TOWARDS A SEMANTIC NETWORK ENRICHED WITH A VARIETY OF SEMANTIC RELATIONS

AbstractThe ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.SignificanceRelations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.

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Distributed Code for Semantic Relations Predicts Neural Similarity during Analogical Reasoning

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01620 ◽

2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Jeffrey N. Chiang ◽

Yujia Peng ◽

Hongjing Lu ◽

Keith J. Holyoak ◽

Martin M. Monti

Keyword(s):

Analogical Reasoning ◽

Computational Models ◽

Semantic Network ◽

Semantic Relations ◽

Human Cognition ◽

Sequential Presentation ◽

Frontoparietal Network ◽

Neural Activities ◽

Core Set ◽

Superior Parietal Cortex

The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coarsely coded as links in a semantic network or finely coded as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations are yet to be empirically established. Using sequential presentation of verbal analogies, we compared neural activities in making analogy judgments with predictions derived from alternative computational models of relational dissimilarity to adjudicate among rival accounts of how semantic relations are coded and compared in the brain. We found that a frontoparietal network encodes the three relation types included in the design. A computational model based on semantic relations coded as distributed representations over a pool of abstract relations predicted neural activities for individual relations within the left superior parietal cortex and for second-order comparisons of relations within a broader left-lateralized network.

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STRUCTURAL ANALYSIS OF SEMANTIC RELATIONS REGARDING INTEGRATION AND ASSOCIATION OF SEMANTIC NETWORK IN VOCBENCH AS AN AGRICULTURAL ONTOLOGY

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v6.i5.2019.371 ◽

2020 ◽

Vol 6 (5) ◽

pp. 41-57

Author(s):

Maziar Amirhosseini ◽

Juhana Salim

Keyword(s):

Structural Analysis ◽

High Frequency ◽

Semantic Network ◽

Sampling Technique ◽

Semantic Relations ◽

Structural Domains ◽

Data Resource ◽

Taxonomic Relations ◽

United Nation ◽

Agricultural Organization

The purpose of this article is to analyze semantic relations based on graph-independent structural analysis in VocBench. The mix-method of deductive and inductive approach is adapted in operating the research methodology, especially for data collection. The research data are structural domains of semantic relations in ontologies. The data resource is the authoritative agricultural ontology, VocBench, that has been originated by Food and Agricultural organization (FAO), United Nation. VocBench includes around 40000 concepts. The sample size is around 1500 concepts. Sampling technique used is the stratified random sampling. The data analysis results are employed in the SPSS and Excel software using descriptive and proportional analysis. The research results reveal that the taxonomic relations cover a wide area in VocBench. Moreover, the overloading was not seen in the usage of non-taxonomic relations. The high frequency in the usage of the semantic relations’ output might be implied the possibility of the width (i.e., exhaustivity) in semantic network in VocBench.

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The semantic network, lexical access, and reading comprehension in monolingual and bilingual children: An individual differences study

Applied Psycholinguistics ◽

10.1017/s0142716417000224 ◽

2017 ◽

Vol 39 (1) ◽

pp. 225-256 ◽

Cited By ~ 8

Author(s):

TESSA SPÄTGENS ◽

ROB SCHOONEN

Keyword(s):

Reading Comprehension ◽

Individual Differences ◽

Semantic Priming ◽

Lexical Access ◽

Semantic Network ◽

Semantic Relations ◽

Reading Scores ◽

Bilingual Children ◽

Decision Task ◽

Vocabulary Size

ABSTRACTUsing a semantic priming experiment, the influence of lexical access and knowledge of semantic relations on reading comprehension was studied in Dutch monolingual and bilingual minority children. Both context-independent semantic relations in the form of category coordinates and context-dependent semantic relations involving concepts that co-occur in certain contexts were tested in an auditory animacy decision task, along with lexical access. Reading comprehension and the control variables vocabulary size, decoding skill, and mental processing speed were tested by means of standardized tasks. Mixed-effects modeling was used to obtain individual priming scores and to study the effect of individual differences in the various predictor variables on the reading scores. Semantic priming was observed for the coordinate pairs but not the context-dependently related pairs, and neither context-independent priming nor lexical access predicted reading comprehension. Only vocabulary size significantly contributed to the reading scores, emphasizing the importance of the number of words known for reading comprehension. Finally, the results show that the monolingual and bilingual children perform similarly on all measures, suggesting that in the current Dutch context, language status may not be highly predictive of vocabulary knowledge and reading comprehension skill.

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SEMANTIC NETWORK OF THE WORD ASSOCIATION IN THE FIELD OF LAW

LITERA ◽

10.21831/ltr.v18i3.26513 ◽

2019 ◽

Vol 18 (3) ◽

pp. 430-446

Author(s):

Arti Prihatini

Keyword(s):

Word Association ◽

Semantic Network ◽

Case Study Research ◽

Semantic Networks ◽

Point Of View ◽

Semantic Relations ◽

Response Word ◽

Descriptive Case Study ◽

Word Categorization

Word association can be utilized for identifying semantic network and describing point of view and prior knowledge of someone. This study aims to discuss two focuses, (1) the categorization of response word and (2) language factors that form semantic networks from the response words of word association in the field of law. This research uses a qualitative approach which is a type of descriptive case study research. Data collection held by using a word association questionaire. Based on the word categorization, the results showed that the stimulus words in the form of nouns were the most responded by nouns, verbs were the most responded by nouns, while the adjectives were responded more by an adjectives. Overall, responses in the form of nouns are 64.71%, verbs are 17.70%, adjectives are 17.58%. The responses in the form of nouns are related to the semantic relations of argument and predication, while the adjective response tends to be related to other adjectives in the unity of the semantic network. The response words form semantic network based on language factors, namely (1) lexical factors that consist of meaning relationship, connotation of meaning, concreteness and abstractness and (2) grammatical factors that consist of syntagmatic-paradigmatic and predication-argument relationship. Keywords: semantic network, word association, mental lexicon

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