Semantics representation in a sentence with concept relational model (CRM)

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefi ned mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction

Sistemas informáticos e conhecimento organizacional : uma reinterpretação dos papeis desempenhados pelos sistemas informáticos nas organizações

Nem sempre é fácil compreender o papel desempenhado pelos sistemas informáticos (aplicações das tecnologias da informação) nas organizações ou atribuir significado às operações por elas realizadas. Esta dificuldade resulta da complexidade daqueles dispositivos, da proliferação de designações pouco explícitas porque frequentemente orientadas a fins comerciais e ainda à variedade de tarefas que, aos poucos e poucos, os sistemas informáticos têm vindo a assumir nas organizações. As tentativas de produzir taxionomias que facilitem a classificação dos sistemas informáticos e clarifiquem os papeis por eles desempenhados não esgotaram ainda esta problemática. A proposta apresentada neste artigo constitui uma nova maneira de olhar para o papel desempenhado pelos sistemas informáticos nas organizações centrada no apoio dado às actividades de criação, armazenamento e utilização de conhecimento organizacional. Para tal, é utilizada uma classificação de conhecimento organizacional inspirada no trabalho de Mario Bunge (Treatise on Basic Philosophy) e que distingue entre conhecimento comportamental, perceptual e conceptual. Esta distinção é utilizada para explicar os processos de criação e utilização de conhecimento e para explicar o suporte que os sistemas informáticos dão a esses processos, reintrepretando assim as explicações dadas à luz de outros enquadramentos teóricos. Conclui-se chamando a atenção para a semelhança entre os processos de criação de conhecimento organizacional e de criação de conhecimento científico e pela possibilidade de os sistemas informáticos poderem ser vistos como amplificadores das capacidades cognitivas dos agentes organizacionais

What conceptual graph workbenches need for natural language processing

An important capability of the conceptual graph knowledge engineering tools now under development will be the transformation of natural language texts into graphs (conceptual parsing) and its reverse, the production of text from graphs (conceptual generation). Are the existing basic designs adequate for these tasks? Experience developing the BEELINE system's natural language capabilities suggests that good entry/editing tools, a generous but not unlimited storage capacity and efficient, bidirectional lexical access techniques are needed to support the supply of data structures at both the linguistic and conceptual knowledge levels. An active formalism capable of supporting declarative and procedural programs containing both linguistic and knowledge level terms is also important. If these requirements are satisfied, future text-readers can be included as part of a conceptual knowledge workbench without unexpected problems

Performing fusion of news reports through the construction of conceptual graphs

As events occur around the world, different reports about them will be posted on various web portals. Different news agencies write their own report based on the information obtained by its reports on site or through its contacts – thus each report may have its own ‘unique’ information. A per- son interested in a particular event may read various reports about that event from different sources to get all the available information. In our research, we are attempting to fuse all the different pieces of information found in the different reports about the same event into one report – thus providing the user with one document where he/she can find all the information related to the event in question. We attempt to do this by constructing conceptual graph representations of the different news reports, and then merging those graphs together. To evaluate our system, we are building an operational system which will display on a web portal fused reports on events which are currently in the news. Web users can then grade the system on its effectiveness.peer-reviewe

Graph based text representation for document clustering

Advances in digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing and digital library. This phenomenon raises awareness for the requirement of effective techniques that can help during the search and retrieval of text. One of the most needed tasks is clustering, which categorizes documents automatically into meaningful groups. Clustering is an important task in data mining and machine learning. The accuracy of clustering depends tightly on the selection of the text representation method. Traditional methods of text representation model documents as bags of words using term-frequency index document frequency (TFIDF). This method ignores the relationship and meanings of words in the document. As a result the sparsity and semantic problem that is prevalent in textual document are not resolved. In this study, the problem of sparsity and semantic is reduced by proposing a graph based text representation method, namely dependency graph with the aim of improving the accuracy of document clustering. The dependency graph representation scheme is created through an accumulation of syntactic and semantic analysis. A sample of 20 news group, dataset was used in this study. The text documents undergo pre-processing and syntactic parsing in order to identify the sentence structure. Then the semantic of words are modeled using dependency graph. The produced dependency graph is then used in the process of cluster analysis. K-means clustering technique was used in this study. The dependency graph based clustering result were compared with the popular text representation method, i.e. TFIDF and Ontology based text representation. The result shows that the dependency graph outperforms both TFIDF and Ontology based text representation. The findings proved that the proposed text representation method leads to more accurate document clustering results

HOW CAN ONE EVALUATE A CONVERSATIONAL SOFTWARE AGENT FRAMEWORK?

This paper presents a critical evaluation framework for a linguistically orientated conversational software agent (CSA) (Panesar, 2017). The CSA prototype investigates the integration, intersection and interface of the language, knowledge, and speech act constructions (SAC) based on a grammatical object (Nolan, 2014), and the sub-model of belief, desires and intention (BDI) (Rao and Georgeff, 1995) and dialogue management (DM) for natural language processing (NLP). A long-standing issue within NLP CSA systems is refining the accuracy of interpretation to provide realistic dialogue to support the human-to-computer communication. This prototype constitutes three phase models: (1) a linguistic model based on a functional linguistic theory – Role and Reference Grammar (RRG) (Van Valin Jr, 2005); (2) Agent Cognitive Model with two inner models: (a) knowledge representation model employing conceptual graphs serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts (Wooldridge, 2013) and intentionality (Searle, 1983) and rational interaction (Cohen and Levesque, 1990); and (3) a dialogue model employing common ground (Stalnaker, 2002). The evaluation approach for this Java-based prototype and its phase models is a multi-approach driven by grammatical testing (English language utterances), software engineering and agent practice. A set of evaluation criteria are grouped per phase model, and the testing framework aims to test the interface, intersection and integration of all phase models and their inner models. This multi-approach encompasses checking performance both at internal processing, stages per model and post-implementation assessments of the goals of RRG, and RRG based specifics tests. The empirical evaluations demonstrate that the CSA is a proof-of-concept, demonstrating RRG’s fitness for purpose for describing, and explaining phenomena, language processing and knowledge, and computational adequacy. Contrastingly, evaluations identify the complexity of lower level computational mappings of NL – agent to ontology with semantic gaps, and further addressed by a lexical bridging consideration (Panesar, 2017)

Conceptual graph formalism for financial text representation

We present an approach to automatically transform a financial text into conceptual graph formalism.The approach exploits the constituent structure of sentences and general English grammar rules to perform the transformation.We suggest face validation and traces as the evaluation method to be performed on the resulting formalism to validate its accuracy. We also discuss the potential manipulation and application of the constructed conceptual graph database

Motivating a linguistically orientated model for a conversational software agent

This paper proposes a linguistically orientated model of a conversational software agent (CSA) (Panesar, 2017) framework sensitive to natural language processing (NLP) concepts and the levels of adequacy of a functional linguistic theory. We discuss the relationship between natural language processing and knowledge representation (KR), and connect this with the goals of a linguistic theory (Van Valin and LaPolla, 1997), in particular Role and Reference Grammar (RRG) (Van Valin Jr, 2005a). We discuss the advantages of RRG and fitness-for-purpose for computational implementation and its level of computational adequacy (Nolan, 2004). We propose a design of a computational model of the linking algorithm that utilises a speech act construction as a grammatical object (Nolan, 2014a, Nolan, 2014b) and the sub-model of belief-desire and intentions (BDI) (Rao and Georgeff, 1995). This model has been successfully implemented in software (Panesar, 2017, Pokahr et al., 2014), using conceptual graphs, and resource description framework (RDF), and we highlight some implementation issues that arose at the interface between language and knowledge representation

Dissimilarity algorithm on conceptual graphs to mine text outliers

The graphical text representation method such as Conceptual Graphs (CGs) attempts to capture the structure and semantics of documents.As such, they are the preferred text representation approach for a wide range of problems namely in natural language processing, information retrieval and text mining.In a number of these applications, it is necessary to measure the dissimilarity (or similarity) between knowledge represented in the CGs.In this paper, we would like to present a dissimilarity algorithm to detect outliers from a collection of text represented with Conceptual Graph Interchange Format (CGIF).In order to avoid the NP-complete problem of graph matching algorithm, we introduce the use of a standard CG in the dissimilarity computation.We evaluate our method in the context of analyzing real world financial statements for identifying outlying performance indicators.For evaluation purposes, we compare the proposed dissimilarity function with a dice-coefficient similarity function used in a related previous work.Experimental results indicate that our method outperforms the existing method and correlates better to human judgements. In Comparison to other text outlier detection method, this approach managed to capture the semantics of documents through the use of CGs and is convenient to detect outliers through a simple dissimilarity function.Furthermore, our proposed algorithm retains a linear complexity with the increasing number of CGs

Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function

The rapid increase in the amount of textual data has brought forward a growing research interest towards mining text to detect deviations. Specialized methods for specific domains have emerged to satisfy various needs in discovering rare patterns in text. This paper focuses on a graph-based approach for text representation and presents a novel error tolerance dissimilarity algorithm for deviation detection. We resolve two non-trivial problems, i.e. semantic representation of text and the complexity of graph matching. We employ conceptual graphs interchange format (CGIF) – a knowledge representation formalism to capture the structure and semantics of sentences. We propose a novel error tolerance dissimilarity algorithm to detect deviations in the CGIFs. We evaluate our method in the context of analyzing real world financial statements for identifying deviating performance indicators. We show that our method performs better when compared with two related text based graph similarity measuring methods. Our proposed method has managed to identify deviating sentences and it strongly correlates with expert judgments. Furthermore, it offers error tolerance matching of CGIFs and retains a linear complexity with the increasing number of CGIFs