Constructing a Design Knowledge Base Using Natural Language Processing

2006 ◽  
Author(s):  
V. Sundararajan
Keyword(s):  
Research And Development ◽  
Knowledge Base ◽  
Language Processing ◽  
Mechanical Engineering ◽  
Discourse Structure ◽  
Design Knowledge ◽  
Prototype System ◽  
Figures Of Speech ◽  
Part Of Speech ◽  
Engineering Designs

Mechanical engineering, like other engineering disciplines, has witnessed maturation of various aspects of its domain, obsolescence of some areas and a resurgence of others. With a history of over 200 years of continuous research and development, both in academia and industry, the community has generated enormous amounts of design knowledge in the form of texts, articles and design drawings. With the advent of electronics and computer science, several of the classical mechanisms faced obsolescence, but with the emergence of MEMS and nanotechnology, the same designs are facing a resurrection. Research and development in mechanical engineering would derive enormous benefit from a structured knowledge-base of designs and mechanisms. This paper describes a prototype system that synthesizes a knowledge-base of mechanical designs by the processing of the text in engineering descriptions. The goal is to construct a system that stores and catalogs engineering designs, their sub-assemblies and their super-assemblies for the purposes of archiving, retrieval for launching new designs and for education of engineering design. Engineering texts have a relatively clear discourse structure with fewer ambiguities, less stylistic variations and less use of complex figures of speech. The text is first passed through a part-of-speech tagger. The concept of thematic roles is used to link different parts of the sentence. The discourse structure is then taken into account by anaphora resolution. The knowledge is gradually built up through progressive scanning and analysis of text. References, interconnections and attributes are added or deleted based upon the nature, reliability and strength of the new information. Examples of analysis and resulting knowledge structures are presented.

scholarly journals Smart I’rab: Smart Aplicasion for Arabic Grammar Learning

2013 ◽  
Vol 1 (1) ◽  
Author(s):  
Syd. Ali Zein Farmadi ◽  
Ali Ridho Barakbah ◽  
Entin Martiana Kusumaningtyas
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Language Processing ◽  
Inference Engine ◽  
Word Formation ◽  
Input Word ◽  
Subject Pronouns ◽  
Part Of Speech ◽  
Word Position ◽  
Word Function

Arabic grammar, known as nahwu, is necessary to comprehend the Holy Qur’an that is completely written in Arabic. However, many people get trouble to study this skill because there are various kinds of word formation and sentences that may be created from a single verb, noun, adjective, subject, predicate, object, adverb or another formation. This research proposes a new approach to identify the position and word function in Arabic sentence. The approach creates smart process that employs Natural Language Processing (NLP) and expert system with modeling based on knowledge and inference engine in determining the word position. The knowledge base determines the part of speech while the inference engine shows the word function in the sentence. On processing, the system uses 82 templates consisting of 34 verb templates, 34 subject pronouns, 14 pronouns for object or possessive word. All the templates are in the form of char array for harakat (vowel) and letters which become the comparators for determining the part of speech from input word sentence. Output from the system is an i’rab (the explanation of word function in sentence) written in Arabic. The system has been tested for 159 times to examine word and sentence. The examination for word that is done 117 times has not made any error except for the word that is really like another word. While the detection for word function in sentence that is done 42 times experiment, there is no error too. An error happens when the part of speech from the word being examined is not included in the system yet, influencing the following word function detection.Keywords: I’rab, Arabic grammar, NLP, expert system, knowledge base, inference engine

An Automatic Question Generation System using Rule-Based Approach in Bloom’s Taxonomy

2019 ◽  
Vol 13 ◽  
Author(s):  
G Deena ◽  
K Raja ◽  
K Kannan
Keyword(s):  
Language Processing ◽  
Learning Process ◽  
Question Generation ◽  
Test Question ◽  
Rule Based ◽  
Part Of Speech ◽  
Core Idea ◽  
Rule Based Approach ◽  
Teaching Learning ◽  
Automatic Question Generation

: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

scholarly journals Generation of Cross-Lingual Word Vectors for Low-Resourced Languages Using Deep Learning and Topological Metrics in a Data-Efficient Way

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1372
Author(s):  
Sanjanasri JP ◽  
Vijay Krishna Menon ◽  
Soman KP ◽  
Rajendran S ◽  
Agnieszka Wolk
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Semantic Space ◽  
Semantic Interpretation ◽  
Learning Approaches ◽  
Qualitative Comparison ◽  
Bilingual Dictionary ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Cross Lingual

Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.

Personalized knowledge push system based on design intent and user interest

2015 ◽  
Vol 230 (11) ◽  
pp. 1757-1772 ◽  
Author(s):  
Zhan-Song Wang ◽  
Ling Tian ◽  
Yuan-Hao Wu ◽  
Bei-Bei Liu
Keyword(s):  
Knowledge Acquisition ◽  
Conceptual Design ◽  
Current Knowledge ◽  
Target Function ◽  
Design Knowledge ◽  
Prototype System ◽  
Sorting Algorithm ◽  
Illustrative Case ◽  
User Interest ◽  
Design Intent

Existing knowledge provides important reference for designers in mechanical design activities. However, current knowledge acquisition methods based on information retrieval have the problem of inefficiency and low precision, which mainly meet the requirement for knowledge coverage. To improve the efficiency of knowledge acquisition and ensure the availability of design knowledge, this paper proposes a knowledge push service method based on design intent and user interest. First, the design intent model, which is mainly the formal expression of the target function of conceptual design, is built. Second, the user interest model that consists of domain themes and operation logs is built, and an automatic updating method of user interest is proposed. Third, a matching method of design knowledge based on design intent, and a sorting algorithm of knowledge candidates based on user interest are proposed to realize personalized knowledge active push service. Finally, a prototype system called Personalized Knowledge Push System for Mechanical Conceptual Design (MCD-PKPS) is implemented. An illustrative case demonstrates that the proposed method can successfully improve the efficiency and availability of knowledge acquisition.

The Verbmobil prototype system – a software engineering perspective

1999 ◽  
Vol 5 (1) ◽  
pp. 95-112 ◽  
Author(s):  
THOMAS BUB ◽  
JOHANNES SCHWINN
Keyword(s):  
Software Engineering ◽  
Computational Linguistics ◽  
Language Processing ◽  
System Integration ◽  
Evolutionary Process ◽  
Prototype System ◽  
Functional Architecture ◽  
Seamless Integration ◽  
Extensible Software ◽  
Transfer Problems

Verbmobil represents a new generation of speech-to-speech translation systems in which spontaneously spoken language, speaker independence and adaptability as well as the combination of deep and shallow approaches to the analysis and transfer problems are the main features. The project brought together researchers from the fields of signal processing, computational linguistics and artificial intelligence. Verbmobil goes beyond the state-of-the-art in each of these areas, but its main achievement is the seamless integration of them. The first project phase (1993–1996) has been followed up by the second project phase (1997–2000), which aims at applying the results to further languages and at integrating innovative telecooperation techniques. Quite apart from the speech and language processing issues, the size and complexity of the project represent an extreme challenge on the areas of project management and software engineering:[bull ] 50 researchers from 29 organizations at different sites in different countries are involved in the software development process,[bull ] to reuse existing software, hardware, knowledge and experience, only a few technical restrictions could be given to the partners.In this article we describe the Verbmobil prototype system from a software-engineering perspective. We discuss:[bull ] the modularized functional architecture,[bull ] the flexible and extensible software architecture which reflects that functional architecture,[bull ] the evolutionary process of system integration,[bull ] the communication-based organizational structure of the project,[bull ] the evaluation of the system operational by the end of the first project phase.

scholarly journals A Cascaded Unsupervised Model for PoS Tagging

2021 ◽  
Vol 20 (1) ◽  
pp. 1-23
Author(s):  
Necva Bölücü ◽  
Burcu Can
Keyword(s):  
Linear Model ◽  
Language Processing ◽  
Bayesian Model ◽  
Linear Models ◽  
Syntactic Category ◽  
Semantic Parsing ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Sentence Level ◽  
Log Linear

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

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