Semantic Interpretation of Natural Language User Input to Improve Search in Multimedia Knowledge Base (Semantische Interpretation einer Benutzer-Eingabe in natürlicher Sprache für eine verbesserte Suche in einer multimedialen Wissensdatenbank)

2007 ◽  
Vol 49 (1) ◽  
Author(s):  
Serge Linckels ◽  
Christoph Meinel
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Keyword Search ◽  
Semantic Interpretation ◽  
Semantic Query ◽  
User Input ◽  
Background Theory ◽  
Multimedia Resources ◽  
Language User ◽  
Semantic Search Engine

In this article we present an e-librarian service which is able to retrieve multimedia resources from a knowledge base in a more efficient way than by browsing through an index or by using a simple keyword search. Our premise is that more pertinent results would be retrieved if the e-librarian service had a semantic search engine which understood the sense of the user's query. We explored the approach to allow the user to formulate a complete question in natural language.We present our background theory, which is composed of three steps. Firstly, there is the linguistic pre-processing of the user question. Secondly, there is the semantic interpretation of the user question into a logical and unambiguous form, i. e., ALC terminology. Thirdly, there is the generation of a semantic query, and the retrieval of pertinent documents.The background theory was implemented in two prototypes. We report on experiments that confirm the feasibility, the quality and the benefits of such an e-librarian service. From 229 different user questions, the system returned for 97 answer, and for nearly half of the questions only one answer (the best one).

Applying NLP Metrics to Students’ Self-Explanations

2012 ◽  
pp. 261-275 ◽  
Author(s):  
G. Tanner Jackson ◽  
Danielle S. McNamara
Keyword(s):  
Natural Language ◽  
Great Success ◽  
Abstract Concepts ◽  
Tutoring Systems ◽  
Unknown Variable ◽  
Learner Model ◽  
User Input ◽  
Individual Knowledge ◽  
Course Material ◽  
Language User

Intelligent Tutoring Systems (ITSs) are becoming an increasingly common method for students to engage with and learn course material. ITSs are designed to provide students with one-on-one learning that is tailored to their own pace and needs. These systems can adapt to each users’ individual knowledge and ability level to provide the most pedagogically effective learning environment. Tutoring systems have been designed that cover a variety of topics, including both well-defined and ill-defined domains. ITSs have seen great success within well-defined domains, where the topic itself provides only a limited set of responses. For example, in the domain of algebra, there is a limited set of possible actions that can be performed to solve for an unknown variable. Knowing this complete set of actions allows the tutoring system to predict all possible responses from the user. In contrast, ill-defined domains are more abstract and open ended. Reading comprehension is an ill-defined, open ended domain that can incorporate text from any subject, and involve numerous processes and problems for the learner. The number of associations that learners can make with a given text (e.g., based on personal memories, previous courses, ideas within different parts of the same text, etc.) is virtually infinite. These associations make it almost impossible to predict how a user will respond to a text. In addition to working with more abstract concepts, ITSs within ill-defined domains often have the added challenge of interpreting natural language user input. Incorporating natural language allows learners to use their own words and ideas as they interact with the content; however, this also increases the ambiguity of the interaction and decreases the system’s ability to build a precise model of the learner. Building an accurate learner model is essential for the system to adapt the interaction in a pedagogically appropriate manner.

scholarly journals Chatbot in Bahasa Indonesia using NLP to Provide Banking Information

2020 ◽  
Vol 14 (1) ◽  
pp. 91
Author(s):  
Abidah Elcholiqi ◽  
Aina Musdholifah
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Product Information ◽  
Cosine Similarity ◽  
User Input ◽  
Similarity Algorithm ◽  
Bahasa Indonesia ◽  
User Queries

FAQs are mostly provided on the company's website to inform their service and product. It's just that the FAQ is usually less interactive and presents too much information that is less practical. Chatbot can be used as an alternative in providing FAQ. In this study, chatbots were developed for BTPN in providing information about their products, namely Jenius. Chatbot developed utilizes natural language processing so that the system can understand user queries in the form of natural language. The cosine similarity algorithm is used to find similarities between queries and patterns in the knowledge base. Patterns with the highest cosine values are considered to be most similar to user queries. It's just that, this algorithm does not pay attention to the structure of the sentence so that it adds checking the structure of the sentence with the parse tree to give weight to the pattern. This chatbot application has been tested by 10 users and it was found that the suitability of the answers with user input was 84%. Therefore the chatbot developed can be used by BTPN to provide Jenius product information to consumers more interactively and practically.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 5. Introduction to Logic

Discourse ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 109-117
Author(s):  
O. M. Polyakov
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Logic ◽  
Natural Language ◽  
Semantic Interpretation ◽  
General View ◽  
World Model ◽  
Semantic Approach ◽  
Natural Languages ◽  
The World ◽  
Linguistic Aspect

Introduction. The article continues the series of publications on the linguistics of relations (hereinafter R–linguistics) and is devoted to an introduction to the logic of natural language in relation to the approach considered in the series. The problem of natural language logic still remains relevant, since this logic differs significantly from traditional mathematical logic. Moreover, with the appearance of artificial intelligence systems, the importance of this problem only increases. The article analyzes logical problems that prevent the application of classical logic methods to natural languages. This is possible because R-linguistics forms the semantics of a language in the form of world model structures in which language sentences are interpreted.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of logic and semantics, the formulated concept of the interpretation operator is used.Results and discussion. The problems that arise when studying the logic of natural language in the framework of R–linguistics are analyzed. These issues are discussed in three aspects: the logical aspect itself; the linguistic aspect; the aspect of correlation with reality. A very General approach to language semantics is considered and semantic axioms of the language are formulated. The problems of the language and its logic related to the most General view of semantics are shown.Conclusion. It is shown that the application of mathematical logic, regardless of its type, to the study of natural language logic faces significant problems. This is a consequence of the inconsistency of existing approaches with the world model. But it is the coherence with the world model that allows us to build a new logical approach. Matching with the model means a semantic approach to logic. Even the most General view of semantics allows to formulate important results about the properties of languages that lack meaning. The simplest examples of semantic interpretation of traditional logic demonstrate its semantic problems (primarily related to negation).

Automatic Ontology Learning from Multiple Knowledge Sources of Text

2018 ◽  
Vol 14 (2) ◽  
pp. 1-21 ◽  
Author(s):  
B Sathiya ◽  
T.V. Geetha
Keyword(s):  
Knowledge Base ◽  
Web Pages ◽  
Knowledge Sources ◽  
Statistical Measure ◽  
Ontology Learning ◽  
Web Page ◽  
Semantic Query ◽  
Probabilistic Knowledge ◽  
Discovery Algorithms ◽  
Different Sources

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

scholarly journals Drawing dynamic geometry figures online with natural language for junior high school geometry

2012 ◽  
Vol 13 (5) ◽  
pp. 126 ◽  
Author(s):  
Wing-Kwong Wong ◽  
Sheng-Kai Yin ◽  
Chang-Zhe Yang
Keyword(s):  
Distance Learning ◽  
Natural Language ◽  
Knowledge Base ◽  
Junior High School ◽  
Dynamic Geometry ◽  
Preliminary Evaluation ◽  
Web Page ◽  
High School Geometry ◽  
Geometric Figures ◽  
Teachers And Students

<p>This paper presents a tool for drawing dynamic geometric figures by understanding the texts of geometry problems. With the tool, teachers and students can construct dynamic geometric figures on a web page by inputting a geometry problem in natural language. First we need to build the knowledge base for understanding geometry problems. With the help of the knowledge base engine InfoMap, geometric concepts are extracted from an input text. The concepts are then used to output a multistep JavaSketchpad script, which constructs the dynamic geometry figure on a web page. Finally, the system outputs the script as an HTML document that can be visualized and read with an internet browser. Furthermore, a preliminary evaluation of the tool showed that it produced correct dynamic geometric figures for over 90% of problems from textbooks. With such high accuracy, the system produced by this study can support distance learning for geometry students as well as distance learning in producing geometry content for instructors.<br /><br /></p>

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