A Summary of Recent SDA Contributions towards improving Entity Disambiguation, Linking and Prediction

Improvement of State of the Art on different Tasks.
Figure 1: Overall Approach: Φrefers to the ordered set of triples from the KG for a candidate entity while Φ max ⊆ Φ, is the maximum number of triples that fits in the sequence length. For brevity: N→”National”, H→”Highway”, desc→”description.
Figure 2: The red and green dots represent entity candidate vectors for the given question. The green vectors are the correct entity vectors. Although they belong to the same entity they are not the same dots because they come from different n-grams. At each time step the Pointer Network points to one of the input candidate entities as the linked entity, or to the END symbol to indicate nochoice.
Figure 3: Proposed Approach Arjun: Arjun consists of three tasks. First task identifies the surface forms using an attentive neural network. Second task induces background knowledge from the Local KG and associates each surface form with potential entity candidates. Third task links the potential entity candidates to the correct entity labels.
Figure 4: The mechanism in which STARE encodes a hyper-relational fact from Fig. 1.B. Qualifier pairs are passed through a composition functionφq, summed and transformed by Wq. The resulting vector is then merged viaγ, andφrwith the relation and object vector, respectively. Finally, nodeQ937aggregates mes-sages from this and other hyper-relational edges.
Figure 5: Geometric illustration of the translation terms considered in MDE.
Figure 6: illustration of the means and (diagonal) variances of entities and relations in a temporal Gaussian Embedding Space. The labels indicate their position. In the representations, we might infer that Bill ClintonwaspresidentOf USAi n 1998 and Barack Obama was president of USA in 2010



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SDA Research

SDA Research


The Smart Data Analytics (SDA) research group at the University of Bonn working on #semantics, #machinelearning and #bigdata.