Ontological-semantic text analysis and the question answering system using data from ontology

  10. Februar 2023, von Sebastian

From our systematic mapping data, we found that Twitter is the most popular source of web texts and its posts are commonly used for sentiment analysis or event extraction. 9, we can observe the predominance of traditional machine learning algorithms, such as Support Vector Machines , Naive Bayes, K-means, and k-Nearest Neighbors , in addition to artificial neural networks and genetic algorithms. Among these methods, we can find named entity recognition and semantic role labeling. It shows that there is a concern about developing richer text representations to be input for traditional machine learning algorithms, as we can see in the studies of [55, 139–142].

What is a good example of semantic memory?

Semantic: Semantic memory refers to your general knowledge including knowledge of facts. For example, your knowledge of what a car is and how an engine works are examples of semantic memory.

In this study, we identified the languages that were mentioned in paper abstracts. We must note that English can be seen as a standard language in scientific publications; thus, papers whose results were tested only in English datasets may not mention the language, as examples, we can cite [51–56]. Besides, we can find some studies that do not use any linguistic resource and thus are language independent, as in [57–61]. These facts can justify that English was mentioned in only 45.0% of the considered studies.

Keyword Extraction

As a result, their new method for community detection considered the texts and words simultaneously, both in the rows and columns of the affiliation matrices. They concluded that the co-clustering approach avoided the mean value convergence and therefore mirrored real data more closely. We included this research because of its innovative use of the matrix for text analysis, and because they focused on mirroring patterns in real text data. Since we worked with user-inputted review titles, our dataset may show patterns unique to natural language text.

  • To the best of our knowledge, this article reports the first successful incorporation of semantic spaces based on local word co-occurrence in the sentiment analysis task.
  • However, machines first need to be trained to make sense of human language and understand the context in which words are used; otherwise, they might misinterpret the word “joke” as positive.
  • Thus, there is a lack of studies dealing with texts written in other languages.
  • Semantic and sentiment analysis should ideally combine to produce the most desired outcome.
  • Semantic networks is a network whose nodes are concepts that are linked by semantic relations.
  • Thus, the ability of a machine to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation.

The authors also discuss some existing text representation approaches in terms of features, representation model, and application task. The set of different approaches to measure the similarity between documents is also presented, categorizing the similarity measures by type and by unit . The paper provides a brief overview of the most common open databases of computer attacks, information security threats and software vulnerabilities.


Thus, the search terms of a systematic mapping are broader and the results are usually presented through graphs. Text mining techniques have become essential for supporting knowledge discovery as the volume and variety of digital text documents have increased, either in social networks and the Web or inside organizations. Although there is not a consensual definition established among the different research communities , text mining can be seen as a set of methods used to analyze unstructured data and discover patterns that were unknown beforehand . As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals.

They suggested PageRank as a future method to include the importance of different texts in the network. As a result, they were able to quantify the balance between traditional topics and innovative topics in service industry research, which could be useful to future researchers. Schiessl and Bräscher and Cimiano et al. review the automatic construction of ontologies. Schiessl and Bräscher , the only identified review written in Portuguese, formally define the term ontology and discuss the automatic building of ontologies from texts.

Text Classification and Categorization

This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms. The project aiming to build a medical ontology is introduced, and a method to estimate term relations and term classification, which are the basic structure for the ontology are presented. This book provides the state-of-art of many automatic extraction and modeling techniques for ontology building that will lead to the creation of the Semantic Web. An ensemble convolutional network is proposed by combining GCN and CNN, which catches the global information and CNN extracts local features and achieves better performance than other state-of-the-art methods with less memory.

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The researchers mapped scientific knowledge categories to be able to classify topics and taxonomies from the data. This paper suggested that the traditional text analysis methods that rely on knowledge bases of taxonomies can be restrictive. So, this research created a new categorization method, where they used n-dimensional vectors to represent scientific topics, then ranked their similarity based on how close they were in the n-dimensional space. By not relying on a taxonomy knowledge base, the researchers found that they could analyze a wide variety of scientific field with their model. We included this paper because their network analysis was very similar to the other text analysis papers we read, but focused more on the model, and less on the idea of semantic text analysis.


semantic text analysis analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context. It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software. This paper proposed an expansion of the text clustering analysis method used in network semantic text analysis, using co-clustering. Clustering text can lead to clusters where the mean value converges toward the cluster center, which is rarely seen in real text data. Instead, the researchers simultaneously partitioned the rows and columns of matrices to create “co-clusters”, and use a two-mode matrix in the place of the common space-vector model.

lack of studies

Semantic tagging can be seen as an expansion of named entity recognition task, in which the entities are identified, disambiguated, and linked to a real-world entity, normally using a ontology or knowledge base. The authors compare 12 semantic tagging tools and present some characteristics that should be considered when choosing such type of tools. The advantage of a systematic literature review is that the protocol clearly specifies its bias, since the review process is well-defined. However, it is possible to conduct it in a controlled and well-defined way through a systematic process. But before deep dive into the concept and approaches related to meaning representation, firstly we have to understand the building blocks of the semantic system. Therefore, in semantic analysis with machine learning, computers use Word Sense Disambiguation to determine which meaning is correct in the given context.


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