1,720,962 research outputs found
GraphMatcher: A Graph Representation Learning Approach for Ontology Matching
Full text linkOntology matching is defined as finding a relationship or correspondence
between two or more entities in two or more ontologies. To solve the
interoperability problem of the domain ontologies, semantically similar
entities in these ontologies must be found and aligned before merging them.
GraphMatcher, developed in this study, is an ontology matching system using a
graph attention approach to compute higher-level representation of a class
together with its surrounding terms. The GraphMatcher has obtained remarkable
results in in the Ontology Alignment Evaluation Initiative (OAEI) 2022
conference track. Its codes are available at
~\url{https://github.com/sefeoglu/gat_ontology_matching}.Comment: The 17th International Workshop on Ontology Matching, The 21st
International Semantic Web Conference (ISWC) 2022, 23 October 2022, Hangzhou,
Chin
Fine-grained named entities for Corona news
No full text9297Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured texts is time-consuming without representing them in a structured format; therefore, representing them in a structured format is crucial. An information extraction pipeline with essential tasks-named entity tagging and relation extraction-to accomplish this goal might be applied to these texts. This study proposes a data annotation pipeline to generate training data from corona news articles, including generic and domain-specific entities. Named entity recognition models are trained on this annotated corpus and then evaluated on test sentences manually annotated by domain experts evaluating the performance of a trained model. The code base and demonstration are available at https://github.com/sefeoglu/coronanews-ner.git
Fine-Grained Named Entities for Corona News - Presentation
No full textInformation resources like newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured texts is time-consuming without representing them in a structured format; therefore, representing them in a structured format is crucial. An information extraction pipeline with essential tasks: named entity tagging and relation extraction to accomplish this goal might be applied to these texts. This study proposes a data annotation pipeline to generate training data from corona news articles, including generic and domain-specific entities. Named entity recognition models are trained on this annotated corpus and then evaluated on test sentences manually annotated by domain experts evaluating the performance of a trained model. The code base and demo are available at https://github.com/sefeoglu/coronanews-ner.gi
Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks
Full text linkInformation Extraction (IE) is crucial for converting unstructured data into structured formats like Knowledge Graphs (KGs). A key task within IE is Relation Extraction (RE), which identifies relationships between entities in text. Various RE methods exist, including supervised, unsupervised, weakly supervised, and rule-based approaches. Recent studies leveraging pre-trained language models (PLMs) have shown significant success in this area. In the current era dominated by Large Language Models (LLMs), fine-tuning these models can overcome limitations associated with zero-shot LLM prompting-based RE methods, especially regarding domain adaptation challenges and identifying implicit relations between entities in sentences. These implicit relations, which cannot be easily extracted from a sentence\u27s dependency tree, require logical inference for accurate identification. This work explores the performance of fine-tuned LLMs and their integration into the Retrieval Augmented-based (RAG) RE approach to address the challenges of identifying implicit relations at the sentence level, particularly when LLMs act as generators within the RAG framework. Empirical evaluations on the TACRED, TACRED-Revisited (TACREV), Re-TACRED, and SemEVAL datasets show significant performance improvements with fine-tuned LLMs, including Llama2-7B, Mistral-7B, and T5 (Large). Notably, our approach achieves substantial gains on SemEVAL, where implicit relations are common, surpassing previous results on this dataset. Additionally, our method outperforms previous works on TACRED, TACREV, and Re-TACRED, demonstrating exceptional performance across diverse evaluation scenarios.preprin
Retrieval-Augmented Generation-based Relation Extraction
Full text linkInformation Extraction (IE) is a transformative process that converts
unstructured text data into a structured format by employing entity and
relation extraction (RE) methodologies. The identification of the relation
between a pair of entities plays a crucial role within this framework. Despite
the existence of various techniques for relation extraction, their efficacy
heavily relies on access to labeled data and substantial computational
resources. In addressing these challenges, Large Language Models (LLMs) emerge
as promising solutions; however, they might return hallucinating responses due
to their own training data. To overcome these limitations, Retrieved-Augmented
Generation-based Relation Extraction (RAG4RE) in this work is proposed,
offering a pathway to enhance the performance of relation extraction tasks.
This work evaluated the effectiveness of our RAG4RE approach utilizing
different LLMs. Through the utilization of established benchmarks, such as
TACRED, TACREV, Re-TACRED, and SemEval RE datasets, our aim is to
comprehensively evaluate the efficacy of our RAG4RE approach. In particularly,
we leverage prominent LLMs including Flan T5, Llama2, and Mistral in our
investigation. The results of our study demonstrate that our RAG4RE approach
surpasses performance of traditional RE approaches based solely on LLMs,
particularly evident in the TACRED dataset and its variations. Furthermore, our
approach exhibits remarkable performance compared to previous RE methodologies
across both TACRED and TACREV datasets, underscoring its efficacy and potential
for advancing RE tasks in natural language processing.Comment: Submitted to Semantic Web Journal. Under Revie
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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