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Introduction
Published in Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar, Hybrid Intelligent Systems for Information Retrieval, 2023
Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar
Most of the keyword-based search engines get the answers syntactically correct but in huge volume. Endeavors are needed to improve the conventional keyword-based SEs by considering the semantics in it. Semantic Search is a region of research that concentrates on the meaning of terms utilized as a part of client questions. Here ontology assumes a critical part to characterize the idea alongside the relationship of terms in the domain. Since the understanding of concepts is domain specific, ontologies are domain dependent. According to this argument, the meaning of queries in the tourism domain may vary in some other domains such as teaching. It is the fact that classical information retrieval (IR) is a well-informed and set-up discipline. A semantics-based IR moves toward enhancing it as opposed to supplanting it. The strategies created in traditional IR are appropriate to semantic IR as well. Simply, the semantically enhanced IR approach embeds basic knowledge with clearly defined semantics to build an intelligent web retrieval system [22].
Latest Applications of Semantic Web Technologies for Service Industry
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Godspower O. Ekuobase, Esingbemi P. Ebietomere
The production of knowledge commences with access to, comprehension, and analysis of scholarly knowledge through knowledge opportunities to experimentation, theorization or observation, and subsequent notes or documentations and unbiased scrutiny or peer review. It is at the point of acceptance of research contributions as new knowledge by competent authority that scholarly knowledge is said to be produced. SWTs have found particular industrial engagements in efficient knowledge access – digital search systems. The search system has proved invaluable in the search and retrieval of data or information/document in this era of digitization and internet. Semantic search systems for varying nature of information now abound that take advantage of SWTs for excellent precision and recall. Key SWTs used for this purpose are ontology and KG [109–119]. The use of ontology undisputedly yields better semantic search results. The work [120] described the developing procedure of a basic ontology-driven search engine.
Real-Time Search in the Sensor Internet
Published in Ioanis Nikolaidis, Krzysztof Iniewski, Building Sensor Networks, 2017
Ranking is similar to general search engines for semantic search engines, which try to understand the user’s intent. However, when the data set consists of structured semantic data, traditional ranking factors are not feasible. Thus, semantic search engines try to provide concrete answers or information on the query keywords by using the semantics and relationships in their data set.
KG4Py: A toolkit for generating Python knowledge graph and code semantic search
Published in Connection Science, 2022
Lu Liang, Yong Li, Ming Wen, Ying Liu
Traditional search engines only retrieve answers by matching the keywords, while semantic search systems retrieve answers by dividing and understanding sentences. Before semantic search, the questions and answers in the database are embedded in a vector space. When searching, we embed the divided and parsed question into the same vector space, and calculate the similarity between the vectors to display the answers with high similarity. Next, we introduce the selection of the semantic search model.
Data Science with Semantic Technologies: Application to Information Systems Development
Published in Journal of Computer Information Systems, 2023
Semantic technologies techniques have as common a general goal of helping making sense to large or complex data sets without being provided with any predetermined knowledge about that data. However, they are different in produced data formats and underlying formalisms on which they rely. They hardly work well together without investing a lot of effort in integration engineering. Semantic technology techniques include, but not limited to:4Natural-language processing technologies aim at understanding the full meaning of human language by processing text or voice content and determining its structure and sentiment.72Data mining technologies make use of pattern-matching algorithms in order to extract and discover patterns and their correlations within large sets of data, generally to solve (business) problems.73Artificial intelligence or expert systems technologies use elaborate reasoning models to automatically answer complex questions. These systems may include machine-learning algorithms in order to improve the system’s decision-making capabilities over time.74Classification technologies make use of heuristics and rules in order to tag data with categories to make information easier to search and analyze.75Semantic search technologies allow locating information by concept rather than by keyword or key phrase. They are driven by user intent as well as contextual meaning of search terms.76
Intelligent generation method of emergency plan for hydraulic engineering based on knowledge graph – take the South-to-North Water Diversion Project as an example
Published in LHB, 2022
Xuemei Liu, Hankang Lu, Hairui Li
At present, the intelligent generation method of emergency plans mainly relies on case-based reasoning (CBR). CBR solves emergencies by matching the similarity between the target case and historical cases, as well as reusing or modifying emergency plans of historical cases with the highest similarity (Sekar et al., 2019). Many scholars have applied CBR to the field of emergency decision-making. For example, Fan et al. used CBR based on the case retrieval method and hybrid similarity calculation (Fan et al., 2014); and Zhang et al. proposed a new case adjustment method to modify and generate the emergency plan for grid stroke disasters (Zhang et al., 2015). Jiang et al. combined ontology with improved CBR to create a decision method for safety risk management (Jiang et al., 2020). Hadj-Mabrouk set the goal to develop a new approach to the analysis and evaluation of the validity of decision support, based on machine learning and the CBR (Hadj-Mabrouk, 2020). The above research relies on the scale of the historical case base and the calculation weight of the case attributes. The generated emergency plans are presented as documents, with a low degree of digitisation and weak knowledge correlation. The knowledge graph (Chen et al., 2021), created in 2012, was initially applied to semantic search (Dong et al., 2014), question answering (Hao et al., 2017), intelligent recommendation (Gong et al., 2021), and so on. It involved the rapid acquisition, rational organisation and scientific utilisation of massive amounts of knowledge. In recent years, knowledge graphs have also been applied in the field of emergency management. Li et al. used knowledge representation to provide auxiliary decision-making for natural disasters (Li et al., 2020). Liu et al. constructed a knowledge map of geological disaster emergency plans for rapid emergency response actions (Liu et al., 2021). Ni et al. constructed an emergency plan knowledge system to provide reliable information for emergency responders (Ni et al., 2019). Yang et al. constructed a knowledge co-occurrence network to analyse the importance of emergency management in public health emergencies (Yang et al., 2020).