Semantic Network

This work proposes a novel approach to visually interact with semantic networks constructed via natural language processing techniques. The proposed web interface, WINS, allows the user to select a textual document to be analyzed, choose the algorithm to construct the semantic network, and visualize the network with its metrics. Unlike previous works, which are typically based on co-occurrence matrix for constructing the text network, the proposed interface embeds an additional approach based on the combination of network science with distributed representations of words and phrases.

)that)a)neural)network)can)be)implemented)to)identify)whether) students)have)integrated)into)their)lexicon)schemata)related)concepts)by)the)contents) of) a) school) course.) Specifically,) a) neural) net) is) trained) to) discriminate) between) successful) and) unsuccessful) students´) semantic) priming) latencies) of) schemata) related) words)obtained)by)a)semantic)priming)study)at)the)beginning)and)end)of)a)course.)This) neural) network) discrimination) capacity) is) based) on) the) idea) that) once) a) student) has) integrated) new) knowledge) in) long[term) memory) then) a) semantic) priming) effect) is) obtained) from) schemata) related) words) (single) word) schemata) priming;) e.g.,) 2].) The) current) paper) constitutes) the) first) of) three) documents) providing) a) more) in[depth) analysis)to)this)approach)for)cognitive)assessment)of)learning.)For)instance,)the)mental) representation)technique)used)to)obtain)natural)semantic)networks)from)students)and) teachers) (as) opposed) to) idiosyncratic) or) artificial) semantic) nets)) to) study) computer) simulated) schemata) behaviour) is) put) under) academic) scrutiny.) Here,) it) is) argued) that) statistical) properties) regarding) the) kind) of) semantic) net) (small) world) structure,) scale) free)degree))as)well)as)the)implicit)distributed)schema)through)semantic)connectedness) among) concepts) relate) to) emergent) connectionist) schemas) underlying) schemata) priming)that)can)be)identified)by)a)neural)net.)) ) Keywords:)Schemata!behaviour,!meaning,!natural!semantic!networks,!learning.! In!his!accusations!to!cognitive!science!the!famous!behavioural!scientist!Skinner!noted![3]:! ! "I'accuse'cognitive 'scientists'of'misusing'the'metaphor'of'storage…'I'accuse'cognitive' scientists'of'speculating'about'internal'processes…'They'have'no'appropriate'means'of' observation…' I' accuse' cognitive' scientists' of' emasculating' the' experimental' analysis' of' behavior…' I' accuse' cognitive' scientists' as' I' would' psychoanalysis,' of' inventing' explanatory'systems'which'are'admired'for'profundity'which'is'more'properly'called' inaccessibility……'I'accuse'cognitive'scientists'of'relaxing'standards'of'definition'and' logical'thinking'and'releasing'a'flood'of'speculation...'"' ! Certainly,! regarding! the! case! for! the! "Knowledge! Schema"! concept,! some! of! Skinner´s! accusations!toward!cognitive!science!seem!to!hold.!For!instance,!no!definitive!definition!on!this! mental!phenomenon!has!been!achieved!yet.!Even!when!it!is!clear!that!a!schema!(schemata!for! plural)! refers! to! our! capacity! to! dynamically! represent! inside! our! minds! internal! and! surrounding! events! by! using! a! specific! data! structure,! this! theoretical! construct! is! admired! MoralesBMartinez,' G.' E., ' &' SantosBAlcantara,' M.' G.' (2015).' Alternative' Empirical' Directions' to' Evluate' Schamata' Organization' and' Meaning.) Advances)in)Social)Sciences)Research)Journal,)2( ))51B58.

This paper is a contribution to the modelling of adaptive hypermedia. The main feature of such a system is able to construct its adaptive behaviour easily. It is based on the concept of the active rule (Event-Condition-Action). The advantage of this concept resides in the integration of all existing adaptation techniques in the system. Furthermore, this system allows introducing new adaptation strategies easily. The system architecture consists of three main parts: first the traditional hypermedia subsystem that contains the navigational model, the interface model, and the multimedia resources; second the adaptive subsystem that contains the user model, the semantic network model, the events analyser, and the adaptive behaviour specification entity; third the tasks model which contain the strategic rules bases associated with the adaptive behaviour specification entity, the navigational rules bases associated with the navigational model, and the interface rules bases associated with the interface model.

This paper studies the semantics for the class of all defeasible (inheritance) networks, including cyclic and inconsistent networks using a transformation approach. First we show that defeasible networks can be translated, tractably, to default theories while preserving Horty's offpath credulous semantics for all consistent networks. Using the existing methods in dealing with the semantics of default logic, we are able to provide a tractable skeptical semantics, the well-founded semantics, and a new credulous semantics, the regular semantics, both of which are defined for any defeasible network. Furthermore, we show that these semantics are based on the same principle of specificity used by Horty in defining his credulous semantics of defeasible networks. 1 Introduction Two fundamental problems are to be addressed in this paper. First, the semantics of defeasible networks has previously been studied mainly under the assumption that such a network is acyclic and consis...

Background: Knowing the development pattern of children’s language is applicable in developmental psychology. Network models of language are helpful for the identification of these patterns. Objectives: We examined the small-world properties of featured semantic networks of developing children. Materials & Methods: In this longitudinal study, the featured semantic networks of children aged 18-30 months were obtained using R software version 3.5.2 and the igraph software package. The data of 2000 English (British)-speaking children, half boy and half girls, were gathered from existing databases of MCDI (between 2000 and 2007) and McRae feature norms. The growth pattern of these networks was illustrated by graph measures. Comparing these measures with those of the reference random networks, the small-world structure can be examined. Results: To have a comparison between path length and clustering coefficient of featured semantic networks with those of random networks, we computed the ...

This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework owns a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of the semantic network, which constitutes a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE is presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic and drove the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yi...

Generalized formalization of recognition algorithm for specialized computer systems is presented in this paper. The structure features of image recognition methods, which have to be taken into account when developing classifiers for object recognition in specialized computer systems, are described. The fundamental types of images characteristics-features, which are used in various methods of image recognition, are discussed. Approaches to development of classifiers for recognizing robotization objects, which are implemented on basis of Haar classifiers, are discussed. The issues of using machine learning algorithm of adaptive gain AdaBoost for development of such classifiers are also considered. Utilities have been developed for implementation of classifiers for object recognition in specialized computer systems.

This paper adapts a widespread formalism of Knowledge Representation known in the AI literature as J. Sowa's Conceptual Graphs to the purposes of Content Analysis. It is proposed that instead of nested contexts, negation and modalities could be represented by colouring the links and the nodes of the graphs. This colour-based representation of logic enables the replacement of Sowa's game-theoretical semantics by a matching algorithm. It is also claimed that the resulting 'local' application of negation and modalities is close to the behaviour of negation and adverbs in natural language. Above all, the use of colours restores a unique level in semantic networks. These 'flat' semantic networks are appealing to social scientists because they avoid information redundancy and improve connectivity in the network. Two methods of textual analysis that can take direct benefit from semantic networks (semantic paths distributions and texts clustering) are presented. However, 'flat' semantic networks that remain semantically correct are perhaps even more interesting because they may bridge standard networks with connectionnist networks. In such a perspective, Coloured Conceptual Graphs represent an intermediate but promising step.

The paper presents an approach for the dynamic generation of a complex structure of personalized web pages for learning purposes, reflecting the ontology of the considered domain. The need of assuring a holistic character for the body of knowledge induced in the learner's mind is emphasized. This is very important in the learning processes, especially nowadays, in the context of the huge amount of information available on the web and of its permanent evolution. The approach permits the adaptation of the content of the generated web pages to the incoming information from the web. New information is extracted, annotated and coherently integrated in the body of knowledge in order to keep the holistic character of the body of knowledge. Personalization is achieved by filtering the semantic network according to t he learner model, which keeps the list of concepts known or unknown by the learner. The approach was used in an EU INCO Copernicus project for computer aided language learning.

This paper addresses the role of g-band activity in semantic networks associated with the processing of words and sentences in humans. Magnetoencephalography (MEG) was used to compare the responses of eleven normal volunteers to semantically congruous and incongruous words at the end of syntactically correct sentences. The averaged low frequency responses evoked by the two word classes were clearly different within the latency range associated with N400 components. The oscillatory characteristics of the evoked responses were analysed using Gabor transform techniques in conjunction with statistical re-sampling. This revealed transient g-oscillations (|35 Hz) that were significantly phase-locked to both types of stimuli but preferentially present at intermediate (|300 ms) and long (.500 ms) latencies for incongruous words. This stimulus dependent phase locked g-activity occurred at latencies that were distinct from the short latency and evoked N400 components. The findings suggest that synchronised oscillations provide independent information about brain dynamics and that some semantic processes may dissociate into distinct functional stages.

The present study employed multidimensional scaling and ADDTREE clustering analyses to derive the cognitive maps and clustering representations of normal elderly controls (NC), patients with Alzheimer's disease (AD), and patients with Hun-tington's disease (HD); the analyses were performed on subjects' responses in a category fluency task that involved generating animal names for 60 sec. A measure of the proximity of animal names was used as an index of associational strength; MDS and ADDTREE estimates were based on this measure. A comparison of the NC, AD, and HD subjects' cognitive maps suggests that the semantic network of AD patients is abnormal in two ways. First, the organization of the semantic network is disrupted. Second, new abnormal associations and clusterings are formed. These results support the notion that AD is characterized by a breakdown in the structure of semantic knowledge and not primarily by a deficiency in the accessibility of semantic informa...

The Universal Networking Language (UNL) is an artificial language that can replicate human language functions in cyberspace in terms of hyper semantic networks. This paper aims to: a) design a reference grammar capable of dealing with the basic linguistic structures in order to act as a test-bed in automating translation between English and Arabic in both directions through UNL; b) evaluate the current state of the UNL system as an Interlingua in analyzing and generating English and Arabic as far as the reference structures are concerned. A reference parallel corpus of 500 structures was used. Results are promising; precision and recall of analyzing English to UNL (UNLization) are 0.979 and 0.96 respectively, while precision and recall of analyzing Arabic to UNL are 0.98 and 0.96 respectively. Precision and recall of generating English from UNL (NLization) are 0.97 and 0.96 respectively, while precision and recall of generating Arabic from UNL are 0.989 and 0.96 respectively.

This paper presents a topic map approach to PubMed in order to create a knowledge representation for this information system. PubMed is a free search engine that gives very full coverage of the related biomedical sciences. With more than 17 millions of citations since 1865, PubMed users have several problems to find the papers desired. So, it is necessary to organize these concepts in a semantic network. To achieve this objective, we use the Metamorphosis system, choosing the keywords from MeSH ontology. This way, we obtain an ontological index for PubMed, making easier to find specific papers.

Active visual based scene exploration as well as speech understanding and dialogue are important skills of a service robot which is employed in natural environments and has to interact with humans. In this paper we suggest a knowledge based approach for both scene exploration and spoken dialogue using semantic networks. For scene exploration the knowledge base contains information about camera movements and objects. In the dialogue system the knowledge base contains information about the individual dialogue steps as well as about syntax and semantics of utterances. In order to make use of the knowledge, an iterative control algorithm which has realtime and any-time capabilities is applied. In addition, we propose appearance based object models which can substitute the object models represented in the knowledge base for scene exploration. We show the applicability of the approach for exploration of office scenes and for spoken dialogues in the experiments. The integration of the multi-sensory input can easily be done, since the knowledge about both application domains is represented using the same network formalism.

In this paper we present a knowledge based approach to a speech understanding and dialog system which possesses any-time and real-time capabilities. Knowledge is represented by means of a semantic-network formalism; the concept-centered knowledge base is automatically compiled into a fine-grained network which allows an efficient exploitation of parallelism. The interpretation problem is defined as a combinatorial optimization problem and solved by means of iterative optimization methods. By using iterative methods, any-time capabilities are provided since after each iteration step a (sub-)optimal solution is always available. At the moment, the real-time factor for interpreting an initial user's utterance is 0.7.

Knowledge-based systems are data bases with more powerful front ends for dealing with the meaning of data. This paper discusses requirements for a conceptual schema that is general enough to support knowledge bases as well as ordinary data bases. It presents seven features that such a schema must support and evaluates various approaches to data base semantics in terms of them. The AI notations for semantic networks or conceptual graphs are highly general ones that can support all seven features.

This study used the technique of Event-Related Potentials (ERPs), specifically the component N400, to evaluate semantic functions in Alzheimer's disease (AD). ERPs of 10 patients with probable AD and 10 asymptomatic elderly control subjects were recorded during a semantic categorization task using pictures as stimuli, 50% of the stimuli were congruent and the other 50% were incongruent. Significant differences for the incongruent ERPs, with a diminished N400 component, was observed in the patients group whereas the amplitude of the congruent ERPs was similar in both groups. The characteristics of the N400 in the patients with probable AD suggest that there is a lack of efficiency in accessing information and shows that there are deficient associative connections within the semantic network.

INPE – Instituto Nacional de Pesquisas Espaciais, Caixa Postal 515, 12.245-970 São José dos Campos, SP, Brazil (*) hermann@ltid.inpe.br, (**) carolina@ltid.inpe.br ... KEY WORDS: Object-oriented classification, Quickbird satellite, Ikonos satellite, Remote Sensing, e-...

RDF-based P2P networks have a number of advantages compared to simpler P2P networks such as Napster, Gnutella or to approaches based on distributed indices on binary keys such as CAN and CHORD. RDF-based P2P networks allow complex and extendable descriptions of resources instead of fixed and limited ones, and they provide complex query facilities against these metadata instead of simple keyword-based searches. In this paper we will discuss RDF-based P2P networks like Edutella as a specific example of a new type of P2P networks -schema-based P2P networks -and describe the use of super-peer based topologies for these networks. Super-peer based networks can provide better scalability than broadcast based networks, and provide support for inhomogeneous schema-based networks, with different metadata schemas and ontologies (crucial for the Semantic Web). Based on (dynamic) metadata routing indices, stated in RDF, the superpeer network supports sophisticated routing and distribution strategies, as well as preparing the ground for advanced mediation and clustering functionalities.

The World Wide Web constitutes a large information space and a promising support for the development of data inference, analysis and mining mechanisms. The research presented in this paper introduces a modelling approach that derives a social network and computes some of its emerging properties, from the semantics exhibited from a domain knowledge embedded in a series of web pages. The framework is applied to the research communities related to a series of conferences in a given domain, and the university connections materialised by the researcher trajectories over time. The properties of the social network are analysed using graph-based measures, and a novel geographical dispersion coefficient.

An organized knowledge structure or knowledge base plays a vital role in retaining knowledge where data are processed and organized so that machines can understand. Instructive text (iText) consists of a set of instructions to accomplish a task or operation. Hence, iText includes a group of texts having a title or name of the task or operation and step-by-step instructions on how to accomplish the task. In the case of iText, storing only entities and their relationships with other entities does not always provide a solution for capturing knowledge from iTexts as it consists of parameters and attributes of different entities and their action based on different operations or procedures and the values differ for every individual operation or procedure for the same entity. There is a research gap in iTexts that created limitations to learn about different operations, capture human experience and dynamically update knowledge for every individual operation or instruction. This research pr...

Semantic memory is the cognitive system devoted to storage and retrieval of conceptual knowledge. Empirical data indicate that semantic memory is organized in a network structure. Everyday experience shows that word search and retrieval processes provide fluent and coherent speech, i.e. are efficient. This implies either that semantic memory encodes, besides thousands of words, different kind of links for different relationships (introducing greater complexity and storage costs), or that the structure evolves facilitating the differentiation between long-lasting semantic relations from incidental, phenomenological ones. Assuming the latter possibility, we explore a mechanism to disentangle the underlying semantic backbone which comprises conceptual structure (extraction of categorical relations between pairs of words), from the rest of information present in the structure. To this end, we first present and characterize an empirical data set modeled as a network, then we simulate a stochastic cognitive navigation on this topology. We schematize this latter process as uncorrelated random walks from node to node, which converge to a feature vectors network. By doing so we both introduce a novel mechanism for information retrieval, and point at the problem of category formation in close connection to linguistic and non-linguistic experience.

The work presented in this paper deals with the construction of a large-vocabulary semantic network to assist computerised speech or text recognition. The semantic network is systematically constructed with semantic information about nouns and verbs from the Longman Dictionary of Contemporary English by the application of pattern matching rules. It is represented in the form of a directed graph where nodes represent word senses and links represent the types of conceptual relationships. A semantic score, for pairwise combinations of word candidates in a speech recognition lattice, can be derived by traversing the network and calculating the conceptual distance between the senses of these candidates.

Geography-related terms are often used in Web search queries • Many user activities on the Web are directly related to the user's location • It is important to conceive applications that take into consideration this intention • Advances can enhance current information retrieval mechanisms -Allow people to perform local search -Enable geographically-focused advertising -Develop novel ranking strategies -Inferred by the location of its Web server

Alcohol expectancies appear in children before drinking begins and mediate the effects of other drinking antecedents. Hence, application of memory models to expectancies in children may increase understanding of drinking initiation. To this end, an instrument to assess children's expectancies within a memory network model was developed and administered to 470 children in 2nd-5th grades. Multidimensional scaling analyses revealed that children generally organize expectancy information along the same evaluative and arousal-sedation dimensions as do adults, and older children become more likely to expect positive and arousing outcomes from drinking. These results suggest a cognitive process that prepares children for drinking initiation during adolescence as well as new, theory-based approaches to prevention. As evidence has accumulated that alcohol expectancies can influence drinking and statistically mediate the effects of other drinking antecedents, efforts have begun to decipher the mechanism of this influence (see ; National Institute on Alcohol Abuse and Alcoholism [NIAAA], 1993, pp. 133-135). Beginning with Tolman (1932), expectancies have been closely linked to memory (e.g.. Holies, 1972;. Recent approaches to studying memory may be useful, therefore, for understanding how expectancies influence alcohol use. To date, memory approaches to relating expectancies to adult drinking have included theoretical state-

The latency of reading a single word is increased by 20 to 40 msec if another object is present in the display. The delay is affected by the spatial organization of the display: a colored frame causes less delay when it surrounds the word than when it is shown on the opposite side of fixation. A small gap in the frame is also more efficiently detected as a secondary task when the frame is around the word than when the two are spatially separate. The advantage of integrated over separate presentation suggests that a "filtering cost" is incurred when two distinct perceptual objects compete for attention. Attention in filtering tasks operates on perceptually distinct objects rather than on nodes in a semantic network.

This paper suggests a general perception-baaed theory of representation within the framework of the phenomenology of C. S. Pelrce (named by him "phaneroscopy") by means of the generalization of R. Wille's basic lattice-concepts theory of objects and their attributes. We first summarize Peirce's main categorization of all n-uric relations into three fundamental kinds: Firstness, Secondness and Tlfirdness {i.e., relations requiring monads, dyads and triads, respectively, in their definitions). His "reduction thesis" reduces all relations of higher adicity into these three kinds. We then use elementary Category Theory to develop "relation-structures" of concepts, relations and lfigher order relations, baaed entirely on experienced simple "qualities of feeling." A relational algebra restalts which includes semantic nets as relation-structures. In terms of this algebra we use Peirce's "reduction theorem" in order to build a "foliation" of all conceptual/relational-structures by men,is of levels ("sheets") algebraically defined. This provides a canonical "normal form" for networks of n-adic relations. Tiffs can be done to existing senumtic network formalisms to help make sense of phenomenologlcaily confused components. AnMogously to Wille's lattice-concepts theory connecting objects and attributes, we define "represcntatlon-contexts" connecting two corresponding classes of phenomena formalized in terms of Category Theory by diagrmns in a category we call relational structures provided with natural trmlsformations as morplfisnm. This leads us to a follated conception of semantic networks with earl concept a~ld relation a~signed to a particular phenomenologlcal level. Tlnm a foliated network represents not only a state of tldngs but also the mode of connection of the network with the state of things. One consequence of foliation is that we now have a method for relational subsumption using the generallzation-hierarchy of relations. In addition to the insight afforded by this formal analysis, we also obtain a lattice of representationrelations which may be computationally used as an IS-A hierarchy sub-element for the purpose of m,tomatic ilfference, in all subject-domains involving representation. 3p, eferences to CP are followed by volume and paragraph numbers; they refer to The Collected Papers of Charles $. Peiree.

In contrast to the one-dimensional structure of natural language, images consist of two-or three-dimensional structures. This contrast in dimensionality causes the mapping between words and images to be a challenging, poorly understood and undertheorized task. In this paper, we present a general theoretical framework for semantic visual abstraction in massive image databases. Our framework applies specifically to facial identification and visual search for such recognition. It accommodates the by now commonplace observation that, through a graph-based visual abstraction, language allows humans to categorize objects and to provide verbal annotations to shapes. Our theoretical framework assumes a hidden layer between facial features and the referencing of expressive words. This hidden layer contains key points of correspondence that can be articulated mathematically, visually or verbally. A semantic visual abstraction network is designed for efficient facial recognition in massive visual datasets. In this paper, we demonstrate how a two-way mapping of words and facial shapes is feasible in facial information retrieval and reconstruction.

Extracting knowledge from text has long been a goal of AI. Initial approaches were purely logical and brittle. More recently, the availability of large quantities of text on the Web has led to the development of machine learning approaches. However, to date these have mainly extracted ground facts, as opposed to general knowledge. Other learning approaches can extract logical forms, but require supervision and do not scale. In this paper we present an unsupervised approach to extracting semantic networks from large volumes of text. We use the TextRunner system [1] to extract tuples from text, and then induce general concepts and relations from them by jointly clustering the objects and relational strings in the tuples. Our approach is defined in Markov logic using four simple rules. Experiments on a dataset of two million tuples show that it outperforms three other relational clustering approaches, and extracts meaningful semantic networks.

We present a computer-based model of acquisition of word meaning from context. The model uses semantic role assignments to search through a hierarchy of conceptual information for an appropriate meaning for an unknown word. The implementation of this approach has led to many surprising similarities with work in modelling human language acquisition. We describe the learning task and the model, then present an empirical test and discuss the relationships between this approach and the work in psycholinguistics.

One of the fundamental problems in the theory of knowledge representation is the diculty of achieving both logical coherence and computational tractability. We present steps toward a theory of access-limited logic, in which access to assertions in the knowledgebase is constrained by semantic network style locality relations. Where a classical deductive method or logic programming language would retrieve all assertions that satisfy a given pattern, an access-limited logic retrieves all assertions reachable by following an available access path. The complexity of inference is thus independent of the size of the knowledge-base and depends only on its local connectivity. Access-Limited Logic, though incomplete, still has a well de ned semantics and a weakened form of completeness (`Socratic Completeness') and is complete in some important special cases.

One of the fundamental problems in the the-ory of knowledge representation is the di - culty of achieving both logical coherence and computational tractability. We present steps toward a theory of access-limited logic, in which access to assertions in the knowledge-base is ...

We present in this paper an on-going research: the construction and annotation of a Romanian Generative Lexicon (RoGL), following the generative lexicon theory. Our system follows the specifications of CLIPS project for Italian language. It contains a corpus, an ontology of semantic types, a graphical interface and a database from which we generate data in XML format. We describe here the graphical interface structure as well as functionality and the annotation procedure.

This research analyses the critical factors influencing employee satisfaction and retention in the context of the Great Resignation by
exploring the key organizational elements that significantly impact employee contentment and loyalty. The justification for this study lies in
the need for organizations to understand and address the issues leading to high turnover rates and employee dissatisfaction, which have been
exacerbated by recent global workforce changes. The main objectives are to identify the key factors that contribute to employee satisfaction and
to provide insights into how organizations can enhance their work environments, career growth opportunities, and management practices to
retain talent. The methodology involves a semantic network analysis of 138,764 employee reviews from 136 companies, collected via Glassdoor
between 2021 and 2023. These reviews were analyzed using the Semantic Networks Tool to identify recurring themes related to work environment, career growth, workload, and management practices. The main results indicate that both current and former employees highly value
a supportive and positive work environment and career growth opportunities. However, significant disadvantages include overwork, long
hours, and poor management practices. The study highlights that the critical factors influencing employee satisfaction and retention include a
positive work environment, opportunities for career growth, and effective management practices.

Multiple thinking of different stakeholders has to influence collaborative working. Some part of information exchange is fragment knowledge that is a significant challenge to complete knowledge co-creation from various domains. However, an effective obstacle is miscommunication among the stakeholders, particularly when ambiguous terms are mentioned in the discussion contexts. To overcome the challenge, this paper proposes an integration approach of network text analysis and knowledge graph embedding. The approach is employed for understanding semantic meaning of terms from a source of knowledge, as a discussion forum. We calculate each term detected by our cross-domain codebook onto the vector space and straightforwardly investigate the relationship among questions and answers on it. To demonstrate the benefits of employing the approach, the system’s functionality is implemented to manifest a capability of detecting and reducing miscommunication by the case study of Life Cycle Asses...

Generalized formalization of recognition algorithm for specialized computer systems is presented in this paper. The structure features of image recognition methods, which have to be taken into account when developing classifiers for object recognition in specialized computer systems, are described. The fundamental types of images characteristics-features, which are used in various methods of image recognition, are discussed. Approaches to development of classifiers for recognizing robotization objects, which are implemented on basis of Haar classifiers, are discussed. The issues of using machine learning algorithm of adaptive gain AdaBoost for development of such classifiers are also considered. Utilities have been developed for implementation of classifiers for object recognition in specialized computer systems.

Abstract: IntroductionPeople often encounter objects that are perceptually indistinguishablefrom objects that they have seen before. Whenthis happens, how do they decide whether the object theyare looking at is something never before seen, or if it is thesame one they encountered before? To identify these objectspeople surely use background knowledge and contextualcues. We propose a computational theory of identifyingperceptually indistinguishable objects(PIOs)

Abnormal activation of semantic networks may be the underlying basis for thought disorder (ThD) in patients with schizophrenia. However, whether this abnormality consists of increased or decreased activation is unclear. Previous behavioral and electrophysiological (N400) data have failed to yield consistent evidence for clarifying this hypothesis. One of the eventrelated potentials (ERP) components (the recognition potential, RP), presumably reflecting activation of specific semantic networks involved in ThD, has not been used to studies of schizophrenia, and may add additional information to support or refute this hypothesis. In an initial experiment, RP amplitude was reduced in schizophrenic patients, but particularly in those with ThD. In a second experiment, in addition to reduced overall RP amplitude, concrete words were shown to have higher amplitudes than abstract words in both patients and controls, supporting a relative integrity of the lexicon in ThD patients. RP reduction suggests that a fewer number of networks' elements are activated when an incoming word is processed, thus supporting hypoactivation of the semantic networks as the basis for ThD.

Contextual search tries to better capture a user's informa- tion need by augmenting the user's query with contextual information extracted from the search context (for example, terms from the web page the user is currently reading or a file the user is currently editing). This paper presents Y!Q—a first of its kind large-scale contextual search system—and provides an overview of its system design and architecture. Y!Q solves two major prob- lems. First, how to capture high quality search context. Second, how to use that context in a way to improve the relevancy of search queries. To address the first problem, Y!Q introduces an informa- tion widget that captures precise search context and pro- vides convenient access to its functionality at the point of inspiration. For example, Y!Q can be easily embedded into web pages using a web API, or it can be integrated into a web browser toolbar. This paper provides an overview of Y!Q's user interaction design, highlighting its no...

Animating the relationship between affect and ideology in histories of artificial intelligence, this paper explores how the transatlantic postwar quest to engineer common sense via computational means has profoundly shaped both the social logics of machine learning systems and the sensorial politics of everyday knowledge production. Focusing on the Cyc project, a logic-based AI endeavour to 'codify human common sense' which began at the USA-based Microelectronics and Computer Technology Corporation in 1984, and making links to MIT Media Lab's Open Mind Common Sense Project inaugurated in 1999, I trace how the imperative within late twentieth-century computer science to make intelligent systems more intuitive by translating implicit human knowledge into explicit machine knowledge involved not only mathematical and technological challenges but also affective, ideological and socio-political ones. In tracking the interactions between intuition and common sense across these genealogies of machine intelligence, I tease out some of the key atmospheres, processes, and correlations via which AI technologies have become embedded with ideology, normativity and prejudice at the levels of logic, procedure and data. Through adjudicating the meanings of reason, truth and perceptibility as matters of algorithmically calibrated fit and popularity, intelligent architectures are also radically reconstituting the intelligible and the sensiblein ways, I argue, that complicate any notion of a clean epistemological or ontological break between first and second wave AI. Dwelling within Pedwell, Carolyn (in press, 2024), 'The Intuitive and the Counter-Intuitive: AI and the Affective Ideologies of Common Sense, New Formations. 2 these unfinished histories, however, also points to how inhabiting counter-intuitive tendencies may open up new possibilities for (un)common sense and distributed intuition within computational cultures.

In the aim of natural language processing applications improvement, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource EuroWordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules modelOur proposed indexing approach can be applied to text documents in various languages. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing a...

In this paper, the authors propose formalism for representing a knowledge base (KB) by network. The objective is to achieve a high coverage of this base. This type of network is similar to the semantic network with the difference that the arcs are quantified by a value indicating the semantic proximity between the concepts. This semantic proximity presents taxonomic relations, synonyms, and non-taxonomic relations (contextual relations). This latter are discovered based on the association rules model. This model is based on (i) indexing method (ii) the French lexical database EuroWordNet (EWNF) and (iii) the Apriori algorithm. The contextual relations are the latent relations buried in the KB, carried by the semantic context. Evaluating our representation formalism shows better result about 80% of coverage of the KB.

Objectives: Current search engines are based on keyword queries, which are not natural for the user Further, the user often has to dig within each page to find the answer. It is also difficult for the common user to judge the quality of a document retrieved on the Internet? This paper presents a solution using an existing question answering system and investigates whether the quality of the answers extracted depends on the quality of the health web pages analysed. Design: This paper presents two separate evaluations of the QA system developed by HON. The first evaluation is systematic and validates the effectiveness of our system. The second focuses on the use of QA for site selection of quality health care, (those with HONcode) or any site on the Web. Measurements: Results are merged and classify by a medical expert (in A =relevant, B = lead to an answer, C = not relevant). We use Trec eval for the evaluation of our system. Results: Systematic evaluation: For a set of 100 questions, 70 are well answered. Qualitative evaluation: For a set of 100 questions, we obtain a MAP of 59% and a MRR of 76% for QAHON_honcode. Conclusion: According to our results the trustworthiness of the database used influence the quality and accuracy of the answers retrieved by the HON question answering system.

As our ability to store information increases, the mechanisms we employ to access that information become ever more important. In this paper, we present Archosum, a prototype of an organizational system that attempts to encapsulate the benefits of both hierarchical and keyword systems. By introducing abstract entities, Archosum provides a simple interface with which users can build and maintain powerful relationship-based organizations. We compared Archosum to both hierarchy and keyword based systems in a user study. Through our users study we begin to expose some of the advantages and disadvantages to three approaches to designing an organizational system. Furthermore, we begin to consider how organizational systems will work when distinct users create organizations for collections and how sharing might be facilitated using Archosum.

The semantic information in videos is useful for content-based video retrieval and summarization. Traditional image/video understanding is formulated in terms of low-level features describing the structure and intensity of the input image/video. How to generate the high-level knowledge such as common sense and human perceptual knowledge is one of the most difficult problems This paper attempts to bridge this gap through the integration of image analysis algorithms and multi-level Semantic Network (SN) to interpret the semantic meaning of the baseball video.

This paper introduces a highlight detection method that consists of (1) inference scheme for semantic feature extraction, and (2) a semantic highlight detection scheme using a Multi-level Semantic Network (MSN). MSN is basically a Bayesian Belief Network (BBN) which consists of the low-level feature nodes connected to the mid-level semantic nodes and finally the high-level semantic nodes. A probabilistic structure can be combined by visual features and can be applied on highlight detection. Satisfactory results have been shown in the end of this paper. Highlights Non-highlights