. Probabilistic topic models on the other hand utilize word co-occurrences across documents to identify topically related words. Due to t...

Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such a...

Massive Open Online Courses (MOOCs) have introduced a new form of education. With thousands of participants per course, lecturers are confronted with new challenges in the teaching process. In this paper , we describe how we conducted an introductory information retrieval course for participants from all ages and educational backgrounds. We analyze...

Research results manifest in large corpora of patents and scientific papers. However, both corpora lack a consistent taxonomy and references across different document types are sparse. Therefore, and because of contrastive, domain-specific language, recommending similar papers for a given patent (or vice versa) is challenging.

RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA onl...

Evaluating the credibility of a company is an important and complex task for financial experts. When estimating the risk associated with a potential asset, analysts rely on large amounts of data from a variety of different sources, such as newspapers, stock market trends, and bank statements. Finding relevant information in mostly unstructured data i...

During the last presidential election in the United States, Twitter drew a lot of attention. This is because many leading persons and organizations, such as U.S. president Donald J. Trump, showed a strong affection to this medium. In this work we neglect the political contents and opinions shared on Twitter and focus on the question: Can we determi...

Das Hasso-Plattner-Institut (HPI) ist ein privat finanziertes Institut an der Universität Potsdam. Stifter ist Professor Hasso Plattner, Mitgründer und Aufsichtsratsvorsitzender des Software-Konzerns SAP. Das Fachgebiet Informationssysteme, das von Prof. Dr. Felix Naumann geleitet wird, beschäftigt sich mit dem effizienten und effektiven Umgang mit...

RNA-binding proteins (RBPs) play important roles in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. To which extent RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders which produce informative motifs and simultane...

The Hasso Plattner Institute (HPI) is a private computer science institute funded by the eponymous SAP co-founder. It is affiliated with the University of Potsdam in Germany and is dedicated to research and teaching, awarding B.Sc., M.Sc., and Ph.D. degrees. The Information Systems group was founded in 2006, currently has around ten Ph.D. students...

Community based question-and-answer (Q&A) sites rely on well-posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the c...

Massive Open Online Courses (MOOCs) have grown in reach and importance over the last few years, enabling a vast userbase to enroll in online courses. Besides watching videos, user participate in discussion forums to further their understanding of the course material. As in other community-based question-answering communities, in many MOOC forums a...

Recommendation algorithms typically work by suggesting items that are similar to the ones that a user likes, or items that similar users like. We propose a content-based recommendation technique with the focus on serendipity of news recommendations. Serendipitous recommendations have the characteristic of being unexpected yet fortunate and interest...

Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Be...

Twitter has become a prime source for disseminating news and opinions. However, the length of tweets prohibits detailed descriptions; instead, tweets sometimes contain URLs that link to detailed news articles. In this paper, we devise generic techniques for recommending tweets for any given news article. To evaluate and compare the different techni...

E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything....

Microblogging platforms make it easy for users to share information through the publication of short personal messages. However, users are not only interested in sharing, but even more so in consuming information. As a result, they are confronted with new challenges when it comes to retrieving information on microblogging platforms. In this paper w...

The availability of large volumes of granted patents and applications, all publicly available on the Web, enables the use of sophisticated text mining and information retrieval methods to facilitate access and analysis of patents. In this paper we investigate techniques to automatically recommend patents given a query patent. This task is critical...

Topic modeling has gained a lot of popularity as a means for identifying and describing the topical structure of textual documents and whole corpora. There are, however, many document collections such as qualitative studies in the digital humanities that cannot easily benefit from this technology. The limited size of those corpora leads to poor qua...

Sentiment lexica are useful for analyzing opinions in Web collections, for domain-dependent sentiment classification, and as sub-components of recommender systems. In this paper, we present a strategy for automatically generating topic-dependent lexica from large corpora of review articles by exploiting accompanying user ratings. Our approach combi...

Search engine results are often biased towards a certain aspect of a query or towards a certain meaning for ambiguous query terms. Diversification of search results offers a way to supply the user with a better balanced result set increasing the probability that a user finds at least one document suiting her information need. In this paper, we pres...

The Web is a very democratic medium of communication allowing everyone to express his or her opinion about any type of topic. This multitude of voices makes it more and more important to detect bias and help Internet users understand the background of information sources. Political bias of Web sites, articles, or blog posts is hard to identify stra...

With the recent rise of Open Government Data, innovative technologies are required to leverage this new wealth of information. Therefore, we present a system combining several information processing techniques with micro-blogging services to demonstrate how this data can be put to use in order to increase transparency in political processes, and en...