26 papers · ranked by Valyu relevance
Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu
—Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels, e.g., a user…
Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
—Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new…
Fritz Hohl, Nianheng Wu, Martina Galetti, Remi van Trijp
Despite enormous progress in Natural Language Processing (NLP), our field is still lacking a common deep semantic representation scheme. As a result, the problem of meaning and understanding is typically sidestepped through more simple, approximative methods. This paper argues that in order to arrive at such a scheme…
Xuhui Li
Semantic information is often represented as the entities and the relationships among them with conventional semantic models. This approach is straightforward but is not suitable for many posteriori requests in semantic data modeling. In this paper, we propose a meaningoriented approach to modeling semantic data and…
Chen Huang, Keliang Cen, Yang Zhang, Bo Liu + 2 more
Prior studies have suggested close associations between miRNAs and diseases. Correct prediction of potential miRNA-disease pairs by computational methods is able to greatly accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information in the…
Trey Grainger, Khalifeh AlJadda, Mohammed Korayem, Andries Smith
—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting…
P. F. Ding, Yan Wang, Guanfeng Liu, Xiaofang Zhou
—Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types of objects and different types of links. In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data. Since a…
Saeed Salem, Cagri Ozcaglar
Background Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of…
Gunther Eysenbach, Illhoi Yoo, Sarah Lim Choi Keung, Ali Zahrawi + 8 more
Background The Unified Medical Language System (UMLS) contains many important ontologies in which terms are connected by semantic relations. For many studies on the relationships between biomedical concepts, the use of transitively associated information from ontologies and the UMLS has been shown to be effective.…
Eric Ke Wang, Futai Zou
With the development of social networks, people have started to use social network tools to record their life and work more and more frequently. How to analyze social networks to explore potential characteristics and trend of social events has been a hot research topic. In order to analyze it effectively, a kind of…
Dirk Weissenborn, Michael Schroeder, George Tsatsaronis
Background The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the…
Ilya Levin
This article describes Amorpha, a software package based on new concept of exact graph-based linguistic analysis. Analytical capabilities of Amorpha are demonstrated using analysis of scientific abstracts on clinical trials from PubMed. Current trends in therapy of breast cancer and psoriatic arthritis were analyzed…
Charles Tapley Hoyt, Klas Karis, Benjamin M. Gyori
Hundreds of resources assign identifiers to biomedical concepts including genes, small molecules, biological processes, diseases, and cell types. Often, these resources overlap by assigning identifiers to the same or related concepts. This creates a data interoperability bottleneck, as integrating data sets and…
Andre Lamurias, Pedro Ruas, Francisco M. Couto
Background Biomedical literature concerns a wide range of concepts, requiring controlled vocabularies to maintain a consistent terminology across different research groups. However, as new concepts are introduced, biomedical literature is prone to ambiguity, specifically in fields that are advancing more rapidly, for…
Vinh Quang Nguyễn, Jyoti Leeka, Olivier Bodenreider, Amit Sheth
Formalizing an RDF abstract graph model to be compatible with the RDF formal semantics has remained one of the foundational problems in the Semantic Web. In this paper, we propose a new formal graph model for RDF datasets. This model allows us to express the current model-theoretic semantics in the form of a graph. We…
Zixuan Zhu, Yuhai Zhao
Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag…
Authors not listed
A directed graph (or digraph) consists of a finite vertex set 𝑉 and a set of ordered edges 𝐸 ⊆ 𝑉 × 𝑉, each edge (𝑢, 𝑣) indicating a one-way connection from 𝑢 (source) to 𝑣 (target). A bidirected graph is a generalization of an undirected graph where each edge is assigned a direction at each of its endpoints…
Authors not listed
In the real world, many reversal phenomena occur—for example, cases in which a statement once regarded as false is later recognized as true. Upside-Down Logic is a framework designed to formalize such reversal phenomena as a logical system. It inverts the truth and falsity of propositions through contextual…
Authors not listed
We present a unified, set–theoretic framework that extends molecular graphs to hypergraphs and superhypergraphs via iterated power sets. We define Molecular Graphs, Molecular HyperGraphs, and Molecular SuperHyperGraphs, and develop four complements over them: Weighted, Rough, Neural, and Multipolar frameworks. We prove…
Marcel H Schulz, Sebastian Köhler, Sebastian Bauer, Peter N Robinson
Background Semantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic…
Favour James, Christopher Churas, Dexter Pratt, Augustin Luna
Knowledge graphs (KGs) are powerful tools for structuring and analyzing biological information due to their ability to represent data and improve queries across heterogeneous datasets. However, constructing KGs from unstructured literature remains challenging due to the cost and expertise required for manual curation.…
Authors not listed
Graph theory provides a framework for clearly representing relationships between objects [1,2]. In the fields of chemistry and biology, graph-based concepts are widely applied. Hypergraphs generalize classical graphs by allowing hyperedges to connect any nonempty subset of vertices [3]. Superhypergraphs extend this…
Anna Kirkpatrick, Chidozie Onyeze, David Kartchner, Stephen Allegri + 4 more
Information Networks: SemNet 2.0 Authors: ['Anna Kirkpatrick' 'Chidozie Onyeze' 'David Kartchner' 'Stephen Allegri' 'Davi Nakajima An' 'Kevin McCoy' 'Evie Davalbhakta' 'Cassie S. Mitchell'] Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework…
Rachana Niranjan Murthy, Sai Teja Potu, Akhil Thomas, Lokesh Mishra + 2 more
Retrieving structured materials information from unstructured textual data is essential for data mining and automatically developing comprehensive ontologies. Information extraction is a complex task composed of multiple subtasks and thus often relies on systems of task-specialized language models. A foundation…
Vignesh Prabhakar, Chau Vu, Jennifer Crawford, Joseph Waite + 1 more
Generating knowledge graph embeddings (KGEs) to represent entities (nodes) and relations (edges) in large scale knowledge graph datasets has been a challenging problem in representation learning. This is primarily because the embeddings / vector representations that are required to encode the full scope of data in a…
Luca Menestrina, Maurizio Recanatini
In this study, we present PATHOS (PATHologies of HOmo Sapiens), a semantically rich knowledge graph constructed by integrating diverse datasets spanning multiple biomedical entity types. PATHOS provides a comprehensive resource for representing and exploring the intricate relationships underlying human diseases. To…