This paper presents the results of Lily in the ontology alignment contest OAEI 2015. As a comprehensive ontology matching system, Lily is intended to participate in four tracks of the contest: benchmark, conference, anatomy, and instance matching. The speci c techniques used by Lily will be introduced brie y. The strengths and weaknesses of Lily will also be discussed.
1.1
The core principle of matching strategies of Lily is utilizing the useful information correctly and e ectively. Lily combines several e ective and e cient matching techniques to facilitate alignments. There are ve main matching strategies: (1) Generic Ontology Matching (GOM) is used for common matching tasks with normal size ontologies. (2) Large scale Ontology Matching (LOM) is used for the matching tasks with large size ontologies. (3) Instance Ontology Matching (IOM) is used for instance matching tasks. (4) Ontology mapping debugging is used to verify and improve the alignment results. (5) Ontology matching tuning is used to enhance overall performance.
The matching process mainly contains three steps: (1) Pre-processing, when Lily parses ontologies and prepares the necessary information for subsequent steps. Meanwhile, the ontologies will be generally analyzed, whose characteristics, along with studied datasets, will be utilized to determine parameters and strategies. (2) Similarity computing, when Lily uses special methods to calculate the similarities between elements from di erent ontologies. (3) Post-processing, when alignments are extracted and re ned by mapping debugging.
In this year, some algorithms and matching strategies of Lily have been modi ed for higher e ciency, and adjusted for brand-new matching tasks like Author Recognition and Author Disambiguation in the Instance Matching track. 1.2
Lily aims to provide high quality 1:1 concept pair or property pair alignments. The main speci c techniques used by Lily are as follows.
Semantic subgraph An element may have heterogeneous semantic
interpretations in di erent ontologies. Therefore, understanding the real local meanings
of elements is very useful for similarity computation, which are the foundations
for many applications including ontology matching. Therefore, before similarity
computation, Lily rst describes the meaning for each entity accurately. However,
since di erent ontologies have di erent preferences to describe their elements,
obtaining the semantic context of an element is an open problem. The semantic
subgraph was proposed to capture the real meanings of ontology elements [
Based on the extracted semantic subgraphs, more credible matching clues can be discovered, which help reduce the negative e ects of the matching uncertainty. Generic ontology matching method The similarity computation is based on the semantic subgraphs, which means all the information used in the similarity computation comes from the semantic subgraphs. Lily combines the text matching and structure matching techniques.
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 di erent entities, the similarities of the corresponding parts will be calculated. Finally, all separated similarities will be combined with the experiential weights.
Matching weak informative ontologies Most existing ontology matching
methods are based on the linguistic information. However, some ontologies may
lack in regular linguistic information such as natural words and comments.
Consequently the linguistic-based methods will not work. Structure-based methods
are more practical for such situations. Similarity propagation is a feasible idea
to realize the structure-based matching. But traditional propagation strategies
do not take into consideration the ontology features and will be faced with
effectiveness and performance problems. Having analyzed the classical similarity
propagation algorithm, Similarity Flood, we proposed a new structure-based
ontology matching method [
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 Matching large ontologies is a challenge due
to its signi cant time complexity. We proposed a new matching method for large
ontologies based on reduction anchors [
Ontology mapping debugging Lily utilizes a technique named ontology
mapping debugging to improve the alignment results [
Ontology matching tuning Lily adopted ontology matching tuning this year.
By performing parameter optimization on training datasets [
Currently, ontology matching tuning is not totally automatic. It is di cult to nd out typical statistical parameters that distinguish each task from others. Meanwhile, learning from test datasets can be really time-consuming. Our experiment is just a beginning. 1.3
For benchmark, anatomy and conference tasks, Lily is totally automatic, which means Lily can be invoked directly from the SEALS client. It will also determine which strategy to use and the corresponding parameters. For a speci c instance matching task, Lily needs to be con gured and started up manually, so only matching results were submitted. 1.4
SEALS wrapped version of Lily for OAEI 2015 is available at https://drive. google.com/file/d/0B4fqkE38d3QrS1Zta0pPSFpqXzA/view?usp=sharing. 1.5
The set of provided alignments, as well as overall performance, is available at each track of the OAEI 2015 o cial website, http://oaei.ontologymatching. org/2015/. 2
2.1
There are two datasets in di erent sizes: Biblio and energy. The former one, which will be matched using Generic Ontology Matching, is generally small, while the latter one is so much that it has to be matched by Large scale Ontology Matching.
There are ve groups of test suites in each dataset. Each test suite has 94 matching tasks. The overall results of one test suite will be represented by the mean value of Precision, Recall and F-Measure. Test suites were generated from the same seed ontologies, which means they are all equal. Thus, the harmonic mean values of all test suites will be used to evaluate how well Lily worked.
The detailed results are shown in Table 1.
As Table 1 has shown, Lily handles Benchmark datasets well in both small and large scales, although the results of the energy dataset are slightly worse as the expense of better performance. According to the Benchmark results of OAEI20151, Lily has the highest overall F-Measure among 11 matching systems that generated alignments for the Biblio dataset. However, the public results show that Lily failed to produce alignments for energy dataset. That is because the energy dataset is a replacement for its former dataset IFC. The substitution also brought about format changes of ontology description les. Consequently, Lily and some other systems were not able to parse ontologies correctly. After the issue was xed, we evaluated Lily on only energy dataset with SEALS client and obtained the results. 2.2
The anatomy matching task consists of two real large-scale biological ontologies. Table 2 shows the performance of Lily in the Anatomy track on a server with one 3.46 GHz, 6-core CPU and 8GB RAM allocated. The time unit is second (s).
Compared with the result in OAEI 2011 [
However, as can be seen in the overall result, Lily lies in the middle position of the rank, which indicates it is still possible to make further progress. Additionally, some key algorithms have not been successfully parallelized. After that is done, the time consumption is expected to be further reduced. 2.3
In this track, there are 7 independent ontologies that can be matched with one another. The 21 subtasks are based on given reference alignments. As a result of heterogeneous characters, it is a challenge to generate high-quality alignments for all ontology pairs in this track.
Lily adopted ontology matching tuning for the Conference track this year. Table 3 shows its latest performance.
Compared with the result in OAEI 2011 [
For each task, there are two matching subtasks with di erent scales. The sandbox scale is around 1,000 instances, which was provided as the test dataset. The mainbox scale is around 10,000 instances. The results will be analyzed for each task.
Author Disambiguation Task Lily utilized a di erent strategy for this task, as we found several features of the dataset: one author's name in ontology A usually contains the corresponding name in ontology B, and a slight di erence of one property may distinguish publications in two ontologies. The result is shown in Table 4.
As can be seen in Table 4, the strategy is practical. Most correct matches can be found with high precision in both sandbox and mainbox subtasks. According to overall results, Lily scores highest in this task. However, there are still some missing matches. After analyzing the reference alignments and matching ontologies, we found that some matched authors had actually no publication in common, and that accounts for many matches missed by Lily.
Author Recognition Task Quite di erent from the previous task, this task requires computations over the source ontology, whose results will be matched with the target ontology. Lily will rst follow the requirement to generate an intermediate, statistical ontology from the source ontology. Then, string properties and numeric properties of that ontology and the target ontology will be compared in di erent methods. Finally, all the similarities will be combined. The result is shown in Table 5. In this year, a lot of modi cations were done to Lily for both e ectiveness and e ciency. The performance has been improved as we have expected. The strategies for new tasks have been proved to be useful.
On the whole, Lily is a comprehensive ontology matching system with the ability to handle multiple types of ontology matching tasks, of which the results are generally competitive. However, Lily still lacks in strategies for some newly developed matching tasks. The relatively high time and memory consumption also prevent Lily from nishing some challenging tasks. 4
In this paper, we brie y introduced our ontology matching system Lily. The matching process and the special techniques used by Lily were presented, and the alignment results were carefully analyzed.
There is still so much to do to make further progress. Lily needs more optimization to handle large ontologies with limited time and memory. Thus, techniques like parallelization will be applied more. Also, we have just tried out ontology matching tuning. With further research on that, Lily will not only produce better alignments for tracks it was intended for, but also be able to participate in the interactive track.