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Learning Vague Knowledge From Socially Generated Content in an Enterprise Framework
 

Learning Vague Knowledge From Socially Generated Content in an Enterprise Framework

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The advent and wide proliferation of Social Web in the re- ...

The advent and wide proliferation of Social Web in the re-
cent years has promoted the concept of social interaction as an important influencing factor of the way enterprises and organizations conduct business. Among the fields influenced is that of Enterprise Knowledge Management, where adoption of social computing approaches aims at increasing and maintaining at high levels the active participation of users in the organization's knowledge management activities. An important challenge towards this is the achievement of the right balance between informalities of socially generated data and the required formality of enterprise knowledge. In this context, we focus on the problem of mining vague knowledge from social content generated within an enterprise framework and we propose a learning framework based on microblogging and fuzzy ontologies.

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    Learning Vague Knowledge From Socially Generated Content in an Enterprise Framework Learning Vague Knowledge From Socially Generated Content in an Enterprise Framework Presentation Transcript

    • Learning Vague Knowledge From Socially Generated Content in an Enterprise Framework Panos Alexopoulos, John Pavlopoulos, Phivos Mylonas 1st Mining Humanistic Data Workshop, Halkidiki, Greece, September 27th, 2012
    • 2  Introduction  Background and Problem Definition  Approach Overview and Rationale  Vague Knowledge Acquisition  Conceptualization and Initialization  Microblogging Framework  Extraction of Vague Knowledge Assertions  Assertion Strength Assessment  Generation of Membership Functions and Fuzzy Degrees  Conclusions and Future Work Agenda
    • 3 Background Introduction ●Knowledge Management is a discipline that aims to enable enterprises and organizations to fully leverage their knowledge in their effort to grow more efficient and competitive. ●This leverage involves several key objectives such as: ● Identification, gathering and organization of existing knowledge ● Sharing and reusing of this knowledge for different applications and users and facilitation of new knowledge creation ●However, a dimension of this knowledge that has so far been inadequately considered is vagueness.
    • 4 Vagueness Introduction ● Vagueness is manifested through predicates that admit borderline cases, i.e. cases where it is unclear whether or not the predicate applies ● E.g. Tall, High, Experienced etc. Definition ● Degree Vagueness: Lack of crisp boundaries between application and non application in some dimension. ● E.g. Tall, Rich, Recent ● Combinatory Vagueness: Inability to clearly define adequate applicability criteria. ● Π.χ. Modern, Expert, Religion Types of Vagueness ● Uncertainty: E.g. Today it might rain ● Inexactness: E.g. Someone has height between 170 and 180 cm. Frequently Confused Concepts ● A person can be tall with respect to the average population height and not tall with respect to professional basketball players ● This doesn’t mean that a vague predicate can stop being vague in a different context but merely that the interpretation of its vagueness can change. Context Dependence
    • 5 Fuzzy Ontologies Introduction ●Fuzzy Ontologies are extensions of classical ontologies that allow the assignment of truth degrees to vague ontological elements. ●For example: ● “The project's budget is satisfactory to a degree of 0.7" ● “Jane is an expert at Artificial Intelligence to a degree of 0.5".
    • 6 Fuzzy Ontological Elements Introduction ● A fuzzy ontology concept may have instances that belong to it at certain degrees. ● E.g. John is a TallPerson to a degree of 0.5. Fuzzy Concepts ● A fuzzy ontology relation links concept instances at certain degrees. ● Π.χ. John is expert at Machine Learning to a degree of 0.9. ● Similarly, a fuzzy attribute assigns literal values to concept instances at certain degrees. Fuzzy Relations and Attributes ● A fuzzy datatype consists of a set of vague terms which may be used within the ontology as attribute values. ● Π.χ. Low, Average, High for the attribute Project Budget. ● Each term is mapped to a fuzzy set that defines the term’s meaning. Fuzzy Datatypes
    • 7 Problem Definition Introduction ● An important bottleneck in developing fuzzy ontologies is the definition of the degrees and membership functions of the fuzzy elements. ● High level of subjectivity. ● Context dependence. ● Thus the problem is defined as follows: Given a fuzzy enterprise ontology, what are the optimal fuzzy degrees and membership functions that should be assigned to its elements (concepts, relations and datatypes) in order to represent the domain’s vagueness as accurately as possible? ● E.g. given the fuzzy concept CompanyCompetitor and a set of individual components, what is the degree to which each of these companies is considered a competitor? ● E.g. given the fuzzy relation isExpertAt and a set of related through it pairs of instances (e.g. persons related to business areas), what is the degree to which the relation between these pairs actually stands? ● E.g. given the fuzzy datatype ProjectBudget and the terms it consists of (e.g. low, average, high), what are the membership functions of the fuzzy sets that best reflect the meaning of each of these terms?
    • 8 Process Overview Vague Knowledge Acquisition 1. Identification within the enterprise of vague knowledge and conceptual modeling of it in the form of a fuzzy enterprise ontology. 2. Setting up of a microblogging platform in which the members of the enterprise are expected to participate and perform discussions and information exchange on all aspects regarding the enterprise and its environment. 3. Detection and extraction from the user generated platform’s content of vague knowledge assertions, namely statements related to the elements already defined in the fuzzy enterprise ontology. 4. Calculation for each vague assertion of a strength value based on the utilization of various characteristics of the discussions they are involved in. 5. Aggregation of these assertions and automated generation of fuzzy degrees and membership functions.
    • 9 Conceptualization and Initialization with IKARUS-Onto Vague Knowledge Acquisition Acquire Crisp Ontology Define Fuzzy Ontology Elements Formalize Fuzzy Elements Validate Fuzzy Ontology ● Establish a basis for the development of the fuzzy ontology. ● Develop or acquire the crisp ontology. ● Justify and estimate the necessary work for the fuzzy ontology development. ● Ensure existence of vagueness in the domain. ● Ensure vagueness is a requirement. ● Conceptualization of vagueness in an explicit way and shareable way. ● Definition of fuzzy ontology elements ● Specification of fuzzy degrees and membership functions by experts. ● Make fuzzy ontology machine- processable. ● Select fuzzy ontology language and use it to represent the defined elements. ● Ensure adequate and correct capturing of the domain’s vagueness ● Check correctness, accuracy, completeness and consistency. Step Goals Actions Establish Need for Fuzziness
    • 10 Microblogging Platform Vague Knowledge Acquisition ● The microblogging platform we adopt for the purposes of this work is miKrow, an intra-enterprise semantic microblogging tool that allows its end-users to share short messages expressing what are they working at. ● The platform works mostly like Twitter, with two important enhancements: ● When users reply to a message they are able to denote the nature of their reply by using the predefined hashtags #support and #attack. ● Users are also able to denote their agreement or disagreement to a message through a rating functionality ● These two features allows us to use the platform as an argumentation tool and capture the disagreements and debates over vague knowledge statements that may occur.
    • 11 Microblogging Platform Vague Knowledge Acquisition
    • 12 Extraction of Vague Knowledge Assertions Vague Knowledge Acquisition ● Vague knowledge assertions are practically statements related to the elements of fuzzy ontology. ● E.g. The assertion “The budget for the project X is low" is related to the fuzzy datatype “ProjectBudget” ● E.g. The assertion “John is expert at ontologies" is related to the fuzzy relation “isExpertAt”. ● Our goal is to detect and extract such assertions from the user messages so that we can use them for determining the fuzzy degrees of their respective elements. ● To achieve this, we use an in-house developed semantic annotation tool that, given a fuzzy ontology, is able to recognize such assertions within a piece of text. ● An important factor that contributes to higher levels of precision for this detection is the fact that microblogging messages are short. ● In any case, the detection process may be performed in a semi-automatic fashion where the correctness of the extracted assertions could be checked by the system’s administrator.
    • 13 Knowledge Assertion Strength Calculation Vague Knowledge Acquisition ● To calculate the strength of the extracted vague assertions we aggregate the strength of messages that are directly or indirectly related to these assertions. ● Strength of a message depends on: ● On the number of agreements and disagreements it has received by the users. ● On the number and strength of attacking and supporting messages. ● On the overall influence of the user who has published the message. This is generally relevant to the number of users that follow the message publisher but also to the person’s expertise on the messages topic.
    • 14 Generation of Membership Functions and Fuzzy Degrees Vague Knowledge Acquisition ● Given an instance and a fuzzy concept we consider all the relevant assertions along with their strengths: ● A1: “Accenture is a Competitor to a strength of 0.5.” ● A2: “Accenture is a Competitor to a strength of 0.7.” ● A3: “Accenture is a Competitor to a strength of 0.4.” ● To aggregate these strengths into a single degree we: ● Compute the mean value of all the strength values ● We estimate confidence intervals and we only allow those mean values with significance level of no less than 0.05. ● In most cases we expect to have most assertions gathered very close to a single mean value which can then be considered as the degree of the relevant ontological statement. ● In case many assertions seem to be out of the confidence intervals, then that’s an indication that the statement’s interpretation might be context-dependent. Fuzzy Concept and Relation Assertions
    • 15 Generation of Membership Functions and Fuzzy Degrees Vague Knowledge Acquisition ● Given a term and a fuzzy datatype we consider all the relevant assertions along with their strengths: ● A1: “A budget of 20,000 is low to a strength of 0.5.” ● A2: “A budget of 24,000 is low to a strength of 0.3.” ● A3: “A budget of 26,000 is average to a strength of 0.6.” ● A4: “A budget of 30,000 is high to a strength of 0.4.” ● Based on the pairs of these values and strengths, we determine the optimal function fuzzy membership function that links them. ● This is a well-studied problem in the area of fuzzy expert systems and several related methods that construct such functions from training data are available. Fuzzy Datatypes
    • 16 Key Points Conclusions and Future Work ● We proposed a framework for automatic vague knowledge acquisition in enterprise settings based on: ● A semantically enhanced microblogging system. ● A fuzzy ontology learning process that acts upon the social content produced by the enterprise’s people. ● The key characteristic of our approach is the utilization of the content’s social features in order to assign strengths to vague assertions. ● The relative agreement and support that microposts enjoy. ● The status and influence of the users.
    • 17 Future Work Conclusions and Future Work ● In the future we intend to apply our framework in an actual enterprise setting and evaluate its effectiveness in acquiring vague knowledge. ● This evaluation will focus on two dimensions: ● The ability of the microblogging approach in producing rich social context over the vague knowledge. ● The accuracy of the fuzzy ontology degrees and membership functions learned using this context.
    • 18 Contact iSOCO Where we are Questions? Barcelona Tel +34 935 677 200 Edificio Testa A C/ Alcalde Barnils, 64-68 St. Cugat del Vallès 08174 Barcelona Valencia Tel +34 963 467 143 Oficina 107 C/ Prof. Beltrán Báguena, 4 46009 Valencia Pamplona Tel +34 948 102 408 Parque Tomás Caballero, 2, 6º-4ª 31006 Pamplona Dr. Panos Alexopoulos Senior Researcher palexopoulos@isoco.com Madrid Tel +34 913 349 797 Av. del Partenón, 16-18, 1º7ª Campo de las Naciones 28042 Madrid