Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database
Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis. In this work, we present a new method for constructing a vulnerability knowledge graph from information in the National Vulnerability Database (NVD). Our approach combines named entity recognition (NER), relation extraction (RE), and entity prediction using a combination of neural models, heuristic rules, and knowledge graph embeddings. We demonstrate how our method helps to fix missing entities in knowledge graphs used for cybersecurity and evaluate the performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge Graph EmbeddingsKnowledge Graphsnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERRelation ExtractionSimilar Papers 제목 키워드 기반
Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts
Despite being vast repositories of factual information, cross-domain knowledge graphs, such as Wikidata and the Google Knowledge Graph, only sparsely provide short synoptic descriptions for entities. Such descriptions th…
DecoderEntity DisambiguationKnowledge GraphsScene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models
In this work, we propose the application of abstract meaning representation (AMR) based semantic parsing models to parse textual descriptions of a visual scene into scene graphs, which is the first work to the best of ou…
Abstract Meaning RepresentationAMR ParsingDependency ParsingSemantic ParsingFine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis
Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, and most public datasets contain no clinic…
DiagnosticGraph Neural NetworkGraph Representation LearningMedical Image Analysis+1Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network
With the increasing popularity of posting multimodal messages online, many recent studies have been carried out utilizing both textual and visual information for multi-modal sarcasm detection. In this paper, we investiga…
Sarcasm DetectionLeveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of entities and relations of a given graph by mapping them into a vector space. Although the majority of these methods focus on static knowledge …
Knowledge Graph CompletionKnowledge GraphsLink PredictionRepresentation Learning+3