This paper presents an attractor neural network model of semantic fact retrieval , based on the original semantic network models of Collins and Quillian . In the context of modelling a semantic network , a distinction is made between associations linking together objects belonging to hierarchically related semantic classes , and associations linking together objects and their attributes . Keywords : knowledge representation ; standard model ; knowledge network ; semantic network ; parataxis network Abstract . The basic unit of knowledge representation is the knowledge component . There is a lack of agreement between information that is extracted from visual data and the text description . Ontologies are at the heart of all Semantic Web applications , representing domain concepts and relations in the form of a semantic network . In this study , we applied ontology to bridge the semantic gap by developing a prototype multi-modality ontology IRS based on sports news and by integrating it with DBpedia to enrich the knowledge base while overcoming the problem of semantic heterogeneity . Data were drawn from open-ended questionnaires ( n = 282 ) . Semantic network analysis was conducted using CATPAC , artificial neural network software . Results verify previous findings that students turn to the Internet before the library , but a deeper investigation revealed different preferences for study versus project-related research . Web scraping and text mining tools based on R language were used to create semantic network structures . Semantic network analysis revealed that the focus of the articles was on cancer . The analysis carried out using the Sankey diagram revealed that scientists who work in cancer are most frequently involved in the lung , however , the scientists who related with the lung are not focused on treatment , and heart . We expect to fill in the gaps of prior literatures which concluded that the blockbuster project has the same effects on all the other projects within same categories . As a major contribution of this paper , we obtain market structure insights from the creator generated contents based on a text-mining approach and combine it with semantic network analysis to assess the degree to which the latent competition causes cannibalize or spill-over effects inside the same category . Our study uses unique data set , compiled by Python based self-developed crawler . In phase 2 , these triples are then fed into a coherence network analysis process , using a genetic algorithm optimization . Finally , the highest-value sub networks are processed into a semantic network graph for display . Initial work on a well- known data set ( a Wikipedia article on Abraham Lincoln ) has shown excellent results without any specific tuning . If learner 's dictionaries absorb the findings in cognitive metaphor , revealing the cognitive nature of metaphor and the metaphorical motivation between multiple meanings , it will be greatly beneficial for learners to understand and manipulate words correctly . So , by comparing the definitions of metaphor , the uses of figurative labels and special columns in several leading English learner 's dictionaries , this paper finds that the cognitive view of metaphor is not sufficiently reflected in learner 's dictionaries and thus the author proposes that the cognitive view of metaphor be expressed in the definition of metaphor and that cognitive metaphor be better reflected by establishing conceptual metaphor modules like MED 's Metaphor Box , using explanation in brackets or figurative labels like " fig " or " M→ " to reveal metaphorical extension between senses or providing a semantic network graph for words with multiple senses . The relationships among concepts include collection types formed by combining two arbitrary types above concepts . ( 3 ) It researches knowledge graph construction , and the knowledge graph is a semantic network graph which takes concepts and relationships respectively as vertices and edges . ( 4 ) It researches knowledge graph analysis . after the traditional search techniques analyzed , we discovered the traditional grammar-level based search have led to low quality because of the lack of appropriate information on the semantics . For this reason , this in-depth study seeks for a solution to this problem , and explores new technologies to change the urgency of this situation , This article introduced semantic web technologies to e-commerce search field , and designed a semantic network structure for the new search system , and discussed the key technologies in e-commerce semantic search , such as semantic structure and semantic search algorithm . Compared with traditional search , semantic search can return more relevant semantic information and can extract users ' search input more accurate . This article presents research on the design of knowledge-based document retrieval systems . We adopted a semantic network structure to represent subject knowledge and clissification scheme knowledge and modeled experts search strategies and user modeling capability as procedural knowledge . These functionalities were incorporated into a prototype knowledge-based retrieval system . This paper shows that a semantic network structure , named semantic schema , can be used for adequate representation of the syntactic dependency relations existing between the words of a natural language phrase . Such a structure can be further used in various NLP applications that make use of the words interactions inside a phrase . Phillip J. DiBona INFORMATION FORAGING THROUGH THE ANALYSIS OF SEMANTIC NETWORK TOPOLOGY 2017 - 2018 Shen-Shyang Ho , Ph.D. Master of Science in Computer Science Information seekers are posed with multiple challenges in gathering an unbiased and comprehensive body of information . The costs of analyzing documents often drive searches toward a small subset of documents . We investigated a topic‐based navigation guidance system in the World Health Organization portal , compared the link connection network and the semantic connection network derived from the guidance system , analyzed the characteristics of the 2 networks from the perspective of the node centrality ( in_closeness , out_closeness , betweenness , in_degree , and out_degree ) , and provided the suggestions to optimize and enhance the topic‐based navigation guidance system . A mixed research method that combines the social network analysis method , clustering analysis method , and inferential analysis methods was used . The clustering analysis results of the link connection network were quite different from those of the semantic connection network . There were significant differences between the link connection network and the semantic network in terms of density and centrality . This fact contrasts with semantic technologies that alreadyrepresent knowledge , like for instance , RDF.In order to face this problem , in this work we introducea formal model able to represent the classes provided bymicroformats , and their relations . In essence , we modelthe semantic information , in a graph like structure , namelysemantic network , where edges are labeled with predicates . We show how semantic networks allow us to model microfor-mats in a very convenient way for reasoning and knowledgeextraction . After a shoxt description of these tools , we describe how the database model has been designed in order to integrate hypermedia capabilities . Techniques for mixing database objects with text and graphics are explained , as well as a strategy to design a data model using the semantic network concept . We then describe some interesting tkatures of the user interface : the Super-text editor , the Instructions Browser , the InstNctions Displayer and GREG a tool to visualize links . This alignment is a di-cult task since these deflnitions may have difierent labels , although they are related to the same medical concept . There- fore , we have chosen to represent the specialised terminology by several concepts taken from the thesaurus Medical Subject Headings ( MeSH ) and the VIDAL dictionary , while the medical general vocabulary is represented by several defl- nitions from the encyclopaedia Wikipedia and from the semantic network of Le And IB1 ( Instance Based Learning 1 ) is used on this research because of its simplicity and accuracy . WordNet component also used to expand the similar words on database . Performance of the algorithm is measured by evaluation methods such as Accuracy , Precision , Recall and F-measure . The same holds if we nd a chain of lexical relationships that connect terms meanings . The WordNet Domains disambiguation algorithm exploits the information from WordNet Domains . WordNet Domains [ 2 ] can be considered an extended version of Many data mining and machine learning researchers have worked on spam detection and filtering , commonly treating it as a basic text classification problem . This paper proposes an efficient yet simple fuzzy based simple method applied on refined key terms set extracted from the email using wordnet and hypernyms concept to filter spam mail . In this paper , we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags . We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users ' tagging behavior together . To personalize further , clustering of users is performed to generate a more accurate ontology for a particular group of users . Abstract . This paper describes a multi-lingual WordNet construction tool , called KUI ( Knowledge Unifying Initiator ) , which is a knowledge user interface for online collaborative knowledge construction . KUI facilitates online community in developing and discussing multi-lingual WordNet . This paper proposes a tool for Semantic Web Service Discovery ( SWSD ) to calculate Semantic Web Service ( SWS ) similarity values between the Requested Query ( RQ ) and Triples ( Tr ) from OWL-S file . SWSD uses processing components such as Parts of Speech ( POS ) , WordNet Dictionary , and WordNet Sense ( WNS ) for reducing the errors in the matching . This matching determines the similarity Threshold ( T ) using Matching Rules ( MR ) , and then calculates the average T values , which are ordered and ranked . One indispensable step in realizing effective recommendation is to compute short text similarities between micro-blogs . In this paper , we utilize two kinds of approaches , traditional cosine-based approach and WordNet-based semantic approach , to compute similarities between micro-blogs and recommend top related ones to users . We conduct experimental study on the effectiveness of two approaches using a set of evaluation measures . This suggests that human perceptions of relatedness are less strictly constrained than perceptions of similarity and establishes a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness . We compare the results of several WordNet-based similarity and relatedness measures to our Rel-122 norms and demonstrate the limitations of WordNet for discovering general indications of semantic relatedness . We also offer a critique of the field ’s reliance upon similarity norms to evaluate relatedness measures . Our Wordnet is created by the expand approach used for EuroWordnet building as in [ 12 ] , based on the bilingual English-Tunisian Arabic dictionary Peace corps dictionary prepared by the linguists : R. Ben abdelkader , A. Ayed and A. Naouar [ 13 ] , and the last version of Princeton Wordnet PWN 3.1 . Moreover , it is modelized according to ISO-LMF by a switable Wordnet-LMF model for aeb language . In this paper , we present aeb wordnet building approach , describe its current state and propose extensions . Our choice of relations has been motivated by linguistic considerations , especially the concerns of the Polish lexical semantics , and by pragmatic reasons . The discussion includes detailed substitution tests , meant to ensure consistency among wordnet editors . Such a similarity can be estimated by measuring the distance between terms in an ontology . WordNet , a large - scale hierarchical database of over 100 , 000 words for English , provides us a means of building our class hierarchy . To measure similarity , we use Jiang - Conrath ( ) , Lin ( ) and path ( ) similarities formulated in Table [ reference ] . Combination of output embeddings . We explore combinations of five types of output embeddings : supervised attributes , unsupervised Word2Vec , GloVe , BoW and WordNet - derived similarity embeddings . We either concatenate ( cnc ) or combine ( cmb ) different embeddings . Therefore , semantic relatedness defined by hierarchical distance between classes can form numerical vectors to be used as output embeddings for zero - shot learning . We build ontological relationships between our classes using the WordNet taxonomy . Due to its large size , WordNet encapsulates all of our AWA and Dog classes . This is competitive with a state - of - the - art WSD - specific supervised model using hand crafted features Iacobacci2016EmbeddingsFW and a task specific biLSTM that is also trained with auxiliary coarse - grained semantic labels and POS tags Raganato2017NeuralSL . The CoVe biLSTM layers follow a similar pattern to those from the biLM ( higher overall performance at the second layer compared to the first ) ; however , our biLM outperforms the CoVe biLSTM , which trails the WordNet first sense baseline . POS tagging To examine whether the biLM captures basic syntax , we used the context representations as input to a linear classifier that predicts POS tags with the Wall Street Journal portion of the Penn Treebank ( PTB ) Marcus1993BuildingAL . Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique . In our experiments , we show that ConceptNet can improve performance on a common benchmark dataset . Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet . We used eight different essay prompts and a student-essay collection for the performance evaluation , whereby our proposed method shows a performance that is better than those of the previous studies . As ConceptNet enables the conduction of a simple text inference , our new method looks very promising with respect to the design of an essay prompt for which a simple inference is required . The paper is based on the framework of translation of English sentences into Hindi language by using ConceptNet for Statistical Machine Translation and Rule-Based Machine Translation system together . ConceptNet is a knowledge-based graph that represents relation between words using relations like “ RelatedTo ” and “ TranslatedFrom ” . Baseline method does n't appropriately translate the sentences . Then , from the GPS location of the user , we ( i ) extract a list of neighboring commercial activities using a reverse geo-coding service and ( ii ) classify the satellite imagery of the area with state-of-the-art techniques . The proposed approach uses the ConceptNet network for ranking the list of candidate activities using both additional information . Results show an improvement in activity recognition accuracy . More specifically , given the GPS position of the user , we both gather ( i ) a list of neighbouring commercial activities using a reverse geo-coding service and ( ii ) classify the satellite image of the area with state-of-the-art techniques . The approach maps classification labels produced by the three classifiers ( i.e. , activity , reverse geocoding localisation , satellite imagery localisation ) to concepts within the ConceptNet network for the sake of improving activity recognition accuracy . Verbal tasks that have traditionally been difficult for computer systems but are easy for young children are among AI 's " grand challenges " . We present the results of testing the ConceptNet 4 system on the verbal part of the standard WPPSI-III IQ test , using simple test-answering algorithms . It is found that the system has the Verbal IQ of an average four-year-old child . In this report a computational study of ConceptNet 4 is performed using tools from the field of network analysis . Part I describes the process of extracting the data from the SQL database that is available online , as well as how the closure of the input among the assertions in the English language is computed . This part also performs a validation of the input as well as checks for the consistency of the entire database . Part II investigates the structural properties of ConceptNet 4 . Different graphs are induced from the knowledge base by fixing different parameters . We present an architecture for crowdsourcing language resources from language learners and a prototype implementation of it as a vocabulary trainer . The vocabulary trainer relies on lexical resources from the ConceptNet semantic network to generate exercises while using the learners ’ answers to improve the resources used for the exercise generation . 2012 ACM Subject Classification Information systems → Web services ; Information systems → Ontologies It does so by formalising the observation that humans often reason by picturing different contingencies of what the world could be like , and then choose to believe what is thought to be most plausible . We discuss and evaluate an implementation where relevant commonsense background information is obtained from the ConceptNet semantic network , translated into our formalism , and processed by a reasoner for our logic . We used a method consisting of an ensemble of classifiers built using deep learning strategies . It also makes use of the performance and advantages of pretrained word embeddings from the ConceptNet semantic network . We tested two different networks architectures : a recurrent network and a convolutional network . A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space . We present an ensemble method that combines embeddings produced by GloVe ( Pennington et al. , 2014 ) and word2vec ( Mikolov et al. , 2013 ) with structured knowledge from the semantic networks ConceptNet ( Speer and Havasi , 2012 ) and PPDB ( Ganitkevitch et al. , 2013 ) , merging their information into a common representation with a large , multilingual vocabulary . The embeddings it produces achieve state-of-the-art performance on many word-similarity evaluations . Specifically , we employ concepts as features and present a concept extraction algorithm based on a novel concept parser scheme to extract semantic features that exploit semantic relationships between words in natural language text . Additional conceptual information of a concept is obtained using the ConceptNet ontology : Concepts extracted from text are sent as queries to ConceptNet to extract their semantics . We select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique . In this paper , we present a novel methodology for automating the schema matching process at attribute level , among three non-standard and serialized schemas . We achieved 71.8 % mappings , using a four stage process with string matching , longest common substring matching using suffix tree , and ConceptNet lookup . We fine tune different parameters and hyper-parameters associated with each of our model and report the micro precision , micro recall and micro F1-score for each model . We identify the Bi-LSTM model , along with the input word embedding taken as the concatenation of the embeddings generated from the bidirectional character LSTM and ConceptNet embedding , as the best performing model with a highest micro-F1 score over the test set as 0.7261 . In the presented system , we demonstrate how to equip service robots with commonsense knowledge and the dynamic reasoning capabilities of Answer Set Programming ( ASP ) . We investigated the response of our system to basic human needs and evaluated the viability and scalability of the combination of the commonsense knowledge database ConceptNet 5 and the ASP solver Clingo . Our results show the flexibility and versatility of our approach . Additionally , we exploit Wikipedia abstracts in our DQA experiments as described below . paragraph : ConceptNet Integration Here we describe the heuristic we use to obtain plausibly relevant supplementary knowledge for understanding a text pair from ConceptNet . These results suggest that ESIM is able to learn important background information from the large - scale datasets and from pretrained embeddings , but this can be supplemented when necessary . Nevertheless , both ESIM and our BiLSTM models when trained with knowledge from ConceptNet are sensitive to the semantics of the provided assertions as demonstrated in our analysis in § [ reference ] . We argue that this is a desirable side effect because it makes the predictions of our model more interpretable than those not trained with knowledge . Additionally , we call the learning where the learner describes problem maps “ learning by describing problem map ” . In the problem map , a node is a problem and a link describes difference between problems as relations . For the latter support , that is , a support to provide each learner with suitable feedback for the learner ’s thinking , we classify characteristics of problems using several factors . In contrast , if the problem solver can not solve a problem , the next problem should be easier than it . Therefore , the problem exercise map is a problem map which is described by using two types of arrow related difficulty of problems , that is “ more difficult relation ” and “ easier relation ” . Fig. 1 : A relation of ( i ) problem-solving exercise , ( ii ) problem-posing exercise and ( iii ) problem map describing exercise In a learning activity by describing problem exercise map , the learner must not consider binomial relations between two problems , but also , must consider multinomial relations between connected problems by arrows based on viewpoint of coordinator of problem solving exercise . In order to let the learner think about the relations , the authors propose a support environment for learning by describing " problem map " . The " problem map " is a kind of semantic network where a node is a problem and a link describes relations between two problems . The authors are developing a support environment for describing a problem map in arithmetic learning . However , finding the needed information is a complex task since there is a lack of semantic relations and organization between them . This chapter presents a problem-solving map framework for extracting and integrating knowledge from unstructured documents on the Inter-net by exploiting the semantic links between problems , methods for solving them and the people who could solve them . This challenging area of research needs both complex natural language processing , including deep semantic relation interpretation , and the participation of end-users for annotating the answers scattered on the Web . This paper aims to extract the relation between the disease symptoms and the treatments ( called the symptom-treatment relation ) , from hospital-web-board documents to construct the problem-solving map which benefits inexpert people to solve their health problems in preliminary . Both symptoms and treatments expressed on documents are based on several EDUs ( elementary discourse units ) . In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies require storage of large volumes of both quantitative and qualitative information and means to retrieve old cases from these case libraries and match them with a current problem . A semantic network based approach is being presented for natural language processing of qualitative information available from industrial systems in the form of textual descriptions . Syntactic rules are used to extract relationships between the words and the spatial arrangement is preserved using semantic networks . * Hypermedia-Assisted Instruction and Second Language Learning : A Semantic Network-Based Approach * An Example of Software Development and Authoring With HyperCard : Creating an Interactive Video Simulation for Teaching Basic Principles of Classroom Management to Pre-Service Teachers * Integrating Alternative Input Devices and Hypermedia for Use by Exceptional Individuals SW technology can be used to share and re-use knowledge between KBSs in a distributed and heterogeneous environment . Semantic data networks on the Web provide the bases of the SW , enabling knowledge that is distributed over the Web to be easily shared and processed by a machine . A semantic data structure is required for representing data or knowledge in a shareable format , and an ontology is designed to fulfill this objective . The different lines we offer in our research fall mainly into three levels : x The most basic one is in the field of computational linguistics and related to Logical Operations on RSR ( RSR Inverses and plurals , RSR combinations , etc ) , once they have been created . The application of Walter Bosma ’s results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here . x In parallel , we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR . RSR combinations , etc ) , once they have been created . The application of Walter Bosma 's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here . • In parallel , we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR . Topic Maps are a new ISO standard proving a paradigm for semantic information networks over various kinds of resources . The article gives a brief introduction to the Topic Map paradigm and explains why searching and navigation in Topic Maps has great advantages over other techniques and what the differences to Semantic Networks and RDF are . The paper presents an approach for integration of both , semantic networks and hierarchical structures , within a single learning object based on the eLearning markup language 3 . 1 Educational Benefits of Semantic Networks Semantic networks are also described as knowledge networks , since they combine isolated facts with associations and meanings to applicable knowledge . That ’s why an utilization for knowledge transfer and description of learning objects ( LOs ) stands to reason . I provide analyses of the functional syntax of the key examples throughout , and I also analyze selected examples at the level of meaning . The picture of the discourse purposes served by the various types of theme is less complete , but I describe work in the COMMUNAL Project which models the ‘ higher ’ decision-making that affects what is required in the corresponding semantic system networks for ‘ theme ’ . By the end of the paper it will be clear that the various types of ‘ theme ’ recognized in this comprehensive treatment cover a very wide range of meanings , and that they serve an even wider range of purposes in discourse - so raising the question of how far it is still helpful to think in terms of a single , ’ unified ’ concept of ‘ theme ’ . Most of the literature is on English language but recently an increasing number of works are tackling the multilinguality issue , especially in booming online languages such as Chinese . Sentiment analysis techniques can be broadly categorized into symbolic and sub - symbolic approaches : the former include the use of lexicons , ontologies , and semantic networks to encode the polarity associated with words and multiword expressions ; the latter consist of supervised , semi - supervised and unsupervised machine learning techniques that perform sentiment classification based on word co - occurrence frequencies . Among these , the most popular recently are algorithms based on deep neural networks and generative adversarial networks .
This paper presents an attractor neural network model of semantic fact retrieval , based on the original semantic network models of Collins and Quillian . In the context of modelling a semantic network , a distinction is made between associations linking together objects belonging to hierarchically related semantic classes , and associations linking together objects and their attributes .
Keywords : knowledge representation ; standard model ; knowledge network ; semantic network ; parataxis network Abstract . The basic unit of knowledge representation is the knowledge component .
There is a lack of agreement between information that is extracted from visual data and the text description . Ontologies are at the heart of all Semantic Web applications , representing domain concepts and relations in the form of a semantic network . In this study , we applied ontology to bridge the semantic gap by developing a prototype multi-modality ontology IRS based on sports news and by integrating it with DBpedia to enrich the knowledge base while overcoming the problem of semantic heterogeneity .
Data were drawn from open-ended questionnaires ( n = 282 ) . Semantic network analysis was conducted using CATPAC , artificial neural network software . Results verify previous findings that students turn to the Internet before the library , but a deeper investigation revealed different preferences for study versus project-related research .
Web scraping and text mining tools based on R language were used to create semantic network structures . Semantic network analysis revealed that the focus of the articles was on cancer . The analysis carried out using the Sankey diagram revealed that scientists who work in cancer are most frequently involved in the lung , however , the scientists who related with the lung are not focused on treatment , and heart .
We expect to fill in the gaps of prior literatures which concluded that the blockbuster project has the same effects on all the other projects within same categories . As a major contribution of this paper , we obtain market structure insights from the creator generated contents based on a text-mining approach and combine it with semantic network analysis to assess the degree to which the latent competition causes cannibalize or spill-over effects inside the same category . Our study uses unique data set , compiled by Python based self-developed crawler .
In phase 2 , these triples are then fed into a coherence network analysis process , using a genetic algorithm optimization . Finally , the highest-value sub networks are processed into a semantic network graph for display . Initial work on a well- known data set ( a Wikipedia article on Abraham Lincoln ) has shown excellent results without any specific tuning .
If learner 's dictionaries absorb the findings in cognitive metaphor , revealing the cognitive nature of metaphor and the metaphorical motivation between multiple meanings , it will be greatly beneficial for learners to understand and manipulate words correctly . So , by comparing the definitions of metaphor , the uses of figurative labels and special columns in several leading English learner 's dictionaries , this paper finds that the cognitive view of metaphor is not sufficiently reflected in learner 's dictionaries and thus the author proposes that the cognitive view of metaphor be expressed in the definition of metaphor and that cognitive metaphor be better reflected by establishing conceptual metaphor modules like MED 's Metaphor Box , using explanation in brackets or figurative labels like " fig " or " M→ " to reveal metaphorical extension between senses or providing a semantic network graph for words with multiple senses .
The relationships among concepts include collection types formed by combining two arbitrary types above concepts . ( 3 ) It researches knowledge graph construction , and the knowledge graph is a semantic network graph which takes concepts and relationships respectively as vertices and edges . ( 4 ) It researches knowledge graph analysis .
after the traditional search techniques analyzed , we discovered the traditional grammar-level based search have led to low quality because of the lack of appropriate information on the semantics . For this reason , this in-depth study seeks for a solution to this problem , and explores new technologies to change the urgency of this situation , This article introduced semantic web technologies to e-commerce search field , and designed a semantic network structure for the new search system , and discussed the key technologies in e-commerce semantic search , such as semantic structure and semantic search algorithm . Compared with traditional search , semantic search can return more relevant semantic information and can extract users ' search input more accurate .
This article presents research on the design of knowledge-based document retrieval systems . We adopted a semantic network structure to represent subject knowledge and clissification scheme knowledge and modeled experts search strategies and user modeling capability as procedural knowledge . These functionalities were incorporated into a prototype knowledge-based retrieval system .
This paper shows that a semantic network structure , named semantic schema , can be used for adequate representation of the syntactic dependency relations existing between the words of a natural language phrase . Such a structure can be further used in various NLP applications that make use of the words interactions inside a phrase .
Phillip J. DiBona INFORMATION FORAGING THROUGH THE ANALYSIS OF SEMANTIC NETWORK TOPOLOGY 2017 - 2018 Shen-Shyang Ho , Ph.D. Master of Science in Computer Science Information seekers are posed with multiple challenges in gathering an unbiased and comprehensive body of information . The costs of analyzing documents often drive searches toward a small subset of documents .
We investigated a topic‐based navigation guidance system in the World Health Organization portal , compared the link connection network and the semantic connection network derived from the guidance system , analyzed the characteristics of the 2 networks from the perspective of the node centrality ( in_closeness , out_closeness , betweenness , in_degree , and out_degree ) , and provided the suggestions to optimize and enhance the topic‐based navigation guidance system . A mixed research method that combines the social network analysis method , clustering analysis method , and inferential analysis methods was used . The clustering analysis results of the link connection network were quite different from those of the semantic connection network . There were significant differences between the link connection network and the semantic network in terms of density and centrality .
This fact contrasts with semantic technologies that alreadyrepresent knowledge , like for instance , RDF.In order to face this problem , in this work we introducea formal model able to represent the classes provided bymicroformats , and their relations . In essence , we modelthe semantic information , in a graph like structure , namelysemantic network , where edges are labeled with predicates . We show how semantic networks allow us to model microfor-mats in a very convenient way for reasoning and knowledgeextraction .
After a shoxt description of these tools , we describe how the database model has been designed in order to integrate hypermedia capabilities . Techniques for mixing database objects with text and graphics are explained , as well as a strategy to design a data model using the semantic network concept . We then describe some interesting tkatures of the user interface : the Super-text editor , the Instructions Browser , the InstNctions Displayer and GREG a tool to visualize links .
This alignment is a di-cult task since these deflnitions may have difierent labels , although they are related to the same medical concept . There- fore , we have chosen to represent the specialised terminology by several concepts taken from the thesaurus Medical Subject Headings ( MeSH ) and the VIDAL dictionary , while the medical general vocabulary is represented by several defl- nitions from the encyclopaedia Wikipedia and from the semantic network of Le
And IB1 ( Instance Based Learning 1 ) is used on this research because of its simplicity and accuracy . WordNet component also used to expand the similar words on database . Performance of the algorithm is measured by evaluation methods such as Accuracy , Precision , Recall and F-measure .
The same holds if we nd a chain of lexical relationships that connect terms meanings . The WordNet Domains disambiguation algorithm exploits the information from WordNet Domains . WordNet Domains [ 2 ] can be considered an extended version of
Many data mining and machine learning researchers have worked on spam detection and filtering , commonly treating it as a basic text classification problem . This paper proposes an efficient yet simple fuzzy based simple method applied on refined key terms set extracted from the email using wordnet and hypernyms concept to filter spam mail .
In this paper , we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags . We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users ' tagging behavior together . To personalize further , clustering of users is performed to generate a more accurate ontology for a particular group of users .
Abstract . This paper describes a multi-lingual WordNet construction tool , called KUI ( Knowledge Unifying Initiator ) , which is a knowledge user interface for online collaborative knowledge construction . KUI facilitates online community in developing and discussing multi-lingual WordNet .
This paper proposes a tool for Semantic Web Service Discovery ( SWSD ) to calculate Semantic Web Service ( SWS ) similarity values between the Requested Query ( RQ ) and Triples ( Tr ) from OWL-S file . SWSD uses processing components such as Parts of Speech ( POS ) , WordNet Dictionary , and WordNet Sense ( WNS ) for reducing the errors in the matching . This matching determines the similarity Threshold ( T ) using Matching Rules ( MR ) , and then calculates the average T values , which are ordered and ranked .
One indispensable step in realizing effective recommendation is to compute short text similarities between micro-blogs . In this paper , we utilize two kinds of approaches , traditional cosine-based approach and WordNet-based semantic approach , to compute similarities between micro-blogs and recommend top related ones to users . We conduct experimental study on the effectiveness of two approaches using a set of evaluation measures .
This suggests that human perceptions of relatedness are less strictly constrained than perceptions of similarity and establishes a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness . We compare the results of several WordNet-based similarity and relatedness measures to our Rel-122 norms and demonstrate the limitations of WordNet for discovering general indications of semantic relatedness . We also offer a critique of the field ’s reliance upon similarity norms to evaluate relatedness measures .
Our Wordnet is created by the expand approach used for EuroWordnet building as in [ 12 ] , based on the bilingual English-Tunisian Arabic dictionary Peace corps dictionary prepared by the linguists : R. Ben abdelkader , A. Ayed and A. Naouar [ 13 ] , and the last version of Princeton Wordnet PWN 3.1 . Moreover , it is modelized according to ISO-LMF by a switable Wordnet-LMF model for aeb language . In this paper , we present aeb wordnet building approach , describe its current state and propose extensions .
Our choice of relations has been motivated by linguistic considerations , especially the concerns of the Polish lexical semantics , and by pragmatic reasons . The discussion includes detailed substitution tests , meant to ensure consistency among wordnet editors .
Such a similarity can be estimated by measuring the distance between terms in an ontology . WordNet , a large - scale hierarchical database of over 100 , 000 words for English , provides us a means of building our class hierarchy . To measure similarity , we use Jiang - Conrath ( ) , Lin ( ) and path ( ) similarities formulated in Table [ reference ] .
Combination of output embeddings . We explore combinations of five types of output embeddings : supervised attributes , unsupervised Word2Vec , GloVe , BoW and WordNet - derived similarity embeddings . We either concatenate ( cnc ) or combine ( cmb ) different embeddings .
Therefore , semantic relatedness defined by hierarchical distance between classes can form numerical vectors to be used as output embeddings for zero - shot learning . We build ontological relationships between our classes using the WordNet taxonomy . Due to its large size , WordNet encapsulates all of our AWA and Dog classes .
This is competitive with a state - of - the - art WSD - specific supervised model using hand crafted features Iacobacci2016EmbeddingsFW and a task specific biLSTM that is also trained with auxiliary coarse - grained semantic labels and POS tags Raganato2017NeuralSL . The CoVe biLSTM layers follow a similar pattern to those from the biLM ( higher overall performance at the second layer compared to the first ) ; however , our biLM outperforms the CoVe biLSTM , which trails the WordNet first sense baseline . POS tagging To examine whether the biLM captures basic syntax , we used the context representations as input to a linear classifier that predicts POS tags with the Wall Street Journal portion of the Penn Treebank ( PTB ) Marcus1993BuildingAL .
Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique . In our experiments , we show that ConceptNet can improve performance on a common benchmark dataset . Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet .
We used eight different essay prompts and a student-essay collection for the performance evaluation , whereby our proposed method shows a performance that is better than those of the previous studies . As ConceptNet enables the conduction of a simple text inference , our new method looks very promising with respect to the design of an essay prompt for which a simple inference is required .
The paper is based on the framework of translation of English sentences into Hindi language by using ConceptNet for Statistical Machine Translation and Rule-Based Machine Translation system together . ConceptNet is a knowledge-based graph that represents relation between words using relations like “ RelatedTo ” and “ TranslatedFrom ” . Baseline method does n't appropriately translate the sentences .
Then , from the GPS location of the user , we ( i ) extract a list of neighboring commercial activities using a reverse geo-coding service and ( ii ) classify the satellite imagery of the area with state-of-the-art techniques . The proposed approach uses the ConceptNet network for ranking the list of candidate activities using both additional information . Results show an improvement in activity recognition accuracy .
More specifically , given the GPS position of the user , we both gather ( i ) a list of neighbouring commercial activities using a reverse geo-coding service and ( ii ) classify the satellite image of the area with state-of-the-art techniques . The approach maps classification labels produced by the three classifiers ( i.e. , activity , reverse geocoding localisation , satellite imagery localisation ) to concepts within the ConceptNet network for the sake of improving activity recognition accuracy .
Verbal tasks that have traditionally been difficult for computer systems but are easy for young children are among AI 's " grand challenges " . We present the results of testing the ConceptNet 4 system on the verbal part of the standard WPPSI-III IQ test , using simple test-answering algorithms . It is found that the system has the Verbal IQ of an average four-year-old child .
In this report a computational study of ConceptNet 4 is performed using tools from the field of network analysis . Part I describes the process of extracting the data from the SQL database that is available online , as well as how the closure of the input among the assertions in the English language is computed . This part also performs a validation of the input as well as checks for the consistency of the entire database . Part II investigates the structural properties of ConceptNet 4 . Different graphs are induced from the knowledge base by fixing different parameters .
We present an architecture for crowdsourcing language resources from language learners and a prototype implementation of it as a vocabulary trainer . The vocabulary trainer relies on lexical resources from the ConceptNet semantic network to generate exercises while using the learners ’ answers to improve the resources used for the exercise generation . 2012 ACM Subject Classification Information systems → Web services ; Information systems → Ontologies
It does so by formalising the observation that humans often reason by picturing different contingencies of what the world could be like , and then choose to believe what is thought to be most plausible . We discuss and evaluate an implementation where relevant commonsense background information is obtained from the ConceptNet semantic network , translated into our formalism , and processed by a reasoner for our logic .
We used a method consisting of an ensemble of classifiers built using deep learning strategies . It also makes use of the performance and advantages of pretrained word embeddings from the ConceptNet semantic network . We tested two different networks architectures : a recurrent network and a convolutional network .
A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space . We present an ensemble method that combines embeddings produced by GloVe ( Pennington et al. , 2014 ) and word2vec ( Mikolov et al. , 2013 ) with structured knowledge from the semantic networks ConceptNet ( Speer and Havasi , 2012 ) and PPDB ( Ganitkevitch et al. , 2013 ) , merging their information into a common representation with a large , multilingual vocabulary . The embeddings it produces achieve state-of-the-art performance on many word-similarity evaluations .
Specifically , we employ concepts as features and present a concept extraction algorithm based on a novel concept parser scheme to extract semantic features that exploit semantic relationships between words in natural language text . Additional conceptual information of a concept is obtained using the ConceptNet ontology : Concepts extracted from text are sent as queries to ConceptNet to extract their semantics . We select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique .
In this paper , we present a novel methodology for automating the schema matching process at attribute level , among three non-standard and serialized schemas . We achieved 71.8 % mappings , using a four stage process with string matching , longest common substring matching using suffix tree , and ConceptNet lookup .
We fine tune different parameters and hyper-parameters associated with each of our model and report the micro precision , micro recall and micro F1-score for each model . We identify the Bi-LSTM model , along with the input word embedding taken as the concatenation of the embeddings generated from the bidirectional character LSTM and ConceptNet embedding , as the best performing model with a highest micro-F1 score over the test set as 0.7261 .
In the presented system , we demonstrate how to equip service robots with commonsense knowledge and the dynamic reasoning capabilities of Answer Set Programming ( ASP ) . We investigated the response of our system to basic human needs and evaluated the viability and scalability of the combination of the commonsense knowledge database ConceptNet 5 and the ASP solver Clingo . Our results show the flexibility and versatility of our approach .
Additionally , we exploit Wikipedia abstracts in our DQA experiments as described below . paragraph : ConceptNet Integration Here we describe the heuristic we use to obtain plausibly relevant supplementary knowledge for understanding a text pair from ConceptNet .
These results suggest that ESIM is able to learn important background information from the large - scale datasets and from pretrained embeddings , but this can be supplemented when necessary . Nevertheless , both ESIM and our BiLSTM models when trained with knowledge from ConceptNet are sensitive to the semantics of the provided assertions as demonstrated in our analysis in § [ reference ] . We argue that this is a desirable side effect because it makes the predictions of our model more interpretable than those not trained with knowledge .
Additionally , we call the learning where the learner describes problem maps “ learning by describing problem map ” . In the problem map , a node is a problem and a link describes difference between problems as relations . For the latter support , that is , a support to provide each learner with suitable feedback for the learner ’s thinking , we classify characteristics of problems using several factors .
In contrast , if the problem solver can not solve a problem , the next problem should be easier than it . Therefore , the problem exercise map is a problem map which is described by using two types of arrow related difficulty of problems , that is “ more difficult relation ” and “ easier relation ” . Fig. 1 : A relation of ( i ) problem-solving exercise , ( ii ) problem-posing exercise and ( iii ) problem map describing exercise In a learning activity by describing problem exercise map , the learner must not consider binomial relations between two problems , but also , must consider multinomial relations between connected problems by arrows based on viewpoint of coordinator of problem solving exercise .
In order to let the learner think about the relations , the authors propose a support environment for learning by describing " problem map " . The " problem map " is a kind of semantic network where a node is a problem and a link describes relations between two problems . The authors are developing a support environment for describing a problem map in arithmetic learning .
However , finding the needed information is a complex task since there is a lack of semantic relations and organization between them . This chapter presents a problem-solving map framework for extracting and integrating knowledge from unstructured documents on the Inter-net by exploiting the semantic links between problems , methods for solving them and the people who could solve them . This challenging area of research needs both complex natural language processing , including deep semantic relation interpretation , and the participation of end-users for annotating the answers scattered on the Web .
This paper aims to extract the relation between the disease symptoms and the treatments ( called the symptom-treatment relation ) , from hospital-web-board documents to construct the problem-solving map which benefits inexpert people to solve their health problems in preliminary . Both symptoms and treatments expressed on documents are based on several EDUs ( elementary discourse units ) .
In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies require storage of large volumes of both quantitative and qualitative information and means to retrieve old cases from these case libraries and match them with a current problem . A semantic network based approach is being presented for natural language processing of qualitative information available from industrial systems in the form of textual descriptions . Syntactic rules are used to extract relationships between the words and the spatial arrangement is preserved using semantic networks .
* Hypermedia-Assisted Instruction and Second Language Learning : A Semantic Network-Based Approach * An Example of Software Development and Authoring With HyperCard : Creating an Interactive Video Simulation for Teaching Basic Principles of Classroom Management to Pre-Service Teachers * Integrating Alternative Input Devices and Hypermedia for Use by Exceptional Individuals
SW technology can be used to share and re-use knowledge between KBSs in a distributed and heterogeneous environment . Semantic data networks on the Web provide the bases of the SW , enabling knowledge that is distributed over the Web to be easily shared and processed by a machine . A semantic data structure is required for representing data or knowledge in a shareable format , and an ontology is designed to fulfill this objective .
The different lines we offer in our research fall mainly into three levels : x The most basic one is in the field of computational linguistics and related to Logical Operations on RSR ( RSR Inverses and plurals , RSR combinations , etc ) , once they have been created . The application of Walter Bosma ’s results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here . x In parallel , we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR .
RSR combinations , etc ) , once they have been created . The application of Walter Bosma 's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here . • In parallel , we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR .
Topic Maps are a new ISO standard proving a paradigm for semantic information networks over various kinds of resources . The article gives a brief introduction to the Topic Map paradigm and explains why searching and navigation in Topic Maps has great advantages over other techniques and what the differences to Semantic Networks and RDF are .
The paper presents an approach for integration of both , semantic networks and hierarchical structures , within a single learning object based on the eLearning markup language 3 . 1 Educational Benefits of Semantic Networks Semantic networks are also described as knowledge networks , since they combine isolated facts with associations and meanings to applicable knowledge . That ’s why an utilization for knowledge transfer and description of learning objects ( LOs ) stands to reason .
I provide analyses of the functional syntax of the key examples throughout , and I also analyze selected examples at the level of meaning . The picture of the discourse purposes served by the various types of theme is less complete , but I describe work in the COMMUNAL Project which models the ‘ higher ’ decision-making that affects what is required in the corresponding semantic system networks for ‘ theme ’ . By the end of the paper it will be clear that the various types of ‘ theme ’ recognized in this comprehensive treatment cover a very wide range of meanings , and that they serve an even wider range of purposes in discourse - so raising the question of how far it is still helpful to think in terms of a single , ’ unified ’ concept of ‘ theme ’ .
Most of the literature is on English language but recently an increasing number of works are tackling the multilinguality issue , especially in booming online languages such as Chinese . Sentiment analysis techniques can be broadly categorized into symbolic and sub - symbolic approaches : the former include the use of lexicons , ontologies , and semantic networks to encode the polarity associated with words and multiword expressions ; the latter consist of supervised , semi - supervised and unsupervised machine learning techniques that perform sentiment classification based on word co - occurrence frequencies . Among these , the most popular recently are algorithms based on deep neural networks and generative adversarial networks .
An attractor neural network model of semantic fact retrieval Knowledge Network Based on the Standard Model of Knowledge Representation Improving the performance of multi-modality ontology image retrieval system using DBpedia Convenience or credibility? A study of college student online research behaviors Forecasting of innovation in the light of semantic networks An empirical analysis of semantic network in online crowdfunding: evidence from kickstarter Automatic theory generation from analyst text files using coherence networks Reflection of Cognitive View of Metaphor in English Learner's Dictionaries Research on Constructing the Knowledge Graph Based on Microblog Research and Design of E-commerce Semantic Search Knowledge-based document retrieval: framework and design XML Semantic Schema Annotation for Dependency Relationships Information foraging through the analysis of semantic network topology Social network analysis on a topic‐based navigation guidance system in a public health portal Modeling Implicit Knowledge from Microformatted Websites Hyper9002: an online operating manual for a chemical manufacturer using hypertext integrated with an object oriented database Automatic alignment of medical vs. general terminologies Sentiment Classification of Film Reviews Using IB1 Automatic annotation for mapping discovery in data integration systems A Fuzzy approach for Spam Mail Detection integrated with wordnet hypernyms key term extraction Personalization in tag ontology learning for recommendation making KUI: Self-organizing Multi-lingual WordNet Construction Tool Ranking semantic web services by matching triples and query based on similarity measure Finding Related Micro-blogs Based on WordNet A New Set of Norms for Semantic Relatedness Measures Tunisian Arabic aeb Wordnet: Current State and Future Extensions Semantic relations among adjectives in Polish WordNet 2.0: a new relation set, discussion and evaluation Evaluation of output embeddings for fine-grained image classification Deep contextualized word representations How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval Automatic Detection of Off-topic Documents using ConceptNet and Essay Prompt in Automated English Essay Scoring Improvement of English-Hindi machine translation using ConceptNet Augmenting Activity Recognition with Commonsense Knowledge and Satellite Imagery Improving Activity Recognition via Satellite Imagery and Commonsense Knowledge Verbal IQ of a Four-Year Old Achieved by an AI System Commonsense Reasoning and Large Network Analysis: A Computational Study of ConceptNet 4 Designing a Prototype Architecture for Crowdsourcing Language Resources Reasoning about Conditional Beliefs for the Winograd Schema Challenge Ensemble of Artificial Neural Networks for Aspect Based Sentiment Analysis An Ensemble Method to Produce High-Quality Word Embeddings Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach Semantic Bridge for Resolving Healthcare Data Interoperability KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances Teaching Commonsense and Dynamic Knowledge to Service Robots Dynamic Integration of Background Knowledge in Neural NLU Systems An Intelligent Learning Environment for Describing Problem Map in Arithmetical Learning A support environment for learning by describing problem map Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering Using Extracted Symptom-Treatment Relation from Texts to Construct Problem-Solving Map Processing Textual Information from Industrial Systems Using Semantic Networks Multimedia and Megachange: New Roles for Educational Computing Decision Support Tools for Ontological Engineering Didactic Networks: A proposal for e-learning content generation Didactic Networks: A proposal for e-learning content generation Semantic Resource Exploitation with Topic Maps Utilization of Semantic Networks for Education: On the Enhancement of Existing Learning Objects with Topic Maps in ³ The Many Types of 'Theme' in English: their semantic systems and their functional syntax Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling
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ICEC SWWS SAC ESANN SEBD DASFAA Workshops ACL (2) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NAACL-HLT IJCAI DEXA Workshops AAAI (Late-Breaking Developments) LDK (Posters) COMMONSENSE ICCP ICOIN SemEval@NAACL-HLT ICSR ICCE Information Retrieval in Biomedicine KICSS IICAI GLDV-Jahrestagung GI Jahrestagung (2) Knowl. Based Syst.
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