Knowledge Graph Alignment using String Edit Distance
In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity between relations of any arity
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A New String Edit Distance and Applications
String edit distances have been used for decades in applications ranging from spelling correction and web search suggestions to DNA analysis. Most string edit distances are variations of the Levenshtein distance and cons…
DNA analysisSpelling CorrectionNeural String Edit Distance
We propose the neural string edit distance model for string-pair matching and string transduction based on learnable string edit distance. We modify the original expectation-maximization learned edit distance algorithm i…
ClassificationGeneral ClassificationGrapheme-to-Phoneme ConversionTransliterationConvolutional Embedding for Edit Distance
Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment. However, computing edit distance is known to have high complexity, which makes str…
TripletImagined-Trailing-Whitespace-Agnostic Levenshtein Distance For Plaintext Table Detection
The standard algorithm for Levenshtein distance, treats trailing whitespace the same as any other letter or symbol. However, when humans compare 2 strings, we implicitly assume that both strings are padded by infinite tr…
Table DetectionSpace-efficient Feature Maps for String Alignment Kernels
String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in v…