=Paper=
{{Paper
|id=Vol-551/paper-15
|storemode=property
|title=Lily: ontology alignment results for OAEI 2009
|pdfUrl=https://ceur-ws.org/Vol-551/oaei09_paper8.pdf
|volume=Vol-551
|dblpUrl=https://dblp.org/rec/conf/semweb/WangX08a
}}
==Lily: ontology alignment results for OAEI 2009==
https://ceur-ws.org/Vol-551/oaei09_paper8.pdf
Lily: Ontology Alignment Results for OAEI 2009
Peng Wang1, Baowen Xu2,3
1
College of Software Engineering, Southeast University, China
2
State Key Laboratory for Novel Software Technology, Nanjing University, China
3
Department of Computer Science and Technology, Nanjing University, China
pwang@seu.edu.cn, bwxu@nju.edu.cn
Abstract. This paper presents the alignment results of Lily for the ontology
alignment contest OAEI 2009. Lily is an ontology mapping system, and it has
four functions: generic ontology matching, large scale ontology matching,
semantic ontology matching and mapping debugging. In OAEI 2009, Lily
submited the results for four alignment tasks: benchmark, anatomy, directory
and conference.
1 Presentation of the system
Lily is an ontology mapping system for solving the key issues related to
heterogeneous ontologies, and it uses hybrid matching strategies to execute the
ontology matching task. Lily can be used to discovery the mapping for both normal
ontologies and large scale ontologies. In the past year, we did not improve Lily
significantly but revised some bugs according to the reports from some users.
1.1 State, purpose, general statement
In order to obtain good alignments, the core principle of the matching strategy in Lily
is utilizing the useful information effectively and rightly. Lily combines several novel
and efficient matching techniques to find alignments. Currently, Lily realized four
main functions: (1) Generic Ontology Matching method (GOM) is used for common
matching tasks with small size ontologies. (2) Large scale Ontology Matching method
(LOM) is used for the matching tasks with large size ontologies. (3) Semantic
Ontology Matching method (SOM) is used for discovering the semantic relations
between ontologies. Lily uses the web knowledge to recognize the semantic relations
through the search engine. (4) Ontology mapping debugging is used to improve the
alignment results.
The matching process mainly contains three steps: (1) In preprocess, Lily parses
ontologies and prepares the necessary data for the subsequent steps. (2) In computing
step, Lily uses suitable methods to calculate the similarity between elements from
different ontologies. (3)In post-process, the alignments are extracted and then refined
by mapping debugging. The architecture of Lily is shown in Fig. 1.
The lasted version of Lily is V2.0. Lily V2.0 provides a friendly graphical user
interface. Fig.2 shows a snapshot when Lily is running.
Fig. 1. The Architecture of Lily
Fig. 2. The user interface of Lily
1.2 Specific techniques used
Lily aims to provide high quality 1:1 alignments between concept/property pairs. The
main specific techniques used by Lily are as follows.
Semantic subgraph An entity in a given ontology has its specific meaning. In our
ontology mapping view, capturing such meaning is very important to obtain good
alignment results. Therefore, before similarity computation, Lily first describes the
meaning for each entity accurately. The solution is inspired by the method proposed
by Faloutsos et al. for discovering connection subgraphs [1]. It is based on electricity
analogues to extract a small subgraph that best captures the connections between two
nodes of the graph. Ramakrishnan et al. also exploits such idea to find the informative
connection subgraphs in RDF graph [2].
The problem of extracting semantic subgraphs has a few differences from
Faloutsos’s connection subgraphs. We modified and improved the methods provided
by the above two work, and proposed a method for building an n-size semantic
subgraph for a concept or a property in ontology. The subgraphs can give the precise
descriptions of the meanings of the entities, and we call such subgraphs semantic
subgraphs. The detail of the semantic subgraph extraction process is reported in our
other work [3].
The significance of semantic subgraphs is that we can build more credible
matching clues based on them. Therefore it can reduce the negative affection of the
matching uncertain.
Generic ontology matching method The similarity computation is based on the
semantic subgraphs, i.e. all the information used in the similarity computation is come
from the semantic subgraphs. Lily combines the text matching and structure matching
techniques [3].
Semantic Description Document (SDD) matcher measures the literal similarity
between ontologies. A semantic description document of a concept contains the
information about class hierarchies, related properties and instances. A semantic
description document of a property contains the information about hierarchies,
domains, ranges, restrictions and related instances. For the descriptions from different
entities, we calculate the similarities of the corresponding parts. Finally, all separate
similarities are combined with the experiential weights. For the regular ontologies, the
SDD matcher can find satisfactory alignments in most cases.
To solve the matching problem without rich literal information, a similarity
propagation matcher with strong propagation condition (SSP matcher) is presented,
and the matching algorithm utilizes the results of literal matching to produce more
alignments. Compared with other similarity propagation methods such as similarity
flood [4] and SimRank [5], the advantages of our similarity propagation include
defining stronger propagation condition, semantic subgraphs-based and with efficient
and feasible propagation strategies. Using similarity propagation, Lily can find more
alignments that cannot be found in the text matching process.
However, the similarity propagation is not always perfect. When more alignments
are discovered, more incorrect alignments would also be introduced by the similarity
propagation. So Lily also uses a strategy to determine when to use the similarity
propagation.
Large scale ontology matching Large scale ontology matching tasks propose the
rough time complexity and space complexity for ontology mapping systems. To solve
this problem, we proposed a novel method [3], which uses the negative anchors and
positive anchors to predict the pairs can be passed in the later matching computing.
The method is different from other several large scale ontology matching methods,
which are all based on ontology segment or modularization.
Semantic ontology matching Our semantic matching method [6] is base on the
idea that Web is a large knowledge base, and from which we can gain the semantic
relations between ontologies through Web search engine. Based on lexico-syntactic
patterns, this method first obtains a candidate mapping set using search engine. Then
the candidate set is refined and corrected with some rules. Finally, ontology mappings
are chosen from the candidate mapping set automatically.
Ontology mapping debugging Lily uses a technique called ontology mapping
debugging to improve the alignment results [7]. During debugging, some types of
mapping errors, such as redundant and inconsistent mappings, can be detected. Some
warnings, including imprecise mappings or abnormal mappings, are also locked by
analyzing the features of mapping result. More importantly, some errors and warnings
can be repaired automatically or can be presented to users with revising suggestions.
1.3 Adaptations made for the evaluation
In OAEI 2009, Lily used GOM matcher to compute the alignments for three tracks
(benchmark, directory, conference). In order to assure the matching process is fully
automated, all parameters are configured automatically with a strategy. For the large
ontology alignment tracks (anatomy), Lily used LOM matcher to discover the
alignments. Lily can determine which matcher should be chose according to the size
of ontology.
1.4 Link to the system and the set of provided alignments
Lily V2.0 and the alignment results for OAEI 2009 are available at
http://ontomappinglab.googlepages.com/lily.htm.
2 Results
2.1 benchmark
The benchmark test set can be divided into five groups: 101-104, 201-210, 221-247,
248-266 and 301-304.
The following table shows the average performance of each group and the overall
performance on the benchmark test set.
Table 1. The performance on the benchmark
101-104 201-210 221-247 248-266 301-304 Average H-mean
Precision 1.00 0.99 0.99 0.94 0.83 0.95 0.97
Recall 1.00 0.95 1.00 0.76 0.79 0.84 0.88
2.2 anatomy
The anatomy track consists of two real large-scale biological ontologies. Lily can
handle such ontologies smoothly with LOM method. Lily submitted the results for
three sub-tasks in anatomy. Task#1 means that the matching system has to be applied
with standard settings to obtain a result that is as good as possible. Task#2 means that
the system generates the results with high precision. Task#3 means that the system
generates the alignment with high recall.
2.3 directory
The directory track requires matching two taxonomies describing the web directories.
Except the class hierarchy, there is no other information in the ontologies. Therefore,
besides the literal information, Lily also utilizes the hierarchy information to decide
the alignments.
2.4 conference
This task contains 15 real-case ontologies about conference. For a given ontology, we
compute the alignments with itself, as well as with other ontologies. For we treat the
equivalent alignment is symmetric, we get 105 alignment files totally. The
heterogeneous character in this track is various. It is a challenge to generate good
results for all ontology pairs in this test set.
3 General comments
Strengths For normal size ontologies, if they have regular literals or similar
structures, Lily can achieve satisfactory alignments.
Weaknesses Lily needs to extract semantic subgraphs for all concepts and
properties. It is a time-consuming process. Even though we have improved the
efficiency of the extracting algorithm, it still is the bottleneck for the performance of
the system.
4 Conclusion
We briefly introduce our ontology matching tool Lily. The matching process and the
special techniques used by Lily are presented. The preliminary alignment results are
carefully analyzed. Finally, we summarized the strengths and the weaknesses of Lily.
References
1. Faloutsos, C., McCurley, K. S., Tomkins, A.: Fast Discovery of Connection Subgraphs. In
the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining, Seattle, Washington (2004).
2. Ramakrishnan, C., Milnor, W. H., Perry, M., Sheth, A. P.: Discovering Informative
Connection Subgraphs in Multirelational Graphs. ACM SIGKDD Explorations, Vol. 7(2),
(2005)56-63.
3. Wang, P.: Research on the Key Issues in Ontology Mapping (In Chinese). PhD Thesis,
Southeast University, Nanjing, 2009.
4. Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity Flooding: A Versatile Graph Matching
Algorithm and its Application to Schema Matching. In the 18th International Conference on
Data Engineering (ICDE), San Jose CA (2002).
5. Jeh, G., Widom, J.: SimRank: A Measure of Structural-Context Similarity. In the 8th
International Conference on Knowledge Discovery and Data Mining (SIGKDD), Edmonton,
Canada, (2002).
6. Li, K., Xu, B., and Wang, P. An Ontology Mapping Approach Using Web Search Engine.
Journal of Southeast University, 2007, 23(3):352-356.
7. Wang, P., Xu, B. Debugging Ontology Mapping: A Static Method. Computing and
Informatics, 2008, 27(1): 21–36.
Appendix: Raw results
The final results of benchmark task are as follows.
Matrix of results
# Comment Prec. Rec. # Comment Prec. Rec.
101 Reference alignment 1.00 1.00 251 0.96 0.76
103 Language generalization 1.00 1.00 251-2 0.99 0.96
104 Language restriction 1.00 1.00 251-4 0.99 0.90
201 No names 1.00 1.00 251-6 0.96 0.84
201-2 1.00 1.00 251-8 0.99 0.83
201-4 1.00 1.00 252 0.95 0.77
201-6 1.00 1.00 252-2 0.98 0.94
201-8 1.00 1.00 252-4 0.98 0.94
202 No names, no comment 1.00 0.84 252-6 0.98 0.94
202-2 1.00 0.95 252-8 0.97 0.93
202-4 1.00 0.92 253 0.85 0.62
202-6 0.98 0.88 253-2 1.00 0.93
202-8 0.98 0.84 253-4 1.00 0.91
203 Misspelling 1.00 0.98 253-6 0.94 0.82
204 Naming conventions 1.00 1.00 253-8 0.98 0.82
205 Synonyms 1.00 0.99 254 1.00 0.27
206 Translation 1.00 0.99 254-2 1.00 0.82
207 1.00 0.99 254-4 1.00 0.70
208 1.00 0.98 254-6 1.00 0.61
209 0.97 0.87 254-8 1.00 0.42
210 1.00 0.88 257 1.00 0.12
221 No hierarchy 1.00 1.00 257-2 1.00 0.97
222 Flattened hierarchy 1.00 1.00 257-4 1.00 0.94
223 Expanded hierarchy 0.98 0.97 257-6 0.87 0.82
224 No instances 1.00 1.00 257-8 0.85 0.67
225 No restrictions 1.00 1.00 258 0.76 0.56
228 No properties 1.00 1.00 258-2 0.99 0.96
230 Flattening entities 0.94 1.00 258-4 0.96 0.88
231 Multiplying entities 1.00 1.00 258-6 0.95 0.83
No hierarchy no
232 instance 1.00 1.00 258-8 0.94 0.80
No hierarchy no
233 property 1.00 1.00 259 0.91 0.73
No instance no
236 property 1.00 1.00 259-2 0.97 0.94
237 1.00 1.00 259-4 0.97 0.94
238 0.98 0.98 259-6 0.96 0.93
239 0.97 1.00 259-8 0.97 0.94
240 0.97 1.00 260 0.94 0.55
241 1.00 1.00 260-2 0.93 0.93
246 0.97 1.00 260-4 0.90 0.93
247 0.94 0.97 260-6 0.93 0.86
248 1.00 0.81 260-8 0.95 0.69
248-2 1.00 0.95 261 0.61 0.33
248-4 1.00 0.92 261-2 0.88 0.91
248-6 1.00 0.88 261-4 0.88 0.91
248-8 1.00 0.87 261-6 0.88 0.91
249 0.76 0.73 261-8 0.88 0.91
249-2 1.00 0.97 262 NaN 0.00
249-4 0.98 0.91 262-2 1.00 0.76
249-6 0.98 0.87 262-4 1.00 0.61
249-8 0.95 0.82 262-6 1.00 0.42
250 1.00 0.55 262-8 1.00 0.21
250-2 1.00 1.00 265 0.80 0.14
250-4 1.00 1.00 266 0.50 0.09
250-6 1.00 1.00 301 BibTeX/MIT 0.87 0.81
250-8 0.90 0.79 302 BibTeX/UMBC 0.84 0.65
303 Karlsruhe 0.63 0.75
304 INRIA 0.96 0.96