OKGIT: Open Knowledge Graph Link Prediction with Implicit Types
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OKGIT: Open Knowledge Graph Link Prediction with Implicit Types
Open Knowledge Graphs (OpenKG) refer to a set of (head noun phrase, relation phrase, tail noun phrase) triples such as (tesla, return to, new york) extracted from a corpus using OpenIE tools. While OpenKGs are easy to bo…
Knowledge GraphsLink PredictionPredictionQuestion Answering+2Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction
Open Information Extraction systems extract ({``}subject text{''}, {``}relation text{''}, {``}object text{''}) triples from raw text. Some triples are textual versions of facts, i.e., non-canonicalized mentions of entiti…
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We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual des…
Knowledge Graph CompletionLink PredictionPredictionWord EmbeddingsLinking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information
Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a rich source of openly available, volunteered geographic information …
Knowledge GraphsLink PredictionOpen-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs
An open-domain knowledge graph (KG) has entities as nodes and natural language relations as edges, and is constructed by extracting (subject, relation, object) triples from text. The task of open-domain link prediction i…
Link PredictionPrediction