Designing Semantic Kernels as Implicit Superconcept Expansions

Recently, there has been an increased interest in the exploitation of background knowledge in the context of text mining tasks, especially text classification. At the same time, kernel-based learning algorithms like Support Vector Machines have become a dominant paradigm in the text mining community. Amongst other reasons, this is also due to their capability to achieve more accurate learning results by replacing standard linear kernel (bag-of-words) with customized kernel functions which incorporate additional apriori knowledge. In this paper we propose a new approach to the design of ‘semantic smoothing kernels’ by means of an implicit superconcept expansion using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations where (i) training data is scarce or (ii) the bag-ofwords representation is too sparse to build stable models when using the linear kernel

Stromal cell protein kinase C-β inhibition enhances chemosensitivity in B cell malignancies and overcomes drug resistance.

Overcoming drug resistance remains a key challenge to cure patients with acute and chronic B cell malignancies. Here, we describe a stromal cell-autonomous signaling pathway, which contributes to drug resistance of malignant B cells. We show that protein kinase C (PKC)-β-dependent signals from bone marrow-derived stromal cells markedly decrease the efficacy of cytotoxic therapies. Conversely, small-molecule PKC-β inhibitors antagonize prosurvival signals from stromal cells and sensitize tumor cells to targeted and nontargeted chemotherapy, resulting in enhanced cytotoxicity and prolonged survival in vivo. Mechanistically, stromal PKC-β controls the expression of adhesion and matrix proteins, required for activation of phosphoinositide 3-kinases (PI3Ks) and the extracellular signal-regulated kinase (ERK)-mediated stabilization of B cell lymphoma-extra large (BCL-XL) in tumor cells. Central to the stroma-mediated drug resistance is the PKC-β-dependent activation of transcription factor EB, regulating lysosome biogenesis and plasma membrane integrity. Stroma-directed therapies, enabled by direct inhibition of PKC-β, enhance the effectiveness of many antileukemic therapies.This work was funded by Cancer Research UK (CRUK; C49940/A17480). I.R. is a senior CRUK fellow. M.S.S is supported by the DFG through SCHM2440/7-1 and CRC1243 (A12). L.G. & O.W. received funding from CWCUK (grant 14-169) and GOSHCC (grant V2617). A.E. receives research grants from the Austrian Science Fund (FWF; Transcan I2795-B28 to A.E. (FIRE-CLL), DACH grants I3282-B26 and I1299-B21 (FOR2036) and a grant from the Paracelsus Medical University (PMU Grant E-13/18/091-EGF). S.S. receives funding from the DFG (SFB1074 , project B1), relevant to this work

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-γ 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

A self organizing map for relation extraction from wikipedia using structured data representations

Abstract — In this work, we will report on the use of selforganizing maps (SOMs) in a clustering and relation extraction task. Specifically, we use the approach of self-organizing maps for structured data (SOMSDs) (i) for clustering music related articles from the free online encyclopedia Wikipedia and (ii) for extracting relations between the created clusters. We hereby rely on the bag-of-words similarity between individual articles on the one hand but additionally exploit the link structure between the articles on the other. I

UDDI project --- Universal Description, Discovery and Integration

Abstract. The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as classification in Question Answering (QA) scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, we propose a principled approach for jointly exploiting both types of information. We propose a new type of kernel, the Semantic Syntactic Tree Kernel (SSTK), which incorporates linguistic structures, e.g. syntactic dependencies, and semantic background knowledge, e.g. term similarity based on WordNet, to automatically learn question categories in QA. We show the power of this approach in a series of experiments with a well known Question Classification dataset.

Intelligent Community Lifecycle Support

Abstract: Knowledge sharing in communities has attracted much attention in the field of knowledge management in research and practice. In this paper we outline a view where the community lifecycle is supported at different stages. The central component of our framework is the community ontology SWRC+COIN that describes the typical structure of communities. We exemplarily show how communities in the academic domain can be detected automatically by means of analyzing information flow in a bibliographic Peer-to-Peer system and how the instantiated community knowledge base can be exploited to support cooperative work in the communities

2006): Learning Ontologies to Improve Text Clustering and Classification

Abstract. Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones.