diff --git a/besskge/embedding.py b/besskge/embedding.py
index ce04c54..86244da 100644
--- a/besskge/embedding.py
+++ b/besskge/embedding.py
@@ -26,6 +26,24 @@ def init_uniform_norm(embedding_table: torch.Tensor) -> torch.Tensor:
     return torch.nn.functional.normalize(torch.nn.init.uniform(embedding_table), dim=-1)
 
 
+def init_xavier_norm(embedding_table: torch.Tensor, gain: float = 1.0) -> torch.Tensor:
+    """
+    Initialize embeddings according to Xavier normal scheme, with
+    `fan_in = 0`, `fan_out=row_size`.
+
+    :param embedding_table:
+        Tensor of embedding parameters to initialize.
+    :param gain:
+        Scaling factor for standard deviation. Default: 1.0.
+
+    :return:
+        Initialized tensor.
+    """
+    return torch.nn.init.normal_(
+        embedding_table, std=gain * np.sqrt(2.0 / embedding_table.shape[-1])
+    )
+
+
 def init_KGE_uniform(
     embedding_table: torch.Tensor, b: float = 1.0, divide_by_embedding_size: bool = True
 ) -> torch.Tensor:
diff --git a/besskge/scoring.py b/besskge/scoring.py
index cf4173b..7c0b985 100644
--- a/besskge/scoring.py
+++ b/besskge/scoring.py
@@ -15,6 +15,7 @@
     init_KGE_normal,
     init_KGE_uniform,
     init_uniform_norm,
+    init_xavier_norm,
     initialize_entity_embedding,
     initialize_relation_embedding,
     refactor_embedding_sharding,
@@ -291,7 +292,7 @@ def __init__(
         :param relation_initializer:
             Initialization function or table for relation embeddings.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(TransE, self).__init__(
             negative_sample_sharing=negative_sample_sharing, scoring_norm=scoring_norm
@@ -391,7 +392,7 @@ def __init__(
         :param relation_initializer:
             Initialization function or table for relation embeddings.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(RotatE, self).__init__(
             negative_sample_sharing=negative_sample_sharing, scoring_norm=scoring_norm
@@ -502,7 +503,7 @@ def __init__(
             If True, L2-normalize head and tail entity embeddings before projecting,
             as in :cite:p:`PairRE`. Default: True.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(PairRE, self).__init__(
             negative_sample_sharing=negative_sample_sharing, scoring_norm=scoring_norm
@@ -637,7 +638,7 @@ def __init__(
             Offset factor for head/tail relation projections, as in TripleREv2.
             Default: 0.0 (no offset).
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(TripleRE, self).__init__(
             negative_sample_sharing=negative_sample_sharing, scoring_norm=scoring_norm
@@ -776,7 +777,7 @@ def __init__(
         :param relation_initializer:
             Initialization function or table for relation embeddings.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(DistMult, self).__init__(negative_sample_sharing=negative_sample_sharing)
 
@@ -870,7 +871,7 @@ def __init__(
         :param relation_initializer:
             Initialization function or table for relation embeddings.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(ComplEx, self).__init__(negative_sample_sharing=negative_sample_sharing)
 
@@ -944,6 +945,206 @@ def score_tails(
         )
 
 
+class ConvE(MatrixDecompositionScoreFunction):
+    """
+    ConvE scoring function :cite:p:`ConvE`.
+
+    Note that, differently from :cite:p:`ConvE`, the scores returned by this class
+    have not been passed through a final sigmoid layer, as we assume that this is
+    included in the loss function.
+
+    By design, this scoring function should be used in combination with a
+    negative/candidate sampler that only corrupts tails (possibly after
+    including all inverse triples in the dataset, see the `add_inverse_triples`
+    argument in :func:`besskge.sharding.PartitionedTripleSet.create_from_dataset`).
+    """
+
+    def __init__(
+        self,
+        negative_sample_sharing: bool,
+        sharding: Sharding,
+        n_relation_type: int,
+        embedding_size: int,
+        embedding_height: int,
+        embedding_width: int,
+        entity_initializer: Union[torch.Tensor, List[Callable[..., torch.Tensor]]] = [
+            init_xavier_norm,
+            torch.nn.init.zeros_,
+        ],
+        relation_initializer: Union[torch.Tensor, List[Callable[..., torch.Tensor]]] = [
+            init_xavier_norm,
+        ],
+        inverse_relations: bool = True,
+        input_channels: int = 1,
+        output_channels: int = 32,
+        kernel_height: int = 3,
+        kernel_width: int = 3,
+        input_dropout: float = 0.2,
+        feature_map_dropout: float = 0.2,
+        hidden_dropout: float = 0.3,
+        batch_normalization: bool = True,
+    ) -> None:
+        """
+        Initialize ConvE model.
+
+        :param negative_sample_sharing:
+            see :meth:`DistanceBasedScoreFunction.__init__`
+        :param sharding:
+            Entity sharding.
+        :param n_relation_type:
+            Number of relation types in the knowledge graph.
+        :param embedding_size:
+            Size of entity and relation embeddings.
+        :param embedding_height:
+            Height of the 2D-reshaping of the concatenation of
+            head and relation embeddings.
+        :param embedding_width:
+            Width of the 2D-reshaping of the concatenation of
+            head and relation embeddings.
+        :param entity_initializer:
+            Initialization functions or table for entity embeddings.
+            If not passing a table, two functions are needed: the initializer
+            for entity embeddings and initializer for (scalar) tail biases.
+        :param relation_initializer:
+            Initialization function or table for relation embeddings.
+        :param inverse_relations:
+            If True, learn embeddings for inverse relations. Default: True.
+        :param input_channels:
+            Number of input channels of the Conv2D operator. Default: 1.
+        :param output_channels:
+            Number of output channels of the Conv2D operator. Default: 32.
+        :param kernel_height:
+            Height of the Conv2D kernel. Default: 3.
+        :param kernel_width:
+            Width of the Conv2D kernel. Default: 3.
+        :param input_dropout:
+            Rate of Dropout applied before the convolution. Default: 0.2.
+        :param feature_map_dropout:
+            Rate of Dropout applied after the convolution. Default: 0.2.
+        :param hidden_dropout:
+            Rate of Dropout applied after the Linear layer. Default: 0.3.
+        :param batch_normalization:
+            If True, apply batch normalization before and after the
+            convolution and after the Linear layer. Default: True.
+        """
+        super(ConvE, self).__init__(negative_sample_sharing=negative_sample_sharing)
+
+        self.sharding = sharding
+
+        if input_channels * embedding_width * embedding_height != embedding_size:
+            raise ValueError(
+                "`embedding_size` needs to be equal to"
+                " `input_channels * embedding_width * embedding_height`"
+            )
+
+        # self.entity_embedding[..., :embedding_size] entity_embeddings
+        # self.entity_embedding[..., -1] tail biases
+        self.entity_embedding = initialize_entity_embedding(
+            self.sharding, entity_initializer, [embedding_size, 1]
+        )
+        self.relation_embedding = initialize_relation_embedding(
+            n_relation_type, inverse_relations, relation_initializer, [embedding_size]
+        )
+        assert (
+            self.entity_embedding.shape[-1] - 1
+            == self.relation_embedding.shape[-1]
+            == embedding_size
+        ), (
+            "ConvE requires `embedding_size + 1` embedding parameters for each entity"
+            " and `embedding_size` embedding parameters for each relation"
+        )
+        self.embedding_size = embedding_size
+
+        self.inp_channels = input_channels
+        self.emb_h = embedding_height
+        self.emb_w = embedding_width
+        conv_layers = [
+            torch.nn.Dropout(input_dropout),
+            torch.nn.Conv2d(
+                in_channels=self.inp_channels,
+                out_channels=output_channels,
+                kernel_size=(kernel_height, kernel_width),
+            ),
+            torch.nn.ReLU(),
+            torch.nn.Dropout2d(feature_map_dropout),
+        ]
+        fc_layers = [
+            torch.nn.Linear(
+                output_channels
+                * (2 * self.emb_h - kernel_height + 1)
+                * (self.emb_w - kernel_width + 1),
+                embedding_size,
+            ),
+            torch.nn.Dropout(hidden_dropout),
+            torch.nn.ReLU(),
+        ]
+        if batch_normalization:
+            conv_layers.insert(0, torch.nn.BatchNorm2d(input_channels))
+            conv_layers.insert(3, torch.nn.BatchNorm2d(output_channels))
+            fc_layers.insert(2, torch.nn.BatchNorm1d(embedding_size))
+        self.conv_layers = torch.nn.Sequential(*conv_layers)
+        self.fc_layers = torch.nn.Sequential(*fc_layers)
+
+    # docstr-coverage: inherited
+    def score_triple(
+        self,
+        head_emb: torch.Tensor,
+        relation_id: torch.Tensor,
+        tail_emb: torch.Tensor,
+    ) -> torch.Tensor:
+        relation_emb = torch.index_select(
+            self.relation_embedding, index=relation_id, dim=0
+        )
+        # Discard bias for heads
+        head_emb = head_emb[..., :-1]
+        tail_emb, tail_bias = torch.split(tail_emb, self.embedding_size, dim=-1)
+        hr_cat = torch.cat(
+            [
+                head_emb.view(-1, self.inp_channels, self.emb_h, self.emb_w),
+                relation_emb.view(-1, self.inp_channels, self.emb_h, self.emb_w),
+            ],
+            dim=-2,
+        )
+        hr_conv = self.fc_layers(self.conv_layers(hr_cat).flatten(start_dim=1))
+        return self.reduce_embedding(hr_conv * tail_emb) + tail_bias.squeeze(-1)
+
+    # docstr-coverage: inherited
+    def score_heads(
+        self,
+        head_emb: torch.Tensor,
+        relation_id: torch.Tensor,
+        tail_emb: torch.Tensor,
+    ) -> torch.Tensor:
+        raise NotImplementedError("ConvE should not be used with head corruption")
+
+    # docstr-coverage: inherited
+    def score_tails(
+        self,
+        head_emb: torch.Tensor,
+        relation_id: torch.Tensor,
+        tail_emb: torch.Tensor,
+    ) -> torch.Tensor:
+        relation_emb = torch.index_select(
+            self.relation_embedding, index=relation_id, dim=0
+        )
+        # Discard bias for heads
+        head_emb = head_emb[..., :-1]
+        tail_emb, tail_bias = torch.split(tail_emb, self.embedding_size, dim=-1)
+        if self.negative_sample_sharing:
+            tail_bias = tail_bias.view(1, -1)
+        else:
+            tail_bias = tail_bias.squeeze(-1)
+        hr_cat = torch.cat(
+            [
+                head_emb.view(-1, self.inp_channels, self.emb_h, self.emb_w),
+                relation_emb.view(-1, self.inp_channels, self.emb_h, self.emb_w),
+            ],
+            dim=-2,
+        )
+        hr_conv = self.fc_layers(self.conv_layers(hr_cat).flatten(start_dim=1))
+        return self.broadcasted_dot_product(hr_conv, tail_emb) + tail_bias
+
+
 class BoxE(DistanceBasedScoreFunction):
     """
     BoxE scoring function :cite:p:`BoxE`.
@@ -1000,7 +1201,7 @@ def __init__(
             Softening parameter for geometric normalization of box widths.
             Default: 1e-6.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(BoxE, self).__init__(
             negative_sample_sharing=negative_sample_sharing, scoring_norm=scoring_norm
@@ -1258,7 +1459,7 @@ def __init__(
         :param offset:
             Offset applied to auxiliary entity embeddings. Default: 1.0.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(InterHT, self).__init__(
             negative_sample_sharing=negative_sample_sharing, scoring_norm=scoring_norm
@@ -1415,7 +1616,7 @@ def __init__(
         :param offset:
             Offset applied to tilde entity embeddings. Default: 1.0.
         :param inverse_relations:
-            If True, learn embeddings for inverse relations. Default: False
+            If True, learn embeddings for inverse relations. Default: False.
         """
         super(TranS, self).__init__(
             negative_sample_sharing=negative_sample_sharing, scoring_norm=scoring_norm