1,259 research outputs found
TechMiner: Extracting Technologies from Academic Publications
In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision
A knowledge graph embeddings based approach for author name disambiguation using literals
Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github (https://github.com/sntcristian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations
Electronic Theses and Dissertations (ETDs) contain domain knowledge that can
be used for many digital library tasks, such as analyzing citation networks and
predicting research trends. Automatic metadata extraction is important to build
scalable digital library search engines. Most existing methods are designed for
born-digital documents, so they often fail to extract metadata from scanned
documents such as for ETDs. Traditional sequence tagging methods mainly rely on
text-based features. In this paper, we propose a conditional random field (CRF)
model that combines text-based and visual features. To verify the robustness of
our model, we extended an existing corpus and created a new ground truth corpus
consisting of 500 ETD cover pages with human validated metadata. Our
experiments show that CRF with visual features outperformed both a heuristic
and a CRF model with only text-based features. The proposed model achieved
81.3%-96% F1 measure on seven metadata fields. The data and source code are
publicly available on Google Drive (https://tinyurl.com/y8kxzwrp) and a GitHub
repository (https://github.com/lamps-lab/ETDMiner/tree/master/etd_crf),
respectively.Comment: 7 pages, 4 figures, 1 table. Accepted by JCDL '21 as a short pape
- …