Weighted Pseudo-distances for Categorization in Semantic Hierarchies

2005 ◽  
pp. 381-395 ◽  
Author(s):  
Cliff A. Joslyn ◽  
William J. Bruno
Keyword(s):  
Semantic Hierarchies

scholarly journals Image Collection Summarization Method Based on Semantic Hierarchies

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 209-228
Author(s):  
Zahra Riahi Samani ◽  
Mohsen Ebrahimi Moghaddam
Keyword(s):  
Large Volume ◽  
Domain Ontology ◽  
Degree Of Freedom ◽  
Initial Image ◽  
New Techniques ◽  
Summarization Method ◽  
Semantic Hierarchies ◽  
High Level ◽  
Image Collection ◽  
Hierarchical Classifier

The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchical classifier was used, which finally mapped images to different nodes of the ontology. We made a compromise between the degree of freedom of the classifier and the goodness of the summarization method. The summarization was done using a group of high-level features that provided a semantic measurement of information in images. Experimental outcomes indicated that the introduced image collection summarization method outperformed the recent techniques for the summarization of image collections.

Ontologies' Closures Modeling by Formalisms of Semantic Hierarchies

2020 ◽  
Vol 11 (6) ◽  
pp. 349-360
Author(s):  
K. I. Kostenko ◽  
Keyword(s):  
Intelligent Systems ◽  
Life Cycles ◽  
Knowledge Flows ◽  
Knowledge Structures ◽  
Uniform Structure ◽  
Algebraic Structures ◽  
Knowledge Processing ◽  
Special Operations ◽  
Knowledge Presentation ◽  
Semantic Hierarchies

Schemes are studied for modeling the complex knowledge structures as synthesized from knowledge areas ontologies elements. These structures applications relate to knowledge life cycles and knowledge flows stages within intelligent systems. The format of semantic hierarchies is proposed as unified and universal for the synthesis of these structures. This allows replacing the general case of knowledge algebraic structures by special case of semantic hierarchies. Constructing the synthesized knowledge structures is performed by special operations knowledge alge­braic structures in any knowledge representation formalisms. These operations simulate fundamental mathematical systems functional aspects. They are adapted to the knowledge formalisms attributes. Attributes are explored within different knowledge areas. They associated generally with thinking operations and mind memory structure. Datasets (operations bases) of synthesis processes operations are constructed as special classes of knowledge with a uniform structure. Ontologies for considered knowledge areas are used as source of such bases constructing. Ontologies closures are defined as sets of knowledge that may be constructed off ontologies elements by synthesis operations. They determine the ontologies expressive capabilities. The constructs and operations over ontologies, that proposed at descriptive logics, can be modelled by knowledge complete structural representations, adopted for semantic hierarchies formalisms. Such formalisms are convenient for simulating knowledge presentation and processing. They form foundation for constructing intelligent systems abstract and applied models.. The possibility is proved for trans­ferring the knowledge properties and knowledge processing schemes at semantic hierarchies to the general case of knowledge algebraic structures. The schemes for modeling the ontological constructions as semantic hierarchies are given. This proves possibility of applying such formalisms as the basis for modeling synthesis processes in ontolo­gies. Such schemes allow constructing the ontologies closures as generated by knowledge-processing operations sequences. Last fact means possibility for formalisms of semantic hierarchies to be uniform foundation of modelling the knowledge flows and knowledge transforming processes by intelligent systems.

scholarly journals Retrieval of words from subordinate and superordinate categories in semantic hierarchies

1970 ◽  
Vol 21 (4) ◽  
pp. 235-236 ◽  
Author(s):  
Elizabeth F. Loftus ◽  
Jonathan L. Freedman ◽  
Geoffrey R. Loftus
Keyword(s):  
Semantic Hierarchies

scholarly journals Learning Semantic Hierarchies: A Continuous Vector Space Approach

2015 ◽  
Vol 23 (3) ◽  
pp. 461-471 ◽  
Author(s):  
Ruiji Fu ◽  
Jiang Guo ◽  
Bing Qin ◽  
Wanxiang Che ◽  
Haifeng Wang ◽  
...  
Keyword(s):  
Vector Space ◽  
Continuous Vector ◽  
Semantic Hierarchies ◽  
Space Approach

A Neurocomputational Approach to Prepositional Phrase Attachment Ambiguity Resolution

2012 ◽  
Vol 24 (7) ◽  
pp. 1906-1925
Author(s):  
Kailash Nadh ◽  
Christian Huyck
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Ambiguity Resolution ◽  
Syntactic Structure ◽  
Machine Learning Algorithms ◽  
Prepositional Phrase ◽  
Large Network ◽  
Integrate And Fire ◽  
Semantic Hierarchies

A neurocomputational model based on emergent massively overlapping neural cell assemblies (CAs) for resolving prepositional phrase (PP) attachment ambiguity is described. PP attachment ambiguity is a well-studied task in natural language processing and is a case where semantics is used to determine the syntactic structure. A large network of biologically plausible fatiguing leaky integrate-and-fire neurons is trained with semantic hierarchies (obtained from WordNet) on sentences with PP attachment ambiguity extracted from the Penn Treebank corpus. During training, overlapping CAs representing semantic similarities between the component words of the ambiguous sentences emerge and then act as categorizers for novel input. The resulting average resolution accuracy of 84.56% is on par with known machine learning algorithms.

dom as a syntax-pragmatics interface marker

2019 ◽  
Vol 42 (1) ◽  
pp. 56-81
Author(s):  
Marta Khouja
Keyword(s):  
Information Structure ◽  
Semantic Hierarchies ◽  
Shed Light

Abstract Building on the display of dom in Catalan and focusing on the Balearic variety, this paper explores this phenomenon arguing for a discourse-driven marking, showing that the assumption that semantic hierarchies as crucial triggers for dom cannot be assumed anymore. We aim to present some ideas to address the correlation between prepositional markings and peripheral positions and to provide arguments for a syntax-pragmatics approach to dom in Clitic Dislocation. Our data shed light on the link between information structure – in particular, anaphoricity- and marked objects. This analysis would also account for other markers (i.e. de) available as a mechanism for signalling the same [+anaphoric] feature.

Learning Semantic Hierarchies via Word Embeddings

2014 ◽  
Author(s):  
Ruiji Fu ◽  
Jiang Guo ◽  
Bing Qin ◽  
Wanxiang Che ◽  
Haifeng Wang ◽  
...  
Keyword(s):  
Word Embeddings ◽  
Semantic Hierarchies
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