Word graphs: The third set

This is the third paper in a series of natural language processing in term of knowledge graphs. A word is a basic unit in natural language processing. This is why we study word graphs. Word graphs were already built for prepositions and adwords (including adjectives, adverbs and Chinese quantity words) in two other papers. In this paper, we propose the concept of the logic word and classify logic words into groups in terms of semantics and the way they are used in describing reasoning processes. A start is made with the building of the lexicon of logic words in terms of knowledge graphs

Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling

In this paper, we present hierarchical relationbased latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents. In contrast to traditional topic models, hrLDA relies on noun phrases instead of unigrams, considers syntax and document structures, and enriches topic hierarchies with topic relations. Through a series of experiments, we demonstrate the superiority of hrLDA over existing topic models, especially for building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the settings of noisy data sets, which are likely to occur in many practical scenarios. Our ontology evaluation results show that ontologies extracted from hrLDA are very competitive with the ontologies created by domain experts

Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology

Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.Comment: 11 pages, to appear at ACM MM'1

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism

One-, Two-dimensional Model of Personal Identity and Personal Being, as an Accumulator of “Zombies” Ontology (Regressive Tendency of Combining a Living Body and a Corpse Within a Semantic Field of the “Body” Concept in 19 European Languages and in All Hie

The aim of research is revealing the correlation of one-, two-dimensional models of personal identity and the ontology of a dead body without signs of consciousness (“zombies”). Research methods are hermeneutic and systemic structural. The author pays special attention to the phenomena of “philosophical, social, soulless zombies”. It is specified that such concepts as anima (Latin), fren (Greek), 灵魂 (Chinese), 精神 (Chinese), आत्मन (atman) (Sanskrit), बुद्धि (Buddhi) (Sanskrit), رُوحٌ (ruh) (Arabic), הנשמה (Hebrew); רוח (Hebrew), ψϋχ'ή (psyche) (Greek), spirit (English), esprit” (French), gemüt (German), geist (German), Körper (German),body (English),corpus (Latin), Le corps (French), chair (French) contribute most to the deformation of personal identity. Both the transcendental form of identity (spirit, soul) and material (human body) are subject to deformation. Using the example of the substitution of the “god of the morning” (Lucifer) for the “devil” (Satan) within the Latin language, the practice of influencing the collective consciousness of people of the transformational power of letters-symbols relating to the structure of the alphabetical plan of two-dimensional dimension (as understood by A. Sviridov). It is revealed that the concepts of transformation of personal identity within 19 European languages and all hieroglyphic languages are created today by critical masses of people whose consciousness is congruent with the phenomenon of "social zombie"