In recent decades, Recommender Systems (RS) have undergone significant advancements, particularly in popular domains like movies, music, and product recommendations. Yet, progress has been notably slower in leveraging these systems for personal information management and knowledge assistance. In addition to challenges that complicate the adoption of RS in this domain (such as privacy concerns, heterogeneous recommendation items, and frequent context switching), a significant barrier to progress in this area has been the absence of a standardized benchmark for researchers to evaluate their approaches. In response to this gap, this paper presents a benchmark built upon a publicly available dataset of Real-Life Knowledge Work in Context (RLKWiC). This benchmark focuses on evaluating context-based entity recommendation, a use case for leveraging RS to support knowledge workers in their daily digital tasks. By providing this benchmark, it is aimed to facilitate and accelerate research efforts in enhancing personal knowledge assistance through RS.

Information overload resulting from the ever faster-growing mass of digital data makes knowledge work more and more complex. Being able to not get distracted and focus on what is currently relevant consumes valuable cognitive resources. Support by intelligent assistance software might alleviate this problem. We report two experiments that addressed this challenge by examining how context-based assistance may provide more available cognitive resources. Experiment 1 focused on work within a single context. Results indicate that external relevance classification can improve memory for content classified as currently more relevant. Experiment 2 focused on switching between two different contexts and shows that cognitive performance after context switches can be enhanced by context-specific structuring and saving a previous task status. Taken together, these results clearly demonstrate that automatic external information structuring by intelligent assistance software can protect knowledge workers from information overload by lightening their cognitive load and, thus, help improve cognitive performance.

A growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems - just to name a few. To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast. In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task. Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied. Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines. In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER). As a first improvement step, we address word variations induced by inflection, for example present in the German language. Our approach is ontology-based and makes use of several language information sources like Wiktionary. We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour. Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing real-time capable runtime performance.

Abstract Inhibition is one of the core concepts in Cognitive Psychology. The idea of inhibitory mechanisms actively weakening representations in the human mind has inspired a great number of studies in various research domains. In contrast, Computer Science only recently has begun to consider concepts such as digital forgetting or suppression of irrelevant information to complement activation and highlighting of relevant information. Here, we review psychological research on inhibition in memory and link the gained insights with the current efforts and opportunities in Computer Science of incorporating inhibitory principles for reducing information overload and improving information retrieval in Personal Information Management. Four common aspects guide this review in both domains: (i) the purpose of inhibition to increase processing efficiency; (ii) its relation to activation; (iii) its links to contexts; (iv) its temporariness. In summary, the principle of suppressing information has been used by Computer Science for enhancing software in some ways already. Yet, we consider how novel methods for reducing information overload can be inspired by a more systematic involvement of the inhibition concept.

With the growing amount of content stored in personal and organizational information spaces, finding and re-finding documents becomes both more crucial and challenging. In this work, we propose an approach to reduce information overload in navigation by automatically focusing on important documents, adaptively to the tasks at hand. Based on the idea of managed forgetting, we present a ranking method, which unifies activity logs and semantic information about documents into a common framework to identify important documents to the user's current tasks. Our experiments on two real-world datasets, both collected from knowledge work activities in professional scenarios, show that our ranking approach outperforms the baseline methods for both subsequent access prediction and the effectiveness in ranking important documents. Furthermore, we implemented and demonstrated a system for decluttering information spaces as a proof of concept of our managed forgetting approach.

The World Wide Web is arguably the most important source of digital documents nowadays. These documents mainly consist of unstructured and semi-structured data comprising a wealth of information at the disposal of the DAR (Document Analysis and Recognition) community. Contextualization plays an important role in understanding the content of those documents. In this paper, we present an approach to early contextualization of textual data in HTML documents. It combines automatic as well as semiautomatic annotation of named entities with user interaction to support contextualization of the content of digital documents as early as in the authoring stage of their life cycle. We also present the results of an online experimental evaluation involving 120 human test subjects. They show that our approach successfully managed to produce semantically annotated versions of unstructured textual content, which contain reliable contextual information, thus facilitating the task of later document analysis stages.

MS Outlook is currently the most widespread e-mail client in corporate environments. However, e-mail management with MS Outlook is usually decoupled from enterprise processes, making it difficult to synchronize e-mails and attachments with currently running processes. In this paper, we introduce SmartOffice -- an extension for MS Outlook allowing the seamless integration of e-mail management with enterprise workflows, thus increasing the effectiveness of e-mail processing as well as coupling process-relevant e-mails and documents with the respective process instances. SmartOffice was integrated with a legacy system supporting the import management process of a large German retailer. We evaluated the SmartOffice integration in an empirical study in the context of the import process, using real data, and with the employees of the retailer's import office. We conducted a semi-structured interview, where one participant answered questions after solving three typical tasks and surveyed a group after a presentation and demonstration of SmartOffice's functionality. The results show that SmartOffice has high potential for being introduced in the process with high efficiency and high user acceptance. Although the number of participants was low, the results are considered very relevant from the perspective of the domain experts, since the study took place in an industrial setting.

To let a user know the user's personal trend on the user's computer, this paper proposes a method to explore and visualize one's personal trend on the premise that the Gnowsis semantic desktop is used. The proposed method classifies information items into four attention areas with a popularity change measure by using three indicators (direct attention score (DAS), indirect attention score (IAS) (using spreading activation) and popularity change (PC)). Comparing the proposed method with two existing tools (PIMO timeline and PIMO cloud) in the gnowsis, this paper describes the following advantages about the proposed method: (1) To deal with both touched concepts and neighbor concepts, (2) To consider frequency, (3) To consider trend, (4) To catch seven operations in the gnowsis. Moreover, from an experimental usage, which shows more than three subjects answered the proposed method provided good results in every question, this paper suggests the proposed method would be useful to show one's personal trend.