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From the 4th to the 8th of April I had the pleasure to participate the 31st ACM Symposium on Applied Computing (ACM SAC), which was held in beautiful city Pisa, Italy. I was there to present my full paper "Measuring Semantic Distance for Linked Open Data-enabled Recommender Systems" and to participate the Student Research Competition sponsored by Microsoft.

This year, there were over 500+ registrations from 59 countries at this conference. There were 37 tracks and the overall acceptance rate for this year is 24%.

Keynotes:

There were two keynotes given by John Mylopoulos and Marco Conti, respectively. The first keynote is about the requirements problem in Software Engineering and the second keynote is about "From MANET to people-centric computing and communications.

Semantic Web Track:

There were two sessions with eight papers for Semantic Web Track where three of the participants two of the participants from our institute. Pasquale Minervini presented "Leveraging the Schema in Latent Factor Models for Knowledge Graph Completion" and another college Feng Gao presented "QoS-Aware Adaptation for Complex Event Service" in another (SOA) track.

Measuring Semantic Distance for Linked Open Data-enabled Recommender Systems from GUANGYUAN PIAO

Social Network and Media Analysis Track (SONAMA):

One of the papers in this track I'm interested in was "Inferring Semantic Interest Profiles from Twitter Followees: Does Twitter Know Better than Your Friends?" from Christoph Besel, University of Passau, Germany, which is related to my work. Although many previous works focused on using tweets for inferring user interest profiles, they used the alternative source (followees) to retrieve user interest profiles, which are based on the tendency that more and more users are consuming feeds instead of producing content on the social networks.

Student Research Competition (SRC):

I also participated in SAC SRC and went through 2nd round (top-5 list) and it was a good opportunity to compete across different disciplines. Congrats to all top-3 winners!

Lunch

Banquet

What would make the conference better?

It would be better to have a Twitter channel to communicate and disseminate activities during the conference. Next year, it will be in Morocco and hope I could attend again:).

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Update after the conference:

The proceedings are available from June, 2016

RESIM (REsource SIMilarity) for Linked Data

DESCRIPTION

The resim.jar file is an implementation of RESIM (REsource SIMilarity) measure. The measure is designed to calculate the semantic similarity between two resources in a Knowlege Graph (e.g., DBpedia, zhishi.me) with a SPARQL Endpoint. RESIM is presented in [1] and then it is improved in [2] and the summary of the measure is presented in [3]. The implementation extends the measure further by enabling different properties for two resources, e.g., for incoming indirect link between dbpedia:Steve_Jobs and dbpedia:Apple_Inc., it can be connected by dbpedia:Steve_Jobs<-dbpedia-owl:keyPerson<-dbpedia:NexT->dbpedia-owl:successor->dbpedia:Apple_Inc. while in the paper we restrict the property should be the same.

When writing a paper or producing a software application, tool, or interface based on the library, please kindly cite the source [2].

REQUIREMENT

Java 1.7
JENA 2.11.2

EXAMPLE 1

public static void main(String[] args) {

    // similarity measure settings
    List<String> additionalPropertyList = Arrays.asList(
            "<http://purl.org/dc/terms/subject>", 
            "<http://www.w3.org/2000/01/rdf-schema#subClassOf>", 
            "<http://www.w3.org/2004/02/skos/core#narrowerOf>", 
            "<http://www.w3.org/2004/02/skos/core#broaderOf>",
            "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"
            );
    List<String> includePropertyList = Arrays.asList(
            "<http://purl.org/dc/terms/subject>", 
            "<http://www.w3.org/2000/01/rdf-schema#subClassOf>", 
            "<http://www.w3.org/2004/02/skos/core#narrower>", 
            "<http://www.w3.org/2004/02/skos/core#broader>",
            "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"
            );
    List<String> excludePropertyList = Arrays.asList(
            "<http://dbpedia.org/ontology/wikiPageWikiLink>" 
            );

    // initialize similarity measure
    // param1: SPARQL Endpoint URL
    // param2: pattern for property - default: "http://dbpedia.org/ontology/
    // param3: include property list
    // param4: additional property list to pattern
    // param5: exclude property list
    ResourceSimilarityMeasure rsm = new ResourceSimilarityMeasure("http://dbpedia.org/sparql", null, null, additionalPropertyList, excludePropertyList);    

    System.out.println(rsm.getSimilarity("<http://dbpedia.org/resource/Drink>", "<http://dbpedia.org/resource/Mouth>", 2));

}

EXAMPLE 2

public static void main(String[] args) {

    List<String> excludePropertyList = Arrays.asList("<http://dbpedia.org/ontology/wikiPageWikiLink>");
    ResourceSimilarityMeasure rsm = new ResourceSimilarityMeasure("http://dbpedia.org/sparql", null, null, null, excludePropertyList);  

    System.out.println(rsm.getSimilarity("<http://dbpedia.org/resource/Apple_Inc.>", "<http://dbpedia.org/resource/Steve_Jobs>", 2));
    System.out.println(rsm.getSimilarity("<http://dbpedia.org/resource/Apple_Inc.>", "<http://dbpedia.org/resource/Steve_Wozniak>", 2));
    System.out.println(rsm.getSimilarity("<http://dbpedia.org/resource/Apple_Inc.>", "<http://dbpedia.org/resource/Jonathan_Ive>", 2));
    System.out.println(rsm.getSimilarity("<http://dbpedia.org/resource/Apple_Inc.>", "<http://dbpedia.org/resource/Microsoft>", 2));
    System.out.println(rsm.getSimilarity("<http://dbpedia.org/resource/Apple_Inc.>", "<http://dbpedia.org/resource/IPad>", 2));

    // Printed results
    0.7107697604926099
    0.26084710667467736
    0.13425729687979637
    0.6239711506085717
    0.7341358492281069
    Started: 18.1.2016 21:54:42 Finished: 18.1.2016 21:55:12

}

The ResourceSimilarityMeasure requires 5 parameters. The first one is a SPARQL Endpoint (e.g., DBpedia SPARQL Endpoint) and the other ones are used for controling the property list for this measure.
The 2nd parameter (pattern) is used with 4th, 5th parameters. For example, the default pattern of property for this measure is "http://dbpedia.org/ontology/" (i.e., DBpedia Ontology properties) and it will consider/exclude the list of properties if there is an additional property list or an exclude property list exist.
The 3rd parameter is an include property list that controls property list for this measure in a strict way. That is, the measure will only consider this property list if you define the list.

REFENRECES

Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes, Guangyuan Piao, Safina showkat Ara, John G. Breslin 5th Joint International Semantic Technology Conference, Yichang, China, 2015
Measuring Semantic Similarity for Linked Open Data-enabled Recommender Systems, Guangyuan Piao and John G. Breslin, The 31st ACM/SIGAPP Symposium on Applied Computing, Pisa, Italy, 2016
Exploiting the Semantic Similarity of Interests in a Semantic Interest Graph for Social Recommendations, Guangyuan Piao, The 31st ACM/SIGAPP Symposium on Applied Computing, Pisa, Italy, 2016

SPARQL query involving wikiPageWikiLink not working

OntologyProperty:WikiPageWikiLink

Just found the property is included in the dataset that downloaded from http://www.rdfhdt.org/datasets/, which is not found via SPARQL endpoint of DBpedia before.

This property denotes a "Link from a Wikipage to another Wikipage" and DBpedia extracts the Wikipedia page links and offers them for download, but does not add them to the public SPARQL endpoint. There are simply too many of them - they would overwhelm the SPARQL server.

This property might be useful to extract latent relationships between two resources:)

The dot product as projection

ACM SAC 2016 Travel Report