Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Reliability is a well-known concern in the field of personalization technologies. We propose the extension of an ontology-based retrieval system with semantic-based personalization techniques, upon which automatic mechanisms are devised that dynamically gauge the degree of personalization, so as to benefit from adaptivity but yet reduce the risk of obtrusiveness and loss of user control. On the basis of a common domain ontology KB, the personalization framework represents, captures and exploits user preferences to bias search results towards personal user interests. Upon this, the intensity of personalization is automatically increased or decreased according to an assessment of the imprecision contained in user requests and system responses before personalization is applied

Question Answering on Scholarly Knowledge Graphs

Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variety of different questions asked on tabular data in articles. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions. This dataset is used as a benchmark for our system and can be reused by others. Additionally, JarvisQA is evaluated on two datasets against other baselines and shows an improvement of two to three folds in performance compared to related methods.Comment: Pre-print for TPDL2020 accepted full paper, 14 page

Using graph-kernels to represent semantic information in text classification

Most text classification systems use bag-of-words represen- tation of documents to find the classification target function. Linguistic structures such as morphology, syntax and semantic are completely ne- glected in the learning process. This paper proposes a new document representation that, while includ- ing its context independent sentence meaning, is able to be used by a structured kernel function, namely the direct product kernel. The proposal is evaluated using a dataset of articles from a Portuguese daily newspaper and classifiers are built using the SVM algorithm. The results show that this structured representation, while only partially de- scribing document’s significance has the same discriminative power over classes as the traditional bag-of-words approach

Viral transduction of primary human lymphoma B cells reveals mechanisms of NOTCH-mediated immune escape

Hotspot mutations in the PEST-domain of NOTCH1 and NOTCH2 are recurrently identified in B cell malignancies. To address how NOTCH-mutations contribute to a dismal prognosis, we have generated isogenic primary human tumor cells from patients with Chronic Lymphocytic Leukemia (CLL) and Mantle Cell Lymphoma (MCL), differing only in their expression of the intracellular domain (ICD) of NOTCH1 or NOTCH2. Our data demonstrate that both NOTCH-paralogs facilitate immune-escape of malignant B cells by up-regulating PD-L1, partly dependent on autocrine interferon-? signaling. In addition, NOTCH-activation causes silencing of the entire HLA-class II locus via epigenetic regulation of the transcriptional co-activator CIITA. Notably, while NOTCH1 and NOTCH2 govern similar transcriptional programs, disease-specific differences in their expression levels can favor paralog-specific selection. Importantly, NOTCH-ICD also strongly down-regulates the expression of CD19, possibly limiting the effectiveness of immune-therapies. These NOTCH-mediated immune escape mechanisms are associated with the expansion of exhausted CD8+ T cells in vivo.© 2022. The Author(s)

Multi-platform profiling characterizes molecular subgroups and resistance networks in chronic lymphocytic leukemia

Knowledge of the genomic landscape of chronic lymphocytic leukemia (CLL) grows increasingly detailed, providing challenges in contextualizing the accumulated information. To define the underlying networks, we here perform a multi-platform molecular characterization. We identify major subgroups characterized by genomic instability (GI) or activation of epithelial-mesenchymal-transition (EMT)-like programs, which subdivide into non-inflammatory and inflammatory subtypes. GI CLL exhibit disruption of genome integrity, DNA-damage response and are associated with mutagenesis mediated through activation-induced cytidine deaminase or defective mismatch repair. TP53 wild-type and mutated/deleted cases constitute a transcriptionally uniform entity in GI CLL and show similarly poor progression-free survival at relapse. EMT-like CLL exhibit high genomic stability, reduced benefit from the addition of rituximab and EMT-like differentiation is inhibited by induction of DNA damage. This work extends the perspective on CLL biology and risk categories in TP53 wild-type CLL. Furthermore, molecular targets identified within each subgroup provide opportunities for new treatment approaches

Ontology evolution with Evolva

Ontology evolution is a painstaking and time-consuming process, especially in information rich and dynamic domains. While ontology evolution refers both to the adaptation of ontologies (e.g., through additions or updates possibly discovered from external data sources) and the management of these changes, no existing tools offer both functionalities. The Evolva framework aims to be a blueprint for a comprehensive ontology evolution tool that would cover both tasks. Additionally, Evolva proposes the use of background knowledge sources to reduce user involvement in the ontology adaptation step. This demo focuses on the initial, concrete implementation of our framework