=Paper=
{{Paper
|id=Vol-1963/paper522
|storemode=property
|title=Frame Embeddings for Event-Based Knowledge Reconciliation
|pdfUrl=https://ceur-ws.org/Vol-1963/paper522.pdf
|volume=Vol-1963
|authors=Mehwish Alam,Diego Reforgiato,Misael Mongiovi,Aldo Gangemi,Petar Ristoski
|dblpUrl=https://dblp.org/rec/conf/semweb/AlamRMGR17
}}
==Frame Embeddings for Event-Based Knowledge Reconciliation==
https://ceur-ws.org/Vol-1963/paper522.pdf
Frame Embeddings for Event-Based Knowledge
Reconciliation
Mehwish Alam1 , Diego Reforgiato Recupero2,3 , Misael Mongiovi2 , Aldo
Gangemi1,2 , Petar Ristoski4
1
LIPN, Université Paris 13, France, 2 ISTC-CNR, Rome, Catania, Italy,
3
University of Cagliari, Italy, 4 University of Mannheim, Germany.
Abstract. This paper focuses on reconciling knowledge graphs gener-
ated from two text documents about similar events described differently.
The proposed approach employs and extends MERGILO, a tool for rec-
onciling knowledge graphs extracted from text, using word similarity and
graph alignment. Our approach effectively handles events using Frame
Embeddings and Frame Based Similarities. It is evaluated over a coref-
erence resolution task.
1 Introduction
This study addresses the problem of knowledge reconciliation (KR) [4] from
the perspective of events. KR is useful in providing a combination of multi-
ple graphs generated by multiple texts describing the same event. This merged
graph provides a graph based summary of multiple texts which is more easily
comprehensible by users and machines and usable by the algorithms providing
interactive exploration of graphs/text analytics through visualization methods.
MERGILO [4] is a tool for reconciling knowledge graphs extracted from
text, it first computes the word similarity between the node labels and then
performs graph alignment over the whole graphs. When different verbs denote
similar events and different agents play slightly different roles, the string match-
ing techniques as introduced in MERGILO might be not appropriate in the
KR process. For overcoming this limitation we use Frame Semantics which de-
scribes a situation in the text with the help of frames and roles. For identifying
frames and semantic roles of entities in a text we use FRED [3], which generates
event-centered knowledge graphs from two different texts. Then, the similarity
between these events is computed by calculating the similarity between the cor-
responding FrameNet frames and semantic roles (frame elements). We adapt
WordNet similarity measures to frames and roles and vector based similarities
using the FrameNet graph and the subsumption hierarchy of roles as defined
in Framester [2]. We follow the approach RDF2Vec [5] to generate graph based
frame embeddings. It uses graph mining algorithms such as graph walks and
graph kernels to traverse the graph for generating sequences, which are then
fed to a neural model for generating its vector representations. Finally, we show
experimentation over Cross-document Coreference Resolution (CCR) reporting
significant improvements over a baseline.
2 Event-Based Knowledge Reconciliation
Consider the two sentences: “The Spaniards conquered the Incas.” and “The In-
cas were invaded by the Spaniards.” They are describing the same event in the
past using different words i.e., event of an attack or an invasion from Spaniards
to Incas. Figure 1 shows the FRED graph of the first sentence. Given two
such knowledge graphs, MERGILO first performs graph compression by merging
nodes in the same graph. The two compressed graphs are aligned by establishing
a 1-1 correspondence between nodes of the two graphs by maximizing a score
function, which combines the similarity between aligned nodes and the similarity
between aligned edges. In such a case, the similarity between “conquered” and
“invaded” is not effective since word similarity is low, although in this context
such words describe the same event.
Fig. 1: FRED Knowledge Graph for The Spaniards conquered the Incas.
For computing similarity between two nodes containing verb senses, the verb
senses are first mapped to frames using Framester mappings. For example, in
Figure 1 s1 “vn.data1 :Conquer 42030000 and for second sentence we have
s2 “vn.data:Invade 10000000. According to Framester mappings, we obtain
s1 Ñ tConqueringu and s2 Ñ tAttacku. These nodes are replaced by their
corresponding frames. The edges containing the VN-roles are mapped to FN-
roles. For example, in Figure 1, the verb sense vn.data:Conquer 42030000
evokes the roles vn.role:Agent and vn.role:Patient which are mapped to
fe:Conqueror.conquering and fe:Theme.conquering respectively.
Then the similarities are computed in two ways: (i) by considering the tax-
onomical structure imposed by the “inheritance” relation represented as
fnschema2 :inheritsFrom in Framester using Path Similarity, Wu-Palmers Sim-
ilarity, Leacock-Chodorow Similarity; (ii) using Frame Embeddings.
1
prefix vn.data: http://www.ontologydesignpatterns.org/ont/vn/vn31/data/
2
prefix fnschema: http://www.ontologydesignpatterns.org/ont/framenet/tbox/
Frame Embeddings using RDF2Vec: To learn latent numerical representation of
the frames and roles in the FrameNet graph, we follow the RDF2Vec approach.
First we transform the graph into a set of sequences of entities, which is then
fed into a neural language models, resulting into vector representation of all the
nodes in the graph in a latent feature space.
To convert the graph into a set of sequences of entities we use two approaches,
i.e., graph walks and Weisfeiler-Lehman Subtree RDF Graph Kernels. (i) Graph
Walks: given a graph G “ pV, Eq, for each vertex v P V , we generate all graph
walks Pv of depth d rooted in vertex v. To generate the walks, we use the breadth-
first algorithm. In the first iteration, the algorithm generates paths by exploring
the direct outgoing edges of the root node vr . In the second iteration, for each of
the previously explored edges, the algorithm visits the connected vertices. The
final set of sequences Ť for the given graph G is the union of the sequences of
all the vertices PG “ vPV Pv . (ii) Graph Kernels: it computes the number of
sub-trees shared between two or more graphs by using the Weisfeiler-Lehman [1]
test of graph isomorphism. This algorithm creates labels representing subtrees.
Once the set of sequences of entities is extracted, we build a word2vec model.
Word2vec is a particularly computationally-efficient two-layer neural net model
for learning word embeddings from raw text. There are two different algorithms,
the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model. The
CBOW model predicts target words from context words within a given window.
The input layer is comprised from all the surrounding words for which the input
vectors are retrieved from the input weight matrix, averaged, and projected in
the projection layer. Then, using the weights from the output weight matrix,
a score for each word in the vocabulary is computed, which is the probability
of the word being a target word. The skip-gram model does the inverse of the
CBOW model. Once the training is finished, the cosine similarity is computed
between two frames and roles.
3 Experimentation
The experimentations were conducted for the task of Cross-document Corefer-
ence Resolution on RDF graphs, which focuses on associating RDF nodes about
a same entity (object, person, concept, etc.) across different RDF graphs gener-
ated from text. The dataset used for the experimentation was obtained by the
EECB dataset which specifies coreferent mentions (text fragment). Our dataset
was obtained by generating RDF graphs using FRED and associating text men-
sions to graph nodes by manual annotations. Following are the metrics used
for evaluations3 : (1) MUC is a link-based metric that quantifies the number of
merges necessary to cover predicted and gold clusters (ground truth). (2) B 3 is
a mention-based metric that quantifies the overlap between predicted and gold
clusters for a given mention. (3) CEAFM (Constrained Entity Aligned F-measure
Mention-based) measures the number of corresponding mentions in the optimal
3
The formulas of Precision, Recall and F1 are suppressed because of space constraints.
one-to-one alignment between gold and predicted clusters. (4) CEAFE (Con-
strained Entity Aligned F-measure Entity-Based) measures the overlap between
gold and predicted clusters in their optimal one-to-one alignment.
MERGILO was considered as the baseline. Table 1 shows the results for
Wu-Palmer’s similarity, Path similarity and Leacock-Chodorow similarity and
the results for cosine similarity using (i) graph walks, (ii) graph kernels. Here
Frame2Vec and Role2Vec refers to the vector representations generated for
FrameNet frames and frame elements i.e., semantic roles respectively. We fur-
ther built CBOW and Skip-Gram models with the following parameters: window
size = 5; number of iterations = 10; negative sampling for optimization; nega-
tive samples = 25; with average input vector for CBOW. We experiment with
200, 500 and 800 dimensions. These results are compared with MERGILO. Each
model used for graph walks and graph kernels perform better for all the con-
sidered metrics, showing a clear advantage of using the proposed approach. The
generated models are freely available on-line4 .
muc bcub ceafm ceafe
MERGILO Baseline 24.05 17.36 28.61 26.20
Similarity Measures
Wu-Palmer 27.14 19.91 31.91 29.41
Path 27.16 19.93 31.85 29.38
Leacock Chodorow 27.04 19.80 31.74 29.21
Graph Walks
Frame2Vec Role2Vec muc bcub ceafm ceafe
CBOW 200 CBOW 200 27.34 19.99 32.15 29.82
CBOW 200 SG 800 27.38 19.97 32.29 29.98
CBOW 200 SG 500 27.28 19.95 31.99 29.54
Graph Kernels
Frame2Vec Role2Vec muc bcub ceafm ceafe
CBOW 200 CBOW 200 26.76 19.57 31.50 29.06
CBOW 200 SG 200 26.70 19.52 31.45 28.99
CBOW 200 SG 500 26.70 19.52 31.45 28.99
SG 500 CBOW 200 26.90 19.68 31.58 29.08
Table 1: Results and comparisons between MERGILO and the proposed approach.
4 Perspectives
Ongoing work includes application of frame embeddings in real systems, such as
news series integration, knowledge graph evolution with robust event reconcili-
ation (e.g. text streaming) etc.
References
1. G. de Vries and S. de Rooij. Substructure counting graph kernels for machine
learning from rdf data. J. Web Sem., 35, 2015.
2. A. Gangemi, M. Alam, L. Asprino, V. Presutti, and D. Reforgiato. Framester: A
wide coverage linguistic linked data hub. In EKAW 2016., 2016.
3. A. Gangemi, V. Presutti, D. R. Recupero, A. G. Nuzzolese, F. Draicchio, and
M. Mongiovı̀. Semantic web machine reading with FRED. Semantic Web, 8(6),
2017.
4. M. Mongiovı̀, D. Reforgiato, A. Gangemi, V. Presutti, and S. Consoli. Merging open
knowledge extracted from text with MERGILO. Knowl.-Based Syst., 108, 2016.
5. P. Ristoski and H. Paulheim. RDF2Vec: RDF Graph Embeddings for Data Mining.
In ISWC 2016, 2016.
4
http://lipn.univ-paris13.fr/~alam/Frame2Vec/