Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character

Amnestic Forgery: an Ontology of Conceptual Metaphors

This paper presents Amnestic Forgery, an ontology for metaphor semantics, based on MetaNet, which is inspired by the theory of Conceptual Metaphor. Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology design framework to deal with both semiotic and referential aspects of frames, roles, mappings, and eventually blending. The description of the resource is supplied by a discussion of its applications, with examples taken from metaphor generation, and the referential problems of metaphoric mappings. Both schema and data are available from the Framester SPARQL endpoint

Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings

Structure perception is a fundamental aspect of music cognition in humans. Historically, the hierarchical organization of music into structures served as a narrative device for conveying meaning, creating expectancy, and evoking emotions in the listener. Thereby, musical structures play an essential role in music composition, as they shape the musical discourse through which the composer organises his ideas. In this paper, we present a novel music segmentation method, pitchclass2vec, based on symbolic chord annotations, which are embedded into continuous vector representations using both natural language processing techniques and custom-made encodings. Our algorithm is based on long-short term memory (LSTM) neural network and outperforms the state-of-the-art techniques based on symbolic chord annotations in the field

Seeing the Intangible: Surveying Automatic High-Level Visual Understanding from Still Images

The field of Computer Vision (CV) was born with the single grand goal of complete image understanding: providing a complete semantic interpretation of an input image. What exactly this goal entails is not immediately straightforward, but theoretical hierarchies of visual understanding point towards a top level of full semantics, within which sits the most complex and subjective information humans can detect from visual data. In particular, non-concrete concepts including emotions, social values and ideologies seem to be protagonists of this "high-level" visual semantic understanding. While such "abstract concepts" are critical tools for image management and retrieval, their automatic recognition is still a challenge, exactly because they rest at the top of the "semantic pyramid": the well-known semantic gap problem is worsened given their lack of unique perceptual referents, and their reliance on more unspecific features than concrete concepts. Given that there seems to be very scarce explicit work within CV on the task of abstract social concept (ASC) detection, and that many recent works seem to discuss similar non-concrete entities by using different terminology, in this survey we provide a systematic review of CV work that explicitly or implicitly approaches the problem of abstract (specifically social) concept detection from still images. Specifically, this survey performs and provides: (1) A study and clustering of high level visual understanding semantic elements from a multidisciplinary perspective (computer science, visual studies, and cognitive perspectives); (2) A study and clustering of high level visual understanding computer vision tasks dealing with the identified semantic elements, so as to identify current CV work that implicitly deals with AC detection

Linked Metaphors

International audienceThe poster summarizes Amnestic Forgery, an ontology for metaphor semantics, based on MetaNet and Framester factual-linguistic linked data. An example of metaphor generation based on linked metaphors is shown

Semantic Role Labeling for Knowledge Graph Extraction from Text

This paper introduces TakeFive, a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph. It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalizes the results as a knowledge graph. This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource. We tested our method on the WSJ section of the Peen Treebank annotated with VerbNet and PropBank labels and on the Brown corpus. The evaluation has been performed according to the CoNLL Shared Task on Joint Parsing of Syntactic and Semantic Dependencies. The obtained precision, recall, and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM, and FRED. We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall, and F1 measure

MET Gene Amplification and MET Receptor Activation Are Not Sufficient to Predict Efficacy of Combined MET and EGFR Inhibitors in EGFR TKI-Resistant NSCLC Cells

Epidermal growth factor receptor (EGFR), member of the human epidermal growth factor receptor (HER) family, plays a critical role in regulating multiple cellular processes including proliferation, differentiation, cell migration and cell survival. Deregulation of the EGFR signaling has been found to be associated with the development of a variety of human malignancies including lung, breast, and ovarian cancers, making inhibition of EGFR the most promising molecular targeted therapy developed in the past decade against cancer. Human non small cell lung cancers (NSCLC) with activating mutations in the EGFR gene frequently experience significant tumor regression when treated with EGFR tyrosine kinase inhibitors (TKIs), although acquired resistance invariably develops. Resistance to TKI treatments has been associated to secondary mutations in the EGFR gene or to activation of additional bypass signaling pathways including the ones mediated by receptor tyrosine kinases, Fas receptor and NF-kB. In more than 30-40% of cases, however, the mechanisms underpinning drug-resistance are still unknown. The establishment of cellular and mouse models can facilitate the unveiling of mechanisms leading to drug-resistance and the development or validation of novel therapeutic strategies aimed at overcoming resistance and enhancing outcomes in NSCLC patients. Here we describe the establishment and characterization of EGFR TKI-resistant NSCLC cell lines and a pilot study on the effects of a combined MET and EGFR inhibitors treatment. The characterization of the erlotinib-resistant cell lines confirmed the association of EGFR TKI resistance with loss of EGFR gene amplification and/or AXL overexpression and/or MET gene amplification and MET receptor activation. These cellular models can be instrumental to further investigate the signaling pathways associated to EGFR TKI-resistance. Finally the drugs combination pilot study shows that MET gene amplification and MET receptor activation are not sufficient to predict a positive response of NSCLC cells to a cocktail of MET and EGFR inhibitors and highlights the importance of identifying more reliable biomarkers to predict the efficacy of treatments in NSCLC patients resistant to EGFR TKI

Pattern-based design applied to cultural heritage knowledge graphs

Ontology Design Patterns (ODPs) have become an established and recognised practice for guaranteeing good quality ontology engineering. There are several ODP repositories where ODPs are shared as well as ontology design methodologies recommending their reuse. Performing rigorous testing is recommended as well for supporting ontology maintenance and validating the resulting resource against its motivating requirements. Nevertheless, it is less than straightforward to find guidelines on how to apply such methodologies for developing domain-specific knowledge graphs. ArCo is the knowledge graph of Italian Cultural Heritage and has been developed by using eXtreme Design (XD), an ODP- and test-driven methodology. During its development, XD has been adapted to the need of the CH domain e.g. gathering requirements from an open, diverse community of consumers, a new ODP has been defined and many have been specialised to address specific CH requirements. This paper presents ArCo and describes how to apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs. Relevant contributions also include a novel web tool for supporting unit-testing of knowledge graphs, a rigorous evaluation of ArCo, and a discussion of methodological lessons learned during ArCo development