Analyzing Big Data can help corporations to im-prove their efficiency. In
this work we present a new vision to derive Value from Big Data using a
Semantic Hierarchical Multi-label Classification called Semantic HMC based in a
non-supervised Ontology learning process. We also proposea Semantic HMC
process, using scalable Machine-Learning techniques and Rule-based reasoning

Bertaux, Aurlie

Cruz, Christophe

Hassan, Thomas

Peixoto, Rafael

Silva, Nuno

English

arXiv

International audienceAnalyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning

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Semantic HMC for Big Data AnalysisThomas Hassan, Rafael Peixoto, Christophe Cruz, Aurlie Bertaux, Nuno SilvaTo cite this version:Thomas Hassan, Rafael Peixoto, Christophe Cruz, Aurlie Bertaux, Nuno Silva. Semantic HMCfor Big Data Analysis. 2014 IEEE International Conference on Big Data, Oct 2014, Washington,United States. <http://cci.drexel.edu/bigdata/bigdata2014/new.htm>. <hal-01089741>HAL Id: hal-01089741https://hal.archives-ouvertes.fr/hal-01089741Submitted on 2 Dec 2014HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestine´e au de´poˆt et a` la diffusion de documentsscientifiques de niveau recherche, publie´s ou non,e´manant des e´tablissements d’enseignement et derecherche franc¸ais ou e´trangers, des laboratoirespublics ou prive´s.Semantic HMC for Big Data AnalysisThomas HassanUniversit de BourgogneDijon, Francethomas.hassan@checksem.frRafael PeixotoPolytechnic of PortoPorto, Portugalrafpp@isep.ipp.ptChristophe CruzUniversit de BourgogneDijon, Francechristophe.cruz@u-bourgogne.frAurlie BertauxUniversit de BourgogneDijon, Franceaurelie.bertaux@iut-dijon.u-bourgogne.frNuno SilvaPolytechnic of PortoPorto, Portugalnps@isep.ipp.ptAbstract—Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new visionto derive Value from Big Data using a Semantic HierarchicalMulti-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea SemanticHMC process, using scalable Machine-Learning techniques andRule-based reasoning.Index Terms—classification; multi-classify; Big-Data; ontology;semantic technologies; machine learningI. INTRODUCTIONNowadays, discovering knowledge and insights over webdata is a major task for most corporations to increase theircompetitiveness. Determining the value of information relativeto a particular customer is a complex task addressed by thebusiness intelligence/data-mining field [1]. In the context ofBig Data, this task is even more challenging, due to itscharacteristics. An increasing number of Vs has been usedto characterize Big Data [2], [3]: Volume, Velocity, Varietyand Value. Volume concerns the large amount of data thatis generated and stored through the years by social media,sensor data, etc[2]. Velocity concerns both to the productionand the process to meet a demand because Big Data is notonly a huge volume of data but it must be processed quickly.Variety relates to the various types of data composing the BigData. These types include semi-structured and unstructureddata representing 90% of his content [4] such as audio, video,webpage, and text, as well as traditional structured data, etc.Value measures how valuable the information to a Big Dataconsumer is. Value is the most important feature of Big Dataand his raison dłtre because if data dont have value then isuseless. An IDC report [5] proposes the value extraction fromvery large volumes of a wide variety of data, by enablingthe high-velocity capture, discovery, and/or analysis. Sheth [6]proposes deriving Value via harnessing the challenges posedby Volume, Variety, and Velocity using semantic techniquesand technologies. This requires organized ways to harnessand overcome the four V-challenges by using metadata andemploy semantics and intelligent processing. Our aim is toextract Value from Big Data by harnessing a huge Volume andVariety of data that change constantly (Velocity) by using anovel unsupervised ontology learning process based on HMCcalled Semantic HMC. Hierarchical Multi-Label Classification(HMC) is the combination of Multi-Label classification andHierarchical classification [7]. In HMC, the items can beassigned to different hierarchical paths and simultaneouslymay belong to different class labels in the same hierarchicallevel [7]. The ontology [8] plays a key role in defining termsand meanings used to represent the knowledge, reducing thegap between the users and the HMC process. This paper doesnot aim to improve the state of the art in multi-classification,nor the automatic hierarchy construction. Instead it proposes ascalable process to semantically learn the ontology by adoptingscalable machine learning processes and Rule-based reasoning[9] to classify the data items and therefore extract value fromBig Data. The contributions of this work are twofold:• Scalable ontology learning process based on HMC (Se-mantic HMC).• Big Data Analysis using a Semantic HMC.The rest of the paper covers three sections. The second sectiondescribes how to use the Semantic HMC to extract valuefrom Big Data. The third section describes the Semantic HMCprocess proposal. Finally, the last section draws conclusionsand suggests further research.II. USING SEMANTIC HMC TO DERIVE VALUE INBIG DATA CONTEXTOur approach is to exploit value from very large volumesof data that are in constant generation using a SemanticHMC approach. The Semantic HMC process learns the Tbox(Taxonomy and Rules) part of the ontology from the hugeVolume and Variety of initial data. Once this learning phaseis finished, the classification system incrementally learns theTbox from the new incoming items (Abox) to provide andrespond to the Velocity (and the others V) dimension(s).The result of this Semantic HMC process is a rich ontologywith the items (instances) classified according to the learnedconcept hierarchy (i.e. the taxonomy of the ontology). Corpo-rations recurrently use concept hierarchies as taxonomies torepresent their valuable information [10]. Our vision is to usethe concept hierarchies from corporations to validate the valueBig DataSemantic HMCOntology Corporation TaxonomiesSimilarityFig. 1. Value extraction for corporationsof the learned ontology for a specific corporation (Fig. 1).Higher similarity between the concept hierarchy of the learnedontology and the concept hierarchy used by a corporationsuggest better alignment between the HMC results and thecorporations knowledge and goals. Consequently, data itemsclassified as valuable concepts ultimately present better valueto the corporation than those not matching the corporationsconcepts.III. SEMANTIC HMC PROCESSOur Semantic HMC process is generic for a large Varietyof unstructured data items (e.g. text, images) and scalable fora large Volume of data. The process is unsupervised such thatno previously labeled/classified examples or rules to relatethe data items with the labels exist. The label (i.e. concepts)hierarchy and the rules are automatically learned from the datathrough scalable Machine Learning techniques. To infer themost specific concepts for each data item and all subsumingconcepts we use rule-based reasoning that exhaustively appliesa set of rules to a set of triples to infer conclusions [9], i.e.the items classifications. This Rule-based reasoning approachallows the parallelization and distribution of work by largeclusters of inexpensive machines through Big Data technolo-gies as Map-reduce [11]. Web Scale Reasoners [12] currentlyuses Rule-Based reasoning to reach high scalability by loadparallelization and distribution, thus addressing the Velocityand Volume dimensions of Big Data. This proposed processconsists of 5 individually scalable steps (Fig. 2) matching therequirements of Big Data processing:• Indexation parses and creates an index of data items.• Vectorization calculates term-frequency vectors of theindexed items.• Hierarchization creates a concept hierarchy based on termfrequency.• Resolution creates the reasoning rules to relate data itemswith the hierarchy concepts based on term frequency.• Realization first populates the ontology with items andthen for each item determines the most specific hierarchyconcept and all his subsuming concepts.1.Indexation Ontology2.VectorizationItensHierarchy3.HierarchizationRulesClassiﬁed Items4.Resolution5.RealizationUnclassiﬁed ItemsIndexed CollectionHierarchy ConceptsTerm-FrequencyvectorsFig. 2. Semantic HMC ProcessA. IndexationThe indexation step parses and index data items. As oneof our main focus points is the scalability of the architecture,the indexation is a mandatory step. Each item type has itsspecific parser to efficiently retrieve useful information for theother steps reducing the Limited Context Analysis problem.The Limited Content Analysis problem [13][14] is definedby the difficulty in extracting reliable automated informationfrom various content (e.g. text, images, sound, etc.), which cangreatly reduce the quality of the classifications. By reducingthe Limited Content Analysis we improve the Semantic HMCcapability to handle more Variety of data.B. VectorizationThe vectorization step vectorizes the terms in the indexeditems by calculating two types of term frequency vectors :• Term frequency in each item using the frequency of aterm in an item measured by TF-IDF. TF-IDF uses thefrequency of a term in an item (TF) and the inversenumber of items in which the term appears (IDF) [15].• Term frequency in all items using the appearing frequencyof a term in all documents [15].The following steps use the term vectors calculated in this stepto learn the concepts Hierarchy and the Rules.C. HierarchizationThe hierarchization step will select relevant terms as rel-evant concepts and also will generate the broader-narrowerrelations between these concepts. To select the concepts in thehierarchy, a quality measure must be used. There are severalmethods for creating hierarchical relations between conceptsincluding [16], [17]:• Hierarchical clustering that starts with one cluster andprogressively merges clusters that are closest.• Subsumption methods that construct the concept broader-narrower relations based on the co-occurrence of con-cepts.Any of these methods can be used to create the hierarchicalrelations. The advantages and drawbacks of each method isdeeply studied in [16].D. ResolutionThe resolution process will create the ontology rules usedto relate the hierarchy concepts and the items using theterm-frequency vectors. The rules creation process will usethresholds as proposed in [18] to select the most relevantterms for each hierarchy concept that will be used in the rules.The main difference is that instead of translating the rulesinto logical constraints of an ontology captured in DescriptionLogic, these rules will be translated into rules in the SemanticWeb Rule Language (SWRL). The main interest in usingSWRL rules is to reduce the reasoning effort, thus improvingthe scalability and performance of the system. We aim to usea huge amount of simple SWRL rules that will be applied tothe ontology in order to classify items.E. RealizationThe realization phase will populate the learned concepthierarchy with data items. First the ontology is populatedwith new items to label in an assertion level (Abox). Todo the classification/labeling of the items, the SWRL Rulesgenerated in the Resolution step are used. Then a Rule-Basedinference engine will use the SWRL rules and the hierarchyto infer the most specific concepts for each data item and allsubsuming concepts. This leads to a multi-label classificationof the documents based in a hierarchy of labels (HierarchicalMulti-label Classification).IV. CONCLUSIONIn this paper we present our vision to extract value from BigData using a Semantic HMC process and propose a scalablefive-step architecture to automatically classify unstructureditems. We use machine learning to learn an ontology withSWRL rules to automatically classify items of Big Data. TheSemantic HMC process prototype is under development andwe expect to show the implementation and results in furtherwork. Our current work consists in evaluating the resultingontology, considering three different aspects: the process scal-ability (performance), the quality of the hierarchy, and thequality of the classification process (i.e. concept tagging ofitems).ACKNOWLEDGMENTThis project is founded by the company Actualis SARL, theFrench agency ANRT and through the Portuguese COMPETEProgram under the project AAL4ALL (QREN13852).REFERENCES[1] I. H. Witten and E. Frank, Data Mining: Practical machine learningtools and techniques. Morgan Kaufmann, 2005.[2] M. Chen, S. Mao, and Y. Liu, “Big Data: A Survey,” Mobile Networksand Applications, pp. 171–209, 2014.[3] P. Hitzler and K. Janowicz, “Linked data, big data, and the 4thparadigm,” Semantic Web, vol. 4, pp. 233–235, 2013.[4] A. Syed, K. Gillela, and C. Venugopal, “The Future Revolution on BigData,” Future, vol. 2, pp. 2446–2451, 2013.[5] J. Gantz and D. Reinsel, “Extracting value from chaos,” IDC iview, pp.1–12, 2011.[6] A. Sheth, “Transforming Big Data into Smart Data,” 2014 IEEE 30thInternational Conference on Data Engineering, pp. 2–2, 2014.[7] W. Bi and J. Kwok, “Multi-label classification on tree-and DAG-structured hierarchies,” Yeast, pp. 1–8, 2011.[8] R. Studer, V. R. Benjamins, and D. Fensel, “Knowledge engineering:Principles and methods,” Data & Knowledge Engineering, pp. 161–197,1998.[9] J. Urbani, F. van Harmelen, S. Schlobach, and H. Bal, “QueryPIE:Backward Reasoning for OWL Horst over Very Large KnowledgeBases,” in ISWC’11. Springer-Verlag, 2011, pp. 730–745.[10] P. Lambe, Organising knowledge: taxonomies, knowledge and organi-sational effectiveness. Elsevier, 2014.[11] J. Dean and S. Ghemawat, “MapReduce : Simplified Data Processingon Large Clusters,” Communications of the ACM, pp. 1–13, 2008.[12] J. Urbani, “Three Laws Learned from Web-scale Reasoning,” in 2013AAAI Fall Symposium Series, 2013.[13] P. Lops, M. de Gemmis, and G. Semeraro, “Content-based recommendersystems: State of the art and trends,” in Recommender Systems Hand-book. Springer, 2011, pp. 73–105.[14] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutie´rrez, “Recommendersystems survey,” Knowledge-Based Systems, pp. 109–132, 2013.[15] G. Salton and C. Buckley, “Term-weighting approaches in automatictext retrieval,” Information processing & management, pp. 513—-523,1988.[16] J. de Knijff, F. Frasincar, and F. Hogenboom, “Domain taxonomy learn-ing from text: The subsumption method versus hierarchical clustering,”Data & Knowledge Engineering, pp. 54–69, 2013.[17] K. Meijer, F. Frasincar, and F. Hogenboom, “A Semantic Approach forExtracting Domain Taxonomies from Text,” Decision Support Systems,2014.[18] D. Werner, N. Silva, and C. Cruz, “Using DL-Reasoner for HierarchicalMultilabel Classification applied to Economical e-News,” in Science andInformation Conference, 2014, p. 8.

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