InsMT+ Results for OAEI 2015 Instance Matching

Abstract

The InsMT+ is an improved version of InsMT system participated at OAEI 2014. The InsMT+ an automatic instance matching system which consists in identifying the instances that describe the same real-world objects. The InsMT+ applies different string-based matchers with a local filter. This is the second participation of our system and we have improved somehow the results obtained by the previous version.

Keywords

• Terminological Techniques
• String Based Similarity
• Instance Mapping
• Instance Matching
• Linked Data
• Web of Data
• Semantic Interoperability
• Semantic Web

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1.1

Presentation of the System

1.2 The process of our system consists in the following successive steps.

Step 1: Extraction and Normalization of Instances In this step, our system extracts

the instances. Then, we have applied ( 1 ) case conversion (conversion of all words in same upper or lower case) and ( 2 ) stop word elimination to normalize the instance informations.

Step 2: Terminological Matchers In this step, our system calculates the similarities between instances, normalized in previous phase, using various string-based matching algorithms. More precisely the different string-based matching algorithms used are: levenshtein-distance, Jaro, SLIM-Winkler. The calculations of similarities by each string matching algorithm are represented in matrix.

Step 3: Local Filter In this step, our system applies a local filter on each matrix i.e. we choose for each string-based matching algorithm a threshold to realize a filter. We consider that: the similarities which are less than the threshold are set to 0. Our intuition behind this local filter is that the similarities which are less than the threshold can influence the strategy of the average aggregation.

Step 4: Aggregation of Similarities In this step, our system combines the similarities of each matrix (after we have applied a local filter) using the average aggregation method and the result of the aggregation is represented in a matrix.

Step 5: Global Filter and Identification of Alignment In this step, our system applies

a second filter on the combined matrix (result of the previous step) in order to select the correspondences found using the maximum strategy with a threshold. 1.3

Adaptations Made for the Evaluation

We do not have made any specific adaptation for this first version of InsMT+, for OAEI 2015 evaluation campaign. All parameters are the same for instance matching track of OAEI 2015. 1.4

Link to the set of provided alignments (in align format)

The result of InsMT+ system can be downloaded from OAEI 2015 website http: //islab.di.unimi.it/im_oaei_2015/index.html 2

Results

In this section, we present the results obtained by running InsMT+ on instance matching track of OAEI 2015 evaluation campaign. 2.1

Author Disambiguation Task

The goal of the author-dis task is to link OWL instances referring to the same person (i.e., author) based on their publications.

We present below the results obtained by running InsMT+ system on author disambiguation task (see Tab. 1). * The results of InsMT+ are better compared to the first version participated in OAEI 2014, we can say that we have improved the results in terms of precision. However, the results are less better than other systems due to the simple techniques used in InsMT+. Since, InsMT+ is based only on String-based similarity. 2.2

Author Recognition Task

The goal of the author-rec task is to associate a person (i.e., author) with the corresponding publication report containing aggregated information about the publication activity of the person, such as number of publications, h-index, years of activity, number of citations.

We present below the results obtained by running InsMT+ system on author recognition task (see Tab. 2). * The results of InsMT+ on this track are not at all very satisfiable. However, we can remark that the number of retrieved mappings by our system is less 10 time than the mappings discovered by other systems, which explained the results obtained. We are trying to analyses the reason of these results in order to improve our system. 3

Conclusion

This is the second time that InsMT+ system has participated in SEAL platform and OAEI campaign. In this year, our system has participated only in two instance matching tracks of OAEI 2015 evaluation campaign. The InsMT+ system gives good results better than the InsMT system but these results still not statifaisable. As future Perspective, we attempt to improve more our system in order to get better results.

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