In the age of digital transformations, humanities studies have increasingly relied on knowledge extraction from digital texts to enhance understanding and research. This article explores an innovative approach that utilizes advanced language models to extract knowledge from texts encoded in TEI/XML format, focusing on the works of the renowned Italian poet Giacomo Leopardi. The research aims to create knowledge graphs (KGs) that are machine-readable from unstructured texts, facilitating the exploration of relationships between information and openness to external resources. We will investigate an innovative methodology that links large language models with traditional algorithms for relationship extraction, illustrating the benefits that these elements bring when dealing with Italian literary texts. Through a specific case study of Leopardi’s works, we provide a new perspective on how knowledge extraction can be enhanced and supported for literary research.
Research Introduction on Knowledge Extraction from Literary Texts
This research addresses the issue of knowledge extraction from TEI/XML-encoded literary texts, focusing specifically on the works of the Italian poet Giacomo Leopardi. Leopardi is famous for his rich poetic language and profound ideas, making him one of the most prominent writers in Italian literature. A multilingual language model, such as ChatGPT, was employed to extract linguistic structures from literary texts, with this process aiming to produce graphs of knowledge that are machine-readable. This research represents an important step towards improving methods of accessing information from large literary collections and provides a strong foundation for enhancing scientific research in the fields of digital humanities.
Experimental Approach to Creating Knowledge Graphs
The experimental approach in this research involves using data from 41 TEI/XML files based on Leopardi’s letters. Unstructured texts are transformed into semi-structured formats that facilitate interpretation by existing general language models. The research focuses on developing a system that leverages large language models and traditional relationship extraction models, where entity extraction and relationship extraction techniques are integrated to achieve accurate and consistent results. The research also adds a similarity-based filtering mechanism to ensure the preservation of semantic consistency in the extracted results, ultimately improving the quality of the extracted knowledge graphs.
Challenges and Opportunities in Extracting Knowledge from Historical Texts
Traditional methods for creating knowledge graphs yield many benefits, but they also face challenges, especially when dealing with historical texts. Difficulties arise in identifying entities and mapping relationships between them due to differences in language and style compared to modern texts. It is essential to develop customized models that consider the specifics of historical texts and their linguistic diversity. The research highlights the importance of addressing these challenges to realize the full benefits of knowledge extraction from historical literature, such as uncovering civilizations and cultures by understanding the works of ancient writers like Leopardi.
Research Findings and Quantitative Quality Assessment
When comparing the proposed approach to a simple baseline model, results showed a significant improvement in the accuracy of the extracted knowledge. The resulting graphs had fewer relationships, but they were richer semantically, primarily focusing on literary activities and Leopardi’s health. This reflects the significant importance of the extracted knowledge for understanding the life and works of the writer. The research uses numbers and ratios as performance metrics, giving researchers the ability to evaluate the effectiveness of various methods and apply them to other literary texts.
Future Research and Expanding the Study
Based on the achieved results, the research suggests the potential to expand the scope of study to include other literary texts and different genres of literature. Large language models are a powerful tool that opens up opportunities for further exploration of cultural and heritage texts. The outcomes of this research can contribute to enhancing efforts by researchers in the digital humanities field by providing more effective means for exploring and understanding literary texts, through the production of comprehensive and intelligent databases that enhance the potential for research and dialogue between historical and contemporary knowledge. Furthermore, consideration should be given to developing new models that take into account the different cultural and social phenomena represented by classical literary texts.
Challenges
Entity Extraction from Human Texts
The process of entity extraction from literary and historical texts is complex due to the linguistic and idiomatic challenges posed by such texts. Many studies aim to develop specialized tools in this field; however, most encounter significant limitations, including a focus solely on English texts and the unavailability of specific datasets for certain domains. For example, many studies have utilized tools like DBpedia Spotlight on specific text corpora, yet these works have not intensively addressed how to extract relationships between entities, which is a fundamental element for building reliable Knowledge Graphs (KGs).
Other studies have tackled relationship extraction (Relationship Extraction – RE) by improving access to literary and historical text corpora. For instance, a study by Reinanda et al. (2013) proposed a hybrid approach that combines finding correlations and RE to build networks of entities, which was useful in historical and political documents. However, this approach faced difficulties in dealing with rare complexities in varied linguistic domains like the humanities. The methods used, such as statistical co-occurrence measures and modern models, have been found inadequate in conveying the most accurate relationships based on events.
There are also attempts at Open Information Extraction (Open IE) that seek to extract more complex relationships without relying on predefined vocabulary or ontologies. However, it has been shown that this does not yield accurate results when applied to historical texts, where the language is complex and versatile. The live challenges lie in the ability to handle different entities and processes and produce rich relationships that fit the linguistic complexities of the target texts.
Case Study: Exploring the Texts of Giacomo Leopardi
To test the previous hypotheses, the texts of the renowned Italian poet Giacomo Leopardi were used as a case study. Platforms like “LiberLiber” and “Wikisource” contain a diverse range of digital texts by Leopardi. These texts are significant as they include his private correspondences that feature accurate information encompassing facts and literary insights that may not be available in external databases like Wikipedia. The study highlights the importance of using knowledge extraction algorithms to explore the entire network of entities and relationships mentioned in Leopardi’s correspondences.
The digital library at the University of Cambridge has gathered a collection of manuscripts; it includes 41 manuscripts: 36 letters and two pieces from “Essays on Translations” from classical languages. Analyzing these texts poses a challenge to understand all the meanings and relationships they contain. The study processes this data through text analysis and entity recognition, as well as organizing information in a structured form. The main idea behind the research method was to convert the outputs of the texts into a format that can be utilized to build a knowledge database.
The program analyzes TEI/XML files to extract data and metadata. This program works efficiently with the Python lxml library, which allows for easy handling of XML information. Basic information such as document ID, title, date, and other data were extracted to systemically access the information.
Methodological Approaches Used in Knowledge Extraction
The methodological steps in this study are built on multiple phases for knowledge extraction according to text processing techniques. The process begins with scanning the digital texts of Leopardi’s writings found in the digital library of Cambridge. The adopted method indicates employing intelligent language models to aid in facilitating the extraction of information and organizing it into a manageable format. The initial steps involve gathering and extracting the texts into JSON format through the use of ChatGPT, which analyzes entities and relationships in accordance with the literary and historical context.
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The ChatGPT model is employed to extract relationships between entities by creating triples of the form [subject, verb, object], which helps clarify various relationships without the need for a pre-defined structure. The results from the previous step are analyzed using a seq2seq model to infer relational graphs that are linked to a Wikidata schema. These studies leverage the REBEL tool, which is a model trained on English Wikipedia, supporting the name extraction process against the use of internationally directed models.
The filtering process of the results generated by these models is essential to eliminate inaccurate results and ensure that the relationships align with logical data. This relies on the accuracy of the models used and their ability to process data accurately.
Entity Relationship Extraction Model
The process of extracting relationships between entities requires a model that allows for the classification of these relationships according to external vocabularies or concepts. This model converts the data extracted from models like ChatGPT into a simple textual format, which facilitates the recognition of relationships and their properties. At this stage, the outputs generated by ChatGPT are transformed from JSON format into plain text, making the extraction of relationships easier. For example, the triple [“Paolina Leopardi”, “:locationOfWriting”, “Recanati”] is automatically converted into simple text, where the elements are aggregated with added spaces for organization. This is a critical step because it enables the relationship extraction model to comprehend the content and process natural texts.
When inputting text like “Paolina Leopardi location of writing Recanati” into a model like REBEL, the model produces a dictionary containing properties such as “head” and “tail” and “type”, where “head” refers to the first entity and “tail” refers to the second entity. By utilizing properties from Wikidata, the model can extract relationships according to a pre-defined set, enabling the application of logical rules such as symmetry or asymmetry in relationships. The REBEL model has numerous advantages, including facilitating the creation of an operable knowledge database across different systems.
Relationship Extraction Using seq2seq Model
The process of extracting relationships using the seq2seq model involves processing texts with outputs generated from ChatGPT. This is achieved by reshaping the data into a form that the model can externally process. The issue of correcting and refining this data is fundamental, as the model may produce triples that are grammatically correct but semantically inaccurate. To mitigate this issue, a specialized REBEL model for natural language processing, trained on similar texts such as Wikipedia, is employed. However, this model requires further improvements when it comes to texts containing complex information.
While the “related to brother” property in Wikidata is utilized as a symmetric property, the same relationship can be employed in different ways to enhance the database. For instance, if a triple referencing “Giacomo Leopardi” and ”Paolina Leopardi” is extracted as siblings, the symmetric relationship can help enrich the data in unconventional ways, making the information more robust. By introducing more interactions and logical constraints such as asymmetry, the resulting graphs can be explored in greater depth.
Data Filtering Process Using SBERT
One of the main challenges in the REBEL model is the possibility of generating inaccurate triples due to its training on Wikipedia texts. To minimize errors, a filtering step is necessary to help maintain consistency between the extracted information. The SBERT model is utilized, which encodes long texts and converts them into responses in the form of vectors. This aims to ensure the differentiation of triples with divergent meanings and exclude them from the database.
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Applying the SBERT model on the extracted triples outputs, an angular similarity threshold is applied between the vector representations of the extracted triples. A threshold of 0.9 was used, ensuring the retention of closely related triples, thus reducing errors. Through this process, it is verified that quality is stable and data consistency remains continuous, leading to an enhancement of the knowledge base.
Generating the RDF Graph and Linking Entities
The extracted data enters an additional phase represented by merging the triples with metadata from the modified version. Several pre-identified ontologies are used, enabling more effective integration of information. The document is represented using the E31_Document class. Furthermore, the Dublin Core term is used to detail the document’s properties such as title, date, and language.
Each triple in the RDF database is represented using a re-description technique. Each triple consists of an element representing a statement that includes a classification for the subject, verb, and source. This facilitates referencing related data and thus enhances the utility of the database. By developing strong connections with elements in Wikidata, the capability for conceptual embodiment and other complex interactions can be improved.
Evaluation of the Quality of the Knowledge Base
When evaluating the quality of the extracted database, a comparison system based on a simple model for multilingual extraction was adopted. The experimental results demonstrated that the enhancement of results through the use of REBEL and filtering techniques helped validate the data and increase accuracy. Specifically, the comparative results showed that the accuracy of acceptable triples was significantly higher.
The ratio of semantic accuracy and data consistency was measured, and it was found that all criteria related to complex texts were well met. This confirms that the use of generative AI models like ChatGPT with relationship extraction tools increases the efficiency of building a knowledge database for complex literary issues. This type of mix can present new opportunities in the fields of literary research and information science development.
Performance of the mREBEL Model and Challenge Factors
The performance of the mREBEL model relies on several key factors that explain its superiority and challenges when dealing with diverse texts. This analysis begins by emphasizing that mREBEL was primarily trained for extracting triples [Subject, Verb, Object] from small snippets in Wikipedia, making it face significant difficulties when applied to longer and more complex texts such as the letters of the Italian poet Giacomo Leopardi. This highlights that the model’s preparation and training may not align with the complexities of historical or literary texts that contain unfamiliar language and forms.
For instance, Leopardi’s texts contain specific usages of words and relationships that are challenging for the mREBEL model to accurately identify. Besides differing discourse contexts, certain words, such as “medal” which in his letter refers to currency rather than an award, intensify the depth of challenges the model faces in understanding ancient texts. This gap emphasizes the urgent need to develop models capable of handling the complexity of language across different time periods.
Additionally, a measurement reduction study was conducted to assess the quality of data extraction at each step of the process. The results showed that the rates of semantic accuracy were lower when using mREBEL compared to ChatGPT, highlighting the importance of improving relationship extraction methods and adapting models to specialized texts. It was concluded that these challenges reflect the specific barrier in understanding historical language and open a broader understanding of the intricate literary components and the links between them.
Strategy
Knowledge Extraction and Accuracy Improvement
The use of the proposed knowledge extraction strategy in this context has proven to be highly successful, demonstrating that the combination of multilingual language models and relationship extraction techniques can enhance the quality of the extracted knowledge. The idea lies in recognizing how a specially designed model, augmented with advanced techniques, can increase the accuracy in extracting facts and information related to literary texts.
The model’s high performance is reflected in an accuracy of 0.67 and a high reliability of 0.93, making this method superior to other approaches that rely solely on training on general information sources like Wikipedia. The difference becomes clear when the model is able to extract RDF data more accurately, allowing it to use the properties of Wikidata to facilitate SPARQL queries and benefit more from logical properties such as symmetry and dispersion.
On the other hand, this strategy enhances the clarity and reliability of the knowledge extraction process. Therefore, the importance of using well-generated LLM data and the accuracy of the extracted values emerges, as the homogeneity and collision between the metrics of the input data and the outputs are verified. This enhances users’ understanding of how to extract information through the relevant messages. This calls for maintaining a sustainable process aligned with the historical and literary knowledge of the texts being analyzed.
Challenges and Risks of Relying on AI Models
Despite the advancements in knowledge extraction techniques, reliance on models like ChatGPT requires cautious handling, as false generations can lead to errors in the extracted data. This weakness may occur due to a loss of accuracy when processing data through a set of tools that have not been designed together. In this way, the need to engage human oversight in certain stages of the extraction process becomes apparent to reduce errors and provide results with higher reliability.
It also has implications for how researchers deal with live operational results, necessitating the use of rigorous strategies and checklists for the extracted information. By paying attention to these challenges, it becomes possible to improve outcomes through well-considered steps that form a spectrum of available options.
Finally, it would be beneficial to develop standard frameworks for testing the methods used for knowledge extraction, focusing on data-specific considerations and possibilities, which helps frame a healthy counterpart to achieve optimal results in the future.
Expansion of Knowledge Extraction Strategies for Italian Literature
Advanced research in this field aims to open the technological horizon for better understanding literary texts. There are plans to expand the dataset used, including searching for new sentences that express more precise meanings. This requires identifying contradictions in the features and standards in the knowledge networks acquired by incorporating criteria related to Leopardi’s literature.
Additionally, it is essential to build standard foundations to explore additional uses of the utilized models, considering how to use the linguistic understanding from alternative intelligence models to answer complex queries. This methodology involves creating integrated systems that organize previously acquired information to understand the relationships between cultural platforms and literary texts proficiently.
During these hypotheses, it would be beneficial to employ precise software support tools to conduct aggressive inquiries on literary datasets, enhancing the presence of a rich database of academic information on Leopardi’s literature. This effort represents an investment in opportunities for future studies aimed at enhancing model performance and granting it greater investigative abilities over historical literary texts.
The Importance of Knowledge Extraction in Digital Humanities
Knowledge extraction from digital texts has become vital in the field of digital humanities. It requires dealing with massive collections of cultural and heritage materials, contributing to enhancing research and understanding. The literary works written by Giacomo Leopardi stand as a prominent example of this challenge, as Leopardi is considered one of the most important authors in Italian literature. Born in the small town of Recanati in Italy in 1798, he is a writer famous for his poetry that has been translated into more than twenty languages. With more than 15,000 digital copies of his manuscripts available across various social platforms, it becomes essential to employ effective techniques for knowledge extraction to better understand the content of his works.
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The methods used in knowledge extraction are essential when dealing with large historical collections of literary texts. Although there are general knowledge graphs like Wikidata and DBpedia, these graphs may not cover all the entities and relationships referred to in specific texts. For example, the information about an author present in Wikidata might lack important details about their personal life or relationships. This data gap makes knowledge extraction techniques essential for uncovering new entities and facts not found in the connected open data.
Challenges in Representing Knowledge on the Semantic Web
Representing knowledge poses a significant challenge when faced with a wide range of texts. While knowledge graphs like Wikidata are beneficial, there may be specific entities associated with ancient literary works that are not listed within these graphs. Therefore, information extraction and entity extraction methods must be employed to find new information about historical authors and their writings. These challenges include writing texts in multiple languages and searching for insights related to events and characters that are not typically available on the web.
Effective knowledge extraction from historical materials requires appreciation of the structure of texts, including literary and cultural factors. This involves creating semantic graphs that express the relationships between characters and events in their various contexts. Looking at the works of Liu Brady, a researcher can discover through these semantic graphs pivotal links that have not been addressed in general knowledge graphs.
Employing Large Language Models in Knowledge Extraction
Large language models play a crucial role in enhancing knowledge extraction capabilities, as they can perform complex inferencing operations. These models facilitate the processing of large literary texts and extraction of useful information. By employing machine learning techniques, language models can process texts and extract facts and characters, contributing to the development of knowledge graphs. For instance, they can be used to obtain patterns of literary identity for authors and how their works relate to specific time periods or cultural events.
Many studies emphasize the role of large language models in improving the quality of extracted data. For example, tools like ChatGPT can be used to draw connections between characters in Liu Brady’s texts and reveal life details that are not yet known, potentially enriching literary research and studies of cultural history. More importantly, these models can handle multilingual data, allowing them to understand the rich cultural contexts associated with historical texts.
Practical Applications of Knowledge Extraction in Literature and Arts
There are numerous practical applications of knowledge extraction, particularly in the fields of literature and arts. Researchers in digital humanities can benefit from tools like entity extraction techniques, which help identify characters, places, and events mentioned in texts. Additionally, semantic analysis applications allow for building relationships between different texts, deepening the understanding of literary works and enabling researchers to present new insights into well-known texts.
Furthermore, projects such as studying the lives of artists can leverage knowledge extraction-based technological frameworks to understand and analyze the works of historical artists like Liu Brady. This is done by linking their works to innovative insights in the field of art and criticism. These practices enhance the ability of academic systems to build rich and interconnected knowledge through the use of modern technologies.
In this way, researchers can enhance knowledge accessibility and highlight matters that have previously been overlooked. This opens the door to discussions in new areas, which is beneficial for preserving cultural and artistic heritage and underscores the importance of applying technology in academic research.
Techniques
Transforming Unstructured Textual Information into Knowledge Graphs
The process of transforming unstructured textual information into knowledge graphs requires the use of multiple techniques, including entity linking and relationship extraction. Entity Linking is a technique aimed at identifying references to specific characters or concepts in texts and determining the appropriate entry in the knowledge base that should be linked. On the other hand, Relation Extraction works to determine whether two entities are linked by a specific relationship, which is typically defined using certain vocabularies or ontologies. With the advancement of language models, significant progress has been made in this field, allowing for more effective extraction of meaningful information from texts.
For instance, pre-trained language models like REBEL have proven effective in extracting triples [subject, verb, object] from English texts using Wikidata properties. Additionally, a multilingual model named mREBEL has been developed, which enhances the flexibility of the technique for converting texts in different languages into knowledge graphs. It can be said that advancements in this field enable research to progress towards a deeper and more complex understanding of literary and historical texts.
The Importance of Large Language Models in Knowledge Extraction
Large language models have contributed unprecedentedly to improving knowledge extraction from texts. These models rely on techniques such as Few-shot Learning, which allows knowledge transfer from large-scale applications to more specialized fields like literature and the arts. For example, Xu and others utilized relationship extraction methods using large language models, highlighting their ability to perform knowledge extraction using limited training data. Furthermore, the development of techniques like precise query generation for guidance helps improve extraction accuracy by reformulating requests repeatedly.
However, there are still limitations, as large language models produce textual representations, not true knowledge graphs. To achieve an effective transition from the triples generated by language models to knowledge graphs, entities and relationships must be linked to the knowledge base. This requires applying effective methods for relationship extraction to ensure accurate linking of external resources with appropriate properties in ontologies.
Specific Applications in the Field of Digital Humanities
The research goals align with applying knowledge extraction techniques to literary texts, focusing on the TEI/XML encoding specific to Italian texts. This approach addresses challenges related to how to extract formal and machine-readable representations from literary texts. The research aims to bridge the gap between studies based on large language models and link and relationship extraction methods in the field of digital humanities by implementing a system that can handle literary texts written in historical literary language.
The innovation in this direction lies in leveraging the capabilities of ChatGPT to convert unstructured texts into semi-structured formats, making them easier to understand by pre-trained models. Additionally, the study is directed towards achieving two main goals; first, to process texts effectively for accurate knowledge extraction. Second, to provide a methodology that combines large language model techniques with traditional relationship extraction methods to improve results extracted from literary texts, such as the letters of Giacomo Leopardi.
Challenges and Limitations in Knowledge Extraction for Literary and Historical Texts
There remain significant challenges in the field of knowledge extraction from historical and literary texts, including difficulties in handling linguistic diversity, high rates of optical character recognition (OCR) errors, and unclean input texts. Linking and relationship extraction techniques require new models specifically designed to address these challenges, as general models often prove ineffective in processing specialized texts.
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the initial processing, the next step involves cleaning and normalizing the extracted data to ensure consistency and accuracy. This includes removing any irrelevant information and standardizing formats. Once the data is cleaned, the process moves to the identification of entities and relationships within the texts using Natural Language Processing (NLP) techniques. These techniques help in recognizing key entities such as people, places, and events, as well as the relationships between them.
Following the entity and relationship extraction, the final step is constructing the knowledge graph, which visually represents the identified entities and their interconnections. This graph serves as a resource for further analysis and makes it easier to explore the historical context of Leopardi’s works. By effectively managing this pipeline, the research aims to produce a comprehensive knowledge graph that captures the intricate relationships in Leopardi’s oeuvre.
In this phase, different language models are used to generate RDF/XML triples from unstructured text, requiring multiple steps including the extraction of different entities and their relationships. The algorithms used in this process include zero-shot triple extraction using models like ChatGPT-4. At this stage, textual triples are produced without specifying a particular schema for the relationships, allowing greater freedom in data extraction.
Relationship Extraction Using Seq2seq Model
The step of extracting relationships using the seq2seq model is a critical element in the process. At this stage, the initial extraction result is converted into plain text to facilitate data processing. This step allows linking each extracted relationship with a set of predefined properties in Wikidata. This enhances the analysis of complex relationships, as the model can determine whether the relationship follows certain properties such as symmetry or asymmetry.
These steps are essential for creating a queryable knowledge graph using SPARQL language databases. By linking names to various associations such as VIAF and GeoNames, additional references to confirmed entities in Wikidata can also be made. This also contributes to more efficient data integration and facilitates expanding knowledge graphs that include new properties and meaningful relationships.
Automatic Triple Generation Using Seq2seq Model
The generation of triples is an important step in natural language processing, where these triples are automatically created using a Seq2seq model based on neural architecture. This model is capable of transforming texts written in natural language into a data structure that can be understood and processed, though it is not without challenges. This process requires a third component aimed at reducing the risk of errors that may occur during the generation process. This component plays a key role in filtering the generated triples, ensuring accuracy and coherence before integrating them into the knowledge base (KG). Deep learning-based models, such as REBEL, which relies on BERT and is trained on content from Wikipedia, are employed. However, it is found that this model is not optimized for processing texts containing complex factual knowledge, such as the letters of the Italian poet Giacomo Leopardi.
One of the significant problems faced by the REBEL model is the quality of the triples it generates. For example, an inaccurate relationship can be produced, where a triple like [“Giacomo Leopardi”, “:sentLetterTo”, “Antonio Fortunato Stella”] may transform into another inaccurate form like [“Giacomo Leopardi”, “relative”, “Antonio Fortunato Stella”]. This highlights the importance of having a filtering process to prevent the production of such triples that are linguistically sound but semantically inaccurate.
To improve accuracy, a third filtering component based on the SBERT model was integrated. SBERT transforms sentences into numerical representations (or “embeddings”) that represent meaning in a linear space. This filter helps remove triples with different meanings from the outputs. The extracted triples from both ChatGPT and REBEL are converted into text strings, and a certain threshold is applied to evaluate the similarity between the representations. By adopting a high threshold, triples based on relationships that are highly similar in meaning are maintained, contributing to reducing the occurrence of errors.
RDF Model and Graph Generation
With the extraction of triples completed, they are integrated with the metadata of the TEI/XML version to form the knowledge base. Known models and vocabularies in the linked open data (LOD) field are used, facilitating the organization of data in a widely recognized manner. For example, the document is represented using the E31_Document class from the CIDOC-CRM model, showing a clear structure of the relationship between digital content and metadata.
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Data is represented using RDF reification, showing each triple as an element of type rdf:Statement, which includes labels representing natural language for each triple in addition to four main properties: rdf:subject, rdf:predicate, rdf:object, and dcterms:source. This representation facilitates linking entities and properties to resources in Wikidata, enhancing the capability for analysis and inference based on the extracted knowledge structure.
The importance of the RDF model lies in its ability to integrate heterogeneous data from multiple sources and achieve cohesion through well-known and precise models. It particularly supports the knowledge base produced for SPARQL queries, opening the door for advanced research and sophisticated analyses, allowing the utilization of the relationships embedded in the Wikidata system. Recently, a Turtle representation of the extracted knowledge base has been released, enhancing accessibility for the researcher and developer audience.
Evaluation of the Extracted Knowledge Base Quality
The evaluation of the knowledge base quality falls under a fundamental category due to the importance of accuracy and consistency in classifying the extracted information. Our knowledge extraction system was compared to a simple data-based model called mREBEL, which is considered the only available model for evaluating extraction models in the context of historical Italian literature. The model has not been adequately trained on such literary texts, resulting in significantly lower performance when applied to the complex letters of Leopardi.
A variety of metrics were used to evaluate the quality of the knowledge base, such as semantic and pragmatic precision. The first metric is calculated by determining the ratio of triples that reflect factual realities to the total number of triples. While consistency is measured based on the ratio of non-contradictory statements to the total statements. It is clear that the ambitious case requires setting high standards for precision, as the entities used in the triples must be unambiguous, and the relationships must accurately reflect the true connections between entities.
The analysis indicates that the application of the REBEL model, along with the adopted filtering strategy, has shown significant improvements in precision and consistency across the different dimensions of the extracted knowledge base. While the basic model showed lower performance in the case of long and complex texts, the use of flexible strategies in extracting entities and relationships helped achieve better results. This reflects that development and investment in these models is vital for achieving accurate and reliable outcomes.
Using Language Models for Knowledge Extraction
In recent years, large language models (LLMs) have increasingly been used in natural language processing, particularly in the field of information and knowledge extraction from texts. The multilingual language model, trained to follow instructions, is an effective tool for enhancing the information extraction process from literary texts. Through these models, techniques for extracting relationships and knowledge have been implemented, enabling the creation of knowledge graphs (KG) linked to specific topics, such as Italian literature. This use provides a significant advantage in the accuracy and quality of the extracted information, as resources like Wikidata are utilized to enhance data extraction accuracy. For example, researchers have been able to extract links between literary entities and gather information about authors’ lives, making this method highly valuable in literary research.
Types of Entities and Relationships in Knowledge Graphs
Knowledge graphs are a powerful tool for storing and organizing information in a way that allows for precise queries about different subjects. By using the SPARQL protocol, these graphs can be queried to identify the most common types of entities and relationships. For example, key sentences linking literary entities to the author’s life have been extracted, illustrating how these aspects overlap. The results indicate that relationships related to the author’s literary activity were the most common, followed by relationships concerning health, which is a central topic in the lives of many writers. This focus reflects the interaction between mental health and literary creativity, facilitating an understanding of the relationship between the author’s experience and their literary works.
Integration
Language Models with Relation Extraction Techniques
The integration of language learning models with relation extraction methods represents an advanced step in improving the accuracy of information extraction from texts. By utilizing techniques like REBEL, it has become possible to extract precise and reliable information with model accuracy rates reaching 0.67. This success relies on combining the natural language understanding provided by language models and the structural information available in sources like Wikidata. This combination is valuable as it enhances the ability to conduct semantic queries, aiding in logically inferring relationships such as symmetry or transitivity in properties.
Transparency and Reliability of the Knowledge Extraction Process
One of the strengths of this approach is its ability to enhance the transparency and reliability of the knowledge extraction process. Seq2seq models, such as mREBEL, are capable of extracting multiple data points from texts, but they may lack transparency in how certain results are reached. In contrast, the system provides users with a clearer view of the extracted data by organizing it into RDF data supported by the original text. This format not only provides a clear view of the extraction process, but also enhances scientists’ ability to verify the accuracy of the extracted information. The important point here is that each RDF statement corresponds to a natural textual statement extracted from the original text, thereby elevating the quality and visibility of extraction processes.
Challenges and Limitations of the Method Used
Despite the numerous benefits, there are challenges and limitations to this knowledge extraction strategy. For example, the approach relies on synthetic data generated by the ChatGPT model, which may lead to what is known as “model hallucinations,” potentially causing repeated errors in the results. Additionally, the complexity of integrating multiple tools makes the system prone to multiple errors at every stage of processing; therefore, it is essential to incorporate human oversight in some extraction steps to improve accuracy.
Plans for Future Development
Developing a methodology for discovering new information within the acquired knowledge base is one of the important points for future work. This effort should take into account the properties, limitations, and patterns in Wikidata, and how this system can be applied to generate knowledge in other literary fields. Furthermore, it will be necessary to build standard metrics for evaluating various knowledge extraction tasks based on specific literary texts, which will enhance the effectiveness of the systems used in this field. By improving large language models like ChatGPT to perform knowledge extraction tasks based on specific reference schemas, the applications of the models trained on instructions can be evaluated more comprehensively.
Large Language Model and Its Uses in Information Extraction
Large language models like ChatGPT and other applicable tools represent remarkable advancements in the field of natural language processing. These models encompass vast information and advanced capabilities that enable them to generate text that resembles human-written text. However, significant issues arise when considering their accuracy and effectiveness in certain contexts, such as information extraction. According to a recent study, it is not ideal to rely on large language models as an effective tool for extracting information from texts; rather, the results indicate they are better viewed as tools for reclassifying challenging samples.
Many studies address the spotlight points that need attention when using large language models. For instance, despite their high capability for processing data, responses to very complex questions or questions that require deep contextual knowledge may be limited. The models require highly nourishing data to improve their capabilities in these areas. Additionally, it becomes evident that as the complexities of the material to be extracted increase, the necessity of employing additional techniques such as reclassification becomes greater.
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Further research has indicated the importance of using models such as BERT and Sentence-BERT in the field of information extraction. These models provide sequential meetings that help in dealing with linguistic complexities more effectively. Hence, it is vital to understand the usefulness of different techniques in specific contexts before applying them randomly.
Challenges in Natural Language Processing for Cultural Heritage
When attempting to apply natural language processing tools in fields such as cultural heritage, numerous unique challenges arise. These challenges range from simple issues like understanding context, to more complex matters such as deep comprehension of cultural and historical discourse. Techniques like entity extraction and text classification require high accuracy since simple mistakes can lead to misleading results.
Cultural and cognitive complexities are an essential part of cultural heritage, thus it requires integrating contextual understanding with language models. For instance, analyzing a literary text from the seventeenth century using a modern model may lead to misunderstanding the absent cultural messages. Therefore, it is crucial to develop tools that comprehend the unique historical and cultural differences when processing texts related to heritage.
Moreover, natural language models face the issue of data availability, as old or revised texts are often not available in digital format, making it difficult to train effective models. Hence, the importance of collaboration between computer scientists and heritage specialists arises to create databases that reflect cultural and literary diversity.
Improving the Quality of Knowledge Graphs with Large Language Models
Knowledge graphs are vital tools in organizing and analyzing information. Enhancing their quality is one of the most important research trends in the field of information technology. Knowledge graphs rely on linking information and creating new relationships, which helps in enhancing querying and knowledge extraction. Here, recent research highlights how large language models can improve this type of data.
Models like BERT can play a key role in identifying relationships based on information derived from massive datasets. It requires exploring patterns in the data to form new links and knowledge. For example, if a model is used to understand texts related to a specific artwork, utilizing knowledge graphs may enable the connection of artists to artworks and related artistic concepts.
With the growing interest in the subject of information quality in knowledge graphs, there is a need for new techniques that can integrate language models to identify aspects such as accuracy and credibility. For instance, creating models to evaluate information and ensure its reliability is considered an important step in developing an information system reliant on big data.
The Future Horizon for Large Language Models and Advancements in Artificial Intelligence
The future seems to hold many opportunities and challenges for large language models and innovations in artificial intelligence. With the rapid developments in this field, there are high hopes concerning the potential to improve the performance of these models across various applications, from literary endeavors to commercial and medical applications.
Regarding the potential for advancement through the integration of existing models and deep learning environments, it is not only about improving the accuracy of current models but also about innovating new models focused on learning from specific contexts. The task here lies in understanding how general knowledge can interconnect with specialized knowledge in certain fields such as medicine, literature, or economics.
Research is not limited to performance improvement alone but also targets ethical issues associated with using artificial intelligence, such as transparency and bias. A good understanding of ethical trends will shape research and development directions in this field in the coming years, enhancing the acceptance and credibility of these tools in society.
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Source: https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2024.1472512/full
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