Automatic Ontology Construction using Conceptualization and Semantic Roles

The explosive growth of textual data on the web coupled with the increase on demand for ontologies to promote the semantic web, have made the automatic ontology construction from the text a very promising research area. Ontology learning (OL) from text is a process that aims to (semi-) automatically extract and represent the knowledge from text in machine-readable form. Ontology is considered one of the main cornerstones of representing the knowledge in a more meaningful way on the semantic web. Usage of ontologies has proven to be beneficial and efficient in different applications (e.g. information retrieval, information extraction, and question answering). Nevertheless, manually construction of ontologies is time-consuming as well extremely laborious and costly process. In recent years, many approaches and systems that try to automate the construction of ontologies have been developed. This paper reviews various approaches, systems, and challenges of automatic ontology construction from the text. In addition, it also discusses ways the ontology construction process could be enhanced in the future by presenting techniques from shallow learning to deep learning (DL).

Information and resources available on the Web are growing increasingly and web users need to have a common understanding of them. The Semantic Web whose most important role is to help machine to understand and analyze the existing data on the Web, has not been used commonly, yet. The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of domain engineers. Also, Ontologies that have been extracted automatically from corpus on the Web might have incomplete information. The main objective of this study is describing a method to improve and expand the information of the ontologies. Therefore, this study first discusses the automatic construction of prototype ontology in animals‟ domain from Wikipedia and then a method is presented to improve the built ontology. The proposed method of improving ontology expands ontology concepts through Bootstrapping methods using a set of concepts and relations in initial ontology and with the help of the Google search engine. A confidence measure was considered to choose the best option from the returned results by Google. Finally, the experiments showed the information that was obtained using the proposed method is twice more accurate than the information that was obtained at the stage of automatic construction of ontology from Wikipedia.

Using the techniques and theories of natural language processing (NLP), this paper puts forward threads and ways to build automatically domain ontologies based on text content. Through restructuring and utilizing the generally-acknowledged domain knowledge in medical domain, this paper also constructs a concept system of the multidimensional model of modern medicine towards clinic medicine, realizing the automatic construction and acquisition of modern medical knowledge specification system. This further provides theoretical foundation and technical support for automatic construction of professional domain ontologies.

Automatic construction of a large-scale situation ontology by mining how-to instructions from the web

Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data are put forward. Finally, ontology for automated building of ontology from relational data sources has been implemented.

A novel and more effective region-based image retrieval strategy is presented based on semantic ontology. An unsupervised segmentation algorithm splits images into regions that are subsequently used as basis by the ontology-based strategy. The approach comprises three stages, namely automatic region generation, categorization and ontology construction. When receiving a query for a specific object, the search engine will, in addition to conventionally matched images, also find candidates through the semantic ontology using low level features. The proposed approach can thus find a richer set of related candidate images than traditional image retrieval approaches. This strategy is particularly useful for vague queries encountered by inexperienced users that are not trained in searching for images by the means of low-level features. The experimental results demonstrate the effectiveness of the proposed approach.

BackgroundThe engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them.ResultsWe share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods.ConclusionsGiven high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described.AvailabilityThe TFIDF term recognition is available as Web Service, described at

This paper describes and analyzes the basic structural sentence patterns in the Albanian language. The internal structure of sen-tences is determined by the verb, which serves as its predicator: the type of verb/ verb valence determines the number and type of complements, and consequently determines the minimal structure of the sentence. A verb like vdes “die”, which is monova-lent-intransitive, requires only one complement in the function of the subject to complete the sentence. Unlike vdes “die”, the bivalent verb takoj “meet” requires two complements to convey its meaning: an internal complement in the function of the direct object and an external complement in the function of the subject. Thus, the verb vdes “die” forms one of the basic sentence pat-terns in the Albanian language: S + intransitive verb, whereas the verb takoj “meet” forms another basic pattern, namely the pattern S + monotransitive verb + DO. The minimal sentence structure contains the verb together with its valence complements. Therefore, in order to derive the basic sentence patterns in Albanian, the sentence constituents that function as complements were first distinguished from adjuncts, and then it was proceeded with the classification of the type of complements. The de-scription of the basic sentence patterns is made according to the syntactic function carried by verb complements, supplemented by the description according to the syntactic categories and the semantic roles that complements carry.

The event structures expressed by sentences consist of relations among subevents and their participant roles, called thematic roles or relations. We will say that a theory of the thematic structure of sentences is strictly configurational if every thematic role distinction is represented by a configurationally distinct position in this structure. Strict Thematic Configurationality therefore implies an isomorphism between thematic relations and equivalence classes of positions in syntactic structures. Thus, given this principle, thematic or event-structural aspects of semantics must be purely syntactic. More specifically, meaning or interpretation in respect to these aspects must be a function of category type (functional properties) and configuration (purely formal structural/syntactic relations) only. There can be no representation of thematic relations as substantive categories or features. The syntactic forms representing thematic structure may or may not be identified with those of a component of grammatical syntax. However, a minimalist conception of Strict Thematic Configurationality would seem to require that they are: that there is no autonomous system of thematic relations apart from grammatical syntax.

Extracting semantic relations from the Quranic Arabic based on Arabic conjunctive patterns

This study aims to: (i) analyze the tree diagram structure of sentence patterns found in the data source, (ii) analyze the types of translation shifts of sentence patterns in the translation process from source language into target language. The novel entitled “Buddha” is a non-fiction book by Karen Armstrong. It tells about an examination of the life, times, and lasting influence of Siddharta Gautama with core tenets of Buddhism introduced throughout history. This research focuses on The Syntax Analysis and Its Translation Found on Sentence Patterns in the Novel entitled “Buddha”. This study aims at analyzing the tree diagram structure and the types of translation shifts found in the novel entitled “Buddha”. The analysis uses the theory of sentence patterns from Quirk and Greenbaum and the theory of translations shift proposed by Catford. The process of collecting data was started by reading the entire data source to understand the story in the novel entitled “Buddha” and observe the data of sentence patterns that can be taken from the story entitled “Buddha”. In the method of collecting data, the data source was read to find out complex sentences and simple sentence found in the story. The finding of the research is that there are seven sentence patterns that are translated by applying unit shift. The researcher uses the theories to support this research. Those are syntax, types of sentences, sentence pattern, tree diagram, and relevant previous studies. Syntax is stated as one of the branches that focus on the sentence structure. This research study uses theory of translation shift proposed by Catford

Semantic Role Labeling (SRL) is a crucial task in Natural Language Processing (NLP) that plays a vital role in extracting meaningful information from text. In the fields of economics and cybersecurity, accurately identifying and analyzing semantic roles within text is crucial due to the rapid increase in the amount and complexity of textual information. This abstract examines the significant role of SRL and its application in economic and cybersecurity contexts. It discusses the state-of-the-art NLP classifiers used for this purpose. By examining the relationship between language processing and these important areas, we aim to emphasize the importance of SRL in extracting useful information and improving decision-making in a constantly changing digital environment. The aim of the findings is to emphasize the significance of SRL in extracting valuable insights from text, as it serves as a fundamental technique in NLP. It is utilized in the economic context to analyze financial reports, news articles, and economic texts. It assists in decision-making and market analysis. It aids in identifying important participants, actions, and objects in economic discourse, leading to better decision-making and market analysis. In the field of cybersecurity, SRL assists parse and comprehending text data related to security, enabling faster responses to threats. NLP classifiers and machine learning models utilize SRL to automate the analysis of large amounts of text. These techniques are practically significant as they improve the ability to extract actionable insights, assess risks, and make informed decisions by organizing unstructured text data. The process of determining relevant information from a large corpus of data requires an optimal methodological basis. Relevant textual data is collected from sources such as financial reports, news articles, or cybersecurity incident reports. Textual data is cleaned, tokenized, and tagged with part-of-speech labels in preparation for NLP analysis. Human annotators label semantic roles in the text, identifying actors, actions, and objects. This creates a dataset that can be used to train classifiers. NLP classifiers, including machine learning models, are trained using annotated datasets to identify semantic roles. The accuracy and performance of the trained classifiers are evaluated using various metrics. NLP classifiers are used to automatically identify and label semantic roles in new, unseen textual data. The output helps extract insights, such as market trends or security threats, depending on the specific field. Researchers improve classifier models by iteratively training and applying them to increase accuracy.

Literary works are expressions that convey ideas and ideas conveyed by an author. Literary works in the form of poetry discuss a lot about life such as feelings, education, economics, social, politics and culture. This study aims to determine the meaning of the poem Mata Hitam by W.S. Rendra. This study uses a stylistic approach based on the theory of Wallek & Warren, 1995: 229 and Munir, S, & Mulyono, 2013: 2. This study uses qualitative methods and content analysis. The source of data in this study is the poem Mata Hitam by W.S. Rendra. The result of this research is to know the stylistic and meaning contained in the poem through sentence structure, figure of speech, imagery, pattern, and rhyme. Every love poem by W.S. Rendra has different meanings of love and always ends happily.

This study aims to analyze the sentence structures of fairy tales. It was designed with a descriptive qualitative research to Aesop’s fairy tales entitled The Lion and The Mouse and The hare and The Tortoise as the data source and by applying the method of rewriting rules in the tree diagram analysis of syntactical theory, called PSG (Phrase-Structure Grammar). The study took 26 data in which 13 sentences were taken directly from The Lion and The Mouse and the other 13 sentences were taken from The Hare and The Tortoise. The data focused on analyzing the five types of sentence structure based on Burton’s theory including Noun Phrase, Verb Phrase, Adjective Phrase, Adverbial Phrase and Prepositional Phrase. The results showed that the noun phrase uses were found more dominant than the other types of sentence structure with the percentage 38%.

The lyrics of this song are the lyrics of a dangdut song sung by Denny Caknan in Javanese. The interesting thing to study further is the use of diction and the theme using stylistics, namely by looking at the style of language used. The purpose of this study was to analyze the stylistics contained in the lyrics of the song "Kartonyono Medot Promise" Denny Caknan. The study of stylistics is directed to discuss the content of literary works. In general, regulating stylistic studies includes diction or word choice (lexical choice), sentence structure, figure of speech, partnership, rhyme patterns, and dimensions used for authors or available in literary works. This study aims to analyze the use of diction and themes contained in the lyrics of the song Kartonyono Medot Promise sung by Denny Caknan. The method used is a qualitative descriptive method because the object is the text of the Song Lyrics. The data taken is the lyrics of a song by Denny Caknan entitled Kartuyono Medot Promise. Like the style chosen by the author, stylistic theory is used, that is, it does not see aesthetics in song lyrics. As the end of this method is the result of data analysis presentation. The presentation of the results of the data analysis begins with the presentation of the diction on the data source contained in the lyrics of the song Denny Caknan. Then proceed with an explanation of the theme of the lyrics. The results of the research found are diction and theme in the song Kartonyono medot promises, which was popularized by Denny Caknan. Keywords: song lyrics, stylistic analysis, denny caknan