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Building product models, terminologies, and object type libraries
Published in Pieter Pauwels, Kris McGlinn, Buildings and Semantics, 2023
Aaron Costin, Jeffrey W. Ouellette, Jakob Beetz
Structured vocabularies need to capture two important elements: semantics and logic. Semantics are the meanings and interpretations of a word or phrase in a specific context. At the core level of computer science, data are essentially bits and bytes that the computer uses in processes and which are essentially useless to both human and computer function without any context. Therefore, it is important that the data be given the semantic information needed to represent what human function represents. For example, when data are exchanged between BIM software, it is insufficient to solely rely on 3D visual properties of the objects. Although the geometries of the objects in the 3D model are important, they alone are not sufficient to describe the needed meaning of the modelled objects. At the exchange level (i.e. passing information), semantics may cause issues for humans and computers that are interpreting the context. The goal is to have semantic consistency in an information exchange in which the human-based knowledge and computer-based interpretation of the information are equivalent, i.e. the computer understands what the user intends.
Information Retrieval and Semantic Search
Published in Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar, Hybrid Intelligent Systems for Information Retrieval, 2023
Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar
The difficulty we tend to face within the technologically superior globe is the computer interprets the language or logic like a human. “Semantics” refers to the ideas or concepts specified by words, and semantic analysis is creating any topic (or search query) simple for a machine to interpret. Semantic mapping is concerned with visualizing relationships between concepts and entities (as well as relationships between connected concepts and entities). Semantic analysis needs policies to be outlined for the system. These policies are identical because of the manner we think of language and that we anticipate the computer to emulate. For instance, “ball is orange” could be a statement that a person can interpret that there’s one thing known as ball and it is orange in color, and therefore the human is aware of that orange suggests that color. However, for a computer, this is similar to an alien language. The concept of semantics here is that this sentence formation encompasses a structure in it. Subject—predicate-object or briefly type s-p-o. Wherever “ball” is subject, “is” is predicate, and “orange” is object. Likewise, there are different linguistic refinements that are utilized in the semantics analysis.
Understanding Distributed Semantic Analysis with Spark Data Frames
Published in Nedunchezhian Raju, M. Rajalakshmi, Dinesh Goyal, S. Balamurugan, Ahmed A. Elngar, Bright Keswani, Empowering Artificial Intelligence Through Machine Learning, 2022
Richa Mathur, Devesh K. Bandil, Dhanesh Kumar Solanki
Semantics (a linguistic term) is the study of meaning of words in a language or logic. It focuses on the study of relation between signifiers, like words, phrases and symbols, andtheir denotation. Style ofany languageplays an important role in understanding the meaning of code. Semantics tries to understand about the language’s construction, interpretation, clarification, illustration, contradiction, and negotiation by its speakers and listeners. Semantic information is useful for all told aspects of understanding natural language. Natural language includes expounded linguistics analysis with the structures and occurrences of the words, phrases, clauses, paragraphs, etc., and perceives the thought of what’s communication or logic. Extracting information from semantics is difficult to get; however, it will add power and accuracy to natural language processing (NLP) systems.
Knowledge on-demand: a function of the future spatial knowledge infrastructure
Published in Journal of Spatial Science, 2021
Lesley M. Arnold, David A. McMeekin, Ivana Ivánová, Kylie Armstrong
The Semantic Web is a Web of Data where the data is self-describing, that is, structured in a way that it can be machine processed with meaning automatically derived. The Resource Description Framework (RDF) format (W3C 2014b) combined with Linked Open Data (Berners-Lee 2006) facilitate structured data links to be established allowing for the meaning to be discovered within the data. With the semantics held within the data, Web resources can be harnessed for automated processing (Sheth et al. 2005). Thus, with Semantic Web technologies, end users have the ability to mobilise a broad range of spatial resources via an open query interface to extract the available knowledge from the data. This has the potential to be done without having to configure systems specifically the end users.
Crowdsourcing-based semantic relation recognition for natural language questions over RDF data
Published in Enterprise Information Systems, 2019
Xin Hu, Jiangli Duan, Depeng Dang
In the current Big Data era, semantics enables computer to understand and reason data (Salem, Boufares, and Correia 2014), which can be applied to analyse social media (Basili, Croce, and Castellucci 2017), economic news (Elshendy and Colladon 2017), multimedia resources (Hu et al. 2014) and so on. The Semantic web is a web of data, in which each metadata has specific semantics. It can be used to improve information retrieval (Li et al. 2014; Luo et al. 2015), web service (Chen et al. 2015), and business process management (Rico et al. 2015; Hoang, Jung, and Tran 2014). RDF has been widely used as a W3C standard to describe data in the Semantic Web. For the better effectively utilise RDF data, natural language question answering over RDF data have received widespread attention (semantic relation recognition is the core of understanding natural language question).
Supporting the construction of affective product taxonomies from online customer reviews: an affective-semantic approach
Published in Journal of Engineering Design, 2019
W. M. Wang, Z. G. Tian, Z. Li, J. W. Wang, Ali Vatankhah Barenji, M. N. Cheng
Knowledge-based approaches are widely to support product design (Demoly and Roth 2017). In particular, semantic analysis is a computational process that uses techniques in linguistics, text mining, natural language processing, and machine learning to identify and correlate useful information from text (Qi et al. 2016). Due to the rapid growth of online shopping, massive amount of product information is available on the Internet. Verified consumers can write product reviews after purchasing a product. It provides dynamic and trusted data from the customer’s perspective. Therefore, many studies have been done to extract useful information from online product reviews by using semantic analysis (Ravi and Ravi 2015). In particular, researchers use sentiment analysis to measure affective information (Liu 2012). However, most of them only divide reviews into three simple categories which include positive, negative and neutral (e.g. Vilares, Alonso, and Gómez-Rodríguez 2017). It is inadequate for affective design. On the other hand, knowledge of affective design should be efficiently shared in order to achieve a truly collaborative product design. Ontology is an explicit formal specification of knowledge using semantics (Gruber 1995). It facilitates an integrated and consistent access to data and services. Ontologies have been widely used in product design, such as assembly design (Kim, Manley, and Yang 2006; Gruhier et al. 2016), gas turbine design (Li et al. 2014), and others. In particular, construction of taxonomy is an essential step in ontology development. Taxonomy helps to identify the classes and instances of ontology (Noy and McGuinness 2001). However, less attention has been paid to the construction of affective-based product taxonomies and ontologies.