Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2018

Rexha Andi, Dragoni Mauro , Federici Marco

An Unsupervised Aspect Extraction Strategy For Monitoring Real-Time Reviews Stream

Elsevier, 2018

Journal
One of the most important opinion mining research directions falls in the extraction ofpolarities referring to specific entities (aspects) contained in the analyzed texts. Thedetection of such aspects may be very critical especially when documents come fromunknown domains. Indeed, while in some contexts it is possible to train domainspecificmodels for improving the effectiveness of aspects extraction algorithms, inothers the most suitable solution is to apply unsupervised techniques by making suchalgorithms domain-independent and more efficient in a real-time environment. Moreover,an emerging need is to exploit the results of aspect-based analysis for triggeringactions based on these data. This led to the necessity of providing solutions supportingboth an effective analysis of user-generated content and an efficient and intuitive wayof visualizing collected data. In this work, we implemented an opinion monitoringservice implementing (i) a set of unsupervised strategies for aspect-based opinion miningtogether with (ii) a monitoring tool supporting users in visualizing analyzed data.The aspect extraction strategies are based on the use of an open information extractionstrategy. The effectiveness of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domainad aptedtechniques.
2017

Rexha Andi, Kern Roman, Ziak Hermann, Dragoni Mauro

A semantic federated search engine for domain-specific document retrieval

SAC '17 Proceedings of the Symposium on Applied Computing, Sung Y. Shin, Dongwan Shin, Maria Lencastre, ACM, Marrakech, Morocco, 2017

Konferenz
Retrieval of domain-specific documents became attractive for theSemantic Web community due to the possibility of integrating classicInformation Retrieval (IR) techniques with semantic knowledge.Unfortunately, the gap between the construction of a full semanticsearch engine and the possibility of exploiting a repository ofontologies covering all possible domains is far from being filled.Recent solutions focused on the aggregation of different domain-specificrepositories managed by third-parties. In this paper, wepresent a semantic federated search engine developed in the contextof the EEXCESS EU project. Through the developed platform,users are able to perform federated queries over repositories in atransparent way, i.e. without knowing how their original queries aretransformed before being actually submitted. The platform implementsa facility for plugging new repositories and for creating, withthe support of general purpose knowledge bases, knowledge graphsdescribing the content of each connected repository. Such knowledgegraphs are then exploited for enriching queries performed byusers.
2016

Dragoni Mauro, Rexha Andi, Kröll Mark, Kern Roman

Polarity Classification for Target Phrases in Tweets: A Word2Vec approach

The Semantic Web, ESWC 2016 Satellite Events, ESWC 2016, Springer-Verlag, Crete, Greece, 2016

Konferenz
Twitter is one of the most popular micro-blogging serviceson the web. The service allows sharing, interaction and collaboration viashort, informal and often unstructured messages called tweets. Polarityclassification of tweets refers to the task of assigning a positive or a nega-tive sentiment to an entire tweet. Quite similar is predicting the polarityof a specific target phrase, for instance@Microsoftor#Linux,whichiscontained in the tweet.In this paper we present a Word2Vec approach to automatically pre-dict the polarity of a target phrase in a tweet. In our classification setting,we thus do not have any polarity information but use only semantic infor-mation provided by a Word2Vec model trained on Twitter messages. Toevaluate our feature representation approach, we apply well-establishedclassification algorithms such as the Support Vector Machine and NaiveBayes. For the evaluation we used theSemeval 2016 Task #4dataset.Our approach achieves F1-measures of up to∼90 % for the positive classand∼54 % for the negative class without using polarity informationabout single words.
2016

Rexha Andi, Dragoni Mauro, Kern Roman, Kröll Mark

An Information Retrieval Based Approach for Multilingual Ontology Matching

International Conference on Applications of Natural Language to Information Systems, Métais E., Meziane F., Saraee M., Sugumaran V., Vadera S. , Springer , Salford, UK, 2016

Konferenz
Ontology matching in a multilingual environment consists of finding alignments between ontologies modeled by using more than one language. Such a research topic combines traditional ontology matching algorithms with the use of multilingual resources, services, and capabilities for easing multilingual matching. In this paper, we present a multilingual ontology matching approach based on Information Retrieval (IR) techniques: ontologies are indexed through an inverted index algorithm and candidate matches are found by querying such indexes. We also exploit the hierarchical structure of the ontologies by adopting the PageRank algorithm for our system. The approaches have been evaluated using a set of domain-specific ontologies belonging to the agricultural and medical domain. We compare our results with existing systems following an evaluation strategy closely resembling a recommendation scenario. The version of our system using PageRank showed an increase in performance in our evaluations.
2016

Rexha Andi, Kern Roman, Dragoni Mauro , Kröll Mark

Exploiting Propositions for Opinion Mining

ESWC-16 Challenge on Semantic Sentiment Analysis, Springer Link, Springer-Verlag, Crete, Greece, 2016

Konferenz
With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity (i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to drive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information.
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