Abstract. We present two knowledge-rich methods for ranking entities in a semantic network. Our approach relies on the DBpedia knowledge base for acquiring fine-grained information about entities and their semantic relations. Experiments on a benchmarking dataset show the viability of our approach

Michael Schuhmacher

Simone Paolo Ponzetto

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Ranking Entities in a Large Semantic Network
Michael Schuhmacher, Simone Paolo Ponzetto
Research Group Data and Web Science, University of Mannheim
{michael,simone}@informatik.uni-mannheim.de
Abstract. We present two knowledge-rich methods for ranking entities
in a semantic network. Our approach relies on the DBpedia knowledge
base for acquiring fine-grained information about entities and their se-
mantic relations. Experiments on a benchmarking dataset show the via-
bility of our approach.
1 Introduction
Entity ranking [2] is the task of ordering a given set of entities on the basis of
their relevance with respect to a reference entity. As an example, “Apple Inc.”
can have different degrees of association with other entities, ranging from highly
related ones (“Steve Jobs”) to mildly (“NeXT”) or marginally relevant ones
(“Ford Motor Company”) – see Figure 1. Entity ranking can be produced auto-
matically by computing the degree of semantic relatedness between the reference
entity, and each of the other entities of interest. Much work in the field of Natu-
ral Language Processing has focused on knowledge-rich approaches to semantic
relatedness [5]. However, almost all approaches using knowledge resources rely
on the hierarchical structure of a taxonomy, typically WordNet, as opposed to
full-fledged semantic networks – like, for instance, DBpedia [1] – containing fine-
grained, explicit semantic relations, and whose taxonomic backbone represents
only a fraction of the semantic information they encode.
2 Knowledge-based entity ranking
We study two knowledge-rich methods to rank entities in a semantic network.
Path-based method. We compute relatedness directly on the basis of the
cheapest path between two entities. This leverages information from the knowl-
edge base by means of a weighting method that takes into account the explicit
semantic relations found within the resource (see [4] for details):
1) We build from the set of input entities a labeled, directed graph contain-
ing the entities themselves and all intermediate entities and relations in the
knowledge base.
2) We weight graph edges by edge cost, where weights capture the degree of
associativity between the source and target nodes. We use the information-
theoretic measures of [4] to capture different degrees of associations between
entities in the semantic network on the basis of their specificity.
2 Schuhmacher, Ponzetto
Edge 
Weighting
1. Steve Jobs
2. Steve Wozniak
...
10. NeXT 
11. Safari (web browser)
...
20. Ford Motor Company
DBpedia
Entity 
Disambiguator
Edge WeightingSimilarity Score
Document Graph 
Extraction
DBpedia
Document Graph 
Matching
Candidate Entities:
Ford Motor Company, Steve 
Jobs, NeXT, Steve Wozniak, 
Safari (web browser), ...
Desire, a key folk music 
album from the 70’s, is 
mostly known for 
Mozambique.
Dylan played Mozambique at 
Monterey right before Cash. 1 2
34
Entity Graph 
Extraction
1
2
R
ef
e
re
n
ce
:
A
p
p
le
 In
c.
R
an
ke
d
 b
y 
R
e
la
te
d
n
e
ss
:
Path Finding
Graph Matching
3
Fig. 1. Entity ranking workflow.
3) We compute semantic dista ces between entity p irs – i.e., the reference
entity and each of the entities of interest – as the minimum path cost between
them in ou weighted graph. Finally, we rank the entity pairs increasingly by
semantic distance.
Graph-mat hing method. The approach described so far relies on entities be-
ing connected by meaningful semantic relations in the reference resource. How-
ever, this requirement could be too strict for some entities, namely those for
which very few or no informative semantic relations exist in the underlying on-
tology (e.g., technical terms like “Oxygen fluoride” or “Manifold”). Even more
problematic, this method cannot be applied to entities that are not found in
the knowledge base – e.g., how much related is “Simone Ponzetto” with “I-
SEMANTICS”? For this reason, we explore a second, alternative approach:
1) We represent each entity using the set of entities linked within the abstract
of the corresponding (English) Wikipedia article. For example, “Manifold”
links to “Topological Space”, “Lemniscate”, “Klein bottle”, and so on.
2) For each set of entities, we build a weighted semantic graph following the
previously described graph construction method, in order to identify the sub-
graph of DBpedia covered by each definition.
3) Given that each entity is now represented as a subgraph, we view computing
relatedness as a graph comparison problem, and compute relatedness using
a Graph Edit Distance based measure, which finds the optimal matching
between two entity-based graphs using the Hungarian method [3].
Our hunch here is to use a knowledge-rich text similarity method applied to
the entities’ textual descriptions in order to overcome the limited availability
of semantic relations for some entities in the knowledge base. Crucially, this
method enables knowledge-rich entity ranking even for those entities which are
not in the knowledge base, provided they can be associated with a semantified
textual description. To this end, we build upon the work from [4] who present
a structure-based method to compute semantic similarity between documents,
here applied to compute entity relatedness instead.
Ranking Entities in a Very Large Semantic Network 3
Path-based Graph-based
baseline jointIC combIC IC+PMI baseline jointIC combIC IC+PMI
Hollywood Celebr. 0.639 0.541 0.690 0.661 0.439 0.506 0.417 0.401
IT Companies 0.559 0.636 0.644 0.583 0.355 0.446 0.298 0.278
Television Series 0.529 0.595 0.643 0.602 0.302 0.473 0.300 0.280
Video Games 0.451 0.562 0.532 0.484 0.552 0.519 0.434 0.424
Chuck Norris 0.458 0.409 0.558 0.506 0.448 0.544 0.425 0.291
All 0.541 0.575 0.624 0.579 0.414 0.489 0.365 0.343
Table 1. Performance on the entity ranking KORE dataset.
3 Experiments
Experimental setting. We use the KORE entity ranking dataset [2], consist-
ing of 21 different reference entities from four different domains. Relatedness
assessments were obtained using a crowd-sourcing approach. We evaluate using
Spearman’s rank correlation (ρ) and DBpedia 3.8 as knowledge base.
Results and discussion. The results in Table 1 indicate that weighing paths
based on their information content (as introduced in [4]) consistently outper-
forms a baseline approach that simply computes entity relatedness as a function
of distance in the network. In the case of the path-based approach, the best
weighting schema is combIC, which achieves an average increase of 15.5% (sta-
tistically significant for each task at p ≤ .001 level with paired t-test).
The graph-matching approach always performs lower than the cheapest path
based method. Error analysis revealed that this is due to the fact that, although
the Wikipedia abstracts from which entity graphs are built provide us with
an enriched context, they also introduce noise deriving from generic entities –
especially in the case of popular (and hence, highly hyperlinked) entities. For
instance, in the abstract for “Apple Inc.” we found hyperlinks to “Coca-Cola”
and “Fortune 500”. While a context-based approach could still help with those
poorly connected entities, we opt here for evaluation on benchmarking data (i.e.,
KORE) and leave further experimental analysis for future work.
Path-based method with top-K paths. Our path-based method achieves
competitive performance – when compared against [2], our methods achieves a
performance only slightly lower than their original proposal (ρ = 0.673), while
outperforming all its approximations (ρ = 0.621 and 0.425). However, our ap-
proach relies only on the single cheapest path connecting two entities. Conse-
quently, we analyze the impact of taking multiple paths between a pair of entities,
and aggregating evidence by averaging their costs to compute the final related-
ness score. We show the results in Figure 2. For all three weighting schemes the
performance of our method monotonically decreases with the number of top-k
paths used for computing relatedness. The best results are obtained for k = 1,
namely the cheapest path only, thus indicating that robust performance on this
task relies on finding specific, highly informative paths – and thus meaningful
semantic relations – between entities. Again, the best results are obtained using
the combIC weighting, which outperforms all other measures for any k.
4 Schuhmacher, Ponzetto
0 10 20 30 40 50
0.
35
0.
45
0.
55
0.
65
top−k
ρ
Unweighted
jointIC
combIC
IC+PMI
Fig. 2. Results using top-k average path costs.
Path-based method with different knowledge base. We next perform a
diachronic evaluation by evaluating the path-based method using the latest DB-
pedia Version (3.9), which contains more entities (+6.2%) and semantic relations
(+23.9%)1. Results for all three weighting approaches show minimal variations
(ρ =0.592, 0.620 and 0.580 for jointIC, combIC and IC+PMI, respectively), as
opposed to the unweighted baseline, which, in contrast decreases by more than 6
points (−11.3%). Manual inspection revealed that the increased amount of new
relations causes the unweighted approach more often to choose noisy, i.e., low-
informative paths. In contrast, thanks to our weighting, we are able to maintain
a stable performance, regardless of the continuous growth of the network.
4 Conclusions
We presented a knowledge-rich approach to entity ranking. Results indicate that
fine-grained semantic information from a wide-coverage knowledge base can be
effectively used for this task when combined with robust weighting and path
search techniques. Future work will explore multilinguality, and exploit related-
ness scores of unknown entities for knowledge base population.
References
1. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ive, Z.: Dbpedia: A
nucleus for a web of open data. In: ISWC+ASWC. pp. 722–735 (2007)
2. Hoffart, J., Seufert, S., Nguyen, D.B., Theobald, M., Weikum, G.: KORE: Keyphrase
overlap relatedness for entity disambiguation. In: CIKM. pp. 545–554 (2012)
3. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of
bipartite graph matching. Image Vision and Computing 27(7), 950–959 (2009)
4. Schuhmacher, M., Ponzetto, S.P.: Knowledge-based graph document modeling. In:
WSDM. pp. 543–552 (2014)
5. Zhang, Z., Gentile, A.L., Ciravegna, F.: Recent advances in methods of lexical se-
mantic relatedness – a survey. Natural Language Engineering 1(1), 1–69 (2012)
1 http://wiki.dbpedia.org/Datasets39/DatasetStatistics


Ranking Entities in a Large Semantic Network

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