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GuessWhat?! – Human Intelligence for Mining Linked Data
, 2010
"... Abstract. Ontologies are an important prerequisite for an increasing number of knowledge-intensive applications, not to mention the great vision of the Semantic Web. However, despite the obvious need of such formal and explicit representations of knowledge, many people refrain from investing into th ..."
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Abstract. Ontologies are an important prerequisite for an increasing number of knowledge-intensive applications, not to mention the great vision of the Semantic Web. However, despite the obvious need of such formal and explicit representations of knowledge, many people refrain from investing into the tedious and time-consuming task of ontology engineering. At the same time, purely automatic means for ontology construction so far have failed to meet our expectations in terms of quality and expressivity. In this paper we describe GuessWhat?!, a multi-player online game in the tradition of semantic games with a purpose. By leveraging people’s play instinct it motivates them to contribute to the creation of formal domain ontologies from Linked Open Data. We detail on the implementation of the game and present the results of an initial user study. 1
Dynamic Integration of Multiple Evidence Sources for Ontology Learning
"... Abstract. Although ontologies are central to the Semantic Web, current ontology learning methods primarily make use of a single evidence source and are agnostic in their internal representations to the evolution of ontology knowledge. This article presents a continuous ontology learning framework th ..."
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Abstract. Although ontologies are central to the Semantic Web, current ontology learning methods primarily make use of a single evidence source and are agnostic in their internal representations to the evolution of ontology knowledge. This article presents a continuous ontology learning framework that overcomes these shortcomings by integrating evidence from multiple, heterogeneous sources (unstructured, structured, social) in a consistent model, and by providing mechanisms for the fine-grained tracing of the evolution of domain ontologies. The presented framework supports a tight integration of human and machine computation. Crowdsourcing in the tradition of games with a purpose performs the evaluation of the learned ontologies and facilitates the automatic optimization of learning algorithms.
QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts
"... This paper presents our participation to the SemEval Task-17, related to “Taxonomy Ex-traction Evaluation ” (Bordea et al., 2015). We propose a new methodology for semi-supervised and auto-supervised acquisition of lexical taxonomies from raw texts. Our ap-proach is based on the theory of pretopol-o ..."
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Cited by 2 (1 self)
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This paper presents our participation to the SemEval Task-17, related to “Taxonomy Ex-traction Evaluation ” (Bordea et al., 2015). We propose a new methodology for semi-supervised and auto-supervised acquisition of lexical taxonomies from raw texts. Our ap-proach is based on the theory of pretopol-ogy that offers a powerful formalism to model subsumption relations and transforms a list of terms into a structured term space by combin-ing different discriminant criteria. In order to reach a good pretopological space, we define the Learning Pretopological Spaces method that learns a parameterized space by using an evolutionary strategy.
TELESUP Textual Self-Learning Support Systems. In: under review
, 2014
"... Abstract. The regular improvement and adaptation of an ontology is a key factor for the success of an ontology-based system. In this pa-per, we report on an ongoing project that aims for a methodology and tool for ontology development in a self-improving manner. The approach makes heavy use of metho ..."
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Abstract. The regular improvement and adaptation of an ontology is a key factor for the success of an ontology-based system. In this pa-per, we report on an ongoing project that aims for a methodology and tool for ontology development in a self-improving manner. The approach makes heavy use of methods known in natural language processing and information extraction. 1
ASPECT BASED SENTIMENT ANALYSIS
"... Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews or messages from social media) discussing a particular entity (e.g., a new model of a mobile phone). The systems attempt to detect the main (e.g., the most frequently discussed) aspects (features) o ..."
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Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews or messages from social media) discussing a particular entity (e.g., a new model of a mobile phone). The systems attempt to detect the main (e.g., the most frequently discussed) aspects (features) of the entity (e.g., ‘battery’, ‘screen’) and to estimate the average sentiment of the texts per aspect (e.g., how positive or negative the opinions are on average for each aspect). Although several ABSA systems have been proposed, mostly research prototypes, there is no established task decomposition for ABSA, nore are there any established evaluation measures for the subtasks ABSA systems are required to perform. This thesis, proposes a new task decomposition for ABSA, which contains three main subtasks: aspect term extraction, aspect term aggregation, and aspect term polarity estimation. The first subtask detects single- and multi-word terms naming aspects of the entity being discussed (e.g., ‘battery’, ‘hard disc’), called aspect terms. The second subtask aggregates (clusters) similar aspect terms (e.g., ‘price ’ and ‘cost’, but maybe also ‘design ’ and ‘color’), depending on user preferences and other restrictions (e.g., the size of the screen where the results of the ABSA system will be shown). The third subtask estimates the average sentiment per aspect term or cluster of aspect terms. For each one of the above mentioned subtasks, benchmark datasets for different kinds of entities (e.g., laptops, restaurants) were constructed during the work of this thesis. New evaluation measures are introduced for each subtask, arguing that they ii ABSTRACT iii are more appropriate than previous evaluation measures. For each subtask, the thesis also proposes new methods (or improvements over previous methods), showing experi-mentally on the constructed benchmark datasets that the new methods (or the improved versions) are better or at least comparable to state of the art ones.
orleans.fr
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
DOI: 10.1145/2063576.2063990 A Pretopological Framework for the Automatic Construction of Lexical-Semantic Structures from Texts
, 2013
"... {guillaume.cleuziou, davide.buscaldi, ..."
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"... Problems impacting the quality of automatically built ontologies ..."
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Refining Non-Taxonomic Relation Labels with External Structured Data to Support Ontology Learning
"... This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors ..."
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This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors from semantic relations identified in the corpus. It composes a knowledge base which consists of (i) verb centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from external sources. Applying semantic inference and validation to this knowledge base yields a refined relation label suggestion. A formal evaluation compares the accuracy and average ranking precision of this hybrid method with the performance of methods that solely rely on corpus data and those that are only based on reasoning and external data sources.
Learning Pretopological Spaces for Lexical Taxonomy Acquisition
"... Abstract. In this paper, we propose a new methodology for semi-supervised acquisition of lexical taxonomies. Our approach is based on the theory of pretopology that offers a powerful formalism to model se-mantic relations and transforms a list of terms into a structured term space by combining diffe ..."
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Abstract. In this paper, we propose a new methodology for semi-supervised acquisition of lexical taxonomies. Our approach is based on the theory of pretopology that offers a powerful formalism to model se-mantic relations and transforms a list of terms into a structured term space by combining different discriminant criteria. In order to learn a pa-rameterized pretopological space, we define the Learning Pretopological Spaces strategy based on genetic algorithms. In particular, rare but ac-curate pieces of knowledge are used to parameterize the different criteria defining the pretopological term space. Then, a structuring algorithm is used to transform the pretopological space into a lexical taxonomy. Results over three standard datasets evidence improved performances against state-of-the-art associative and pattern-based approaches. 1 Introduction and Related Work By coding the semantic relations between terms, lexical taxonomies (LTs) such as WordNet [9] have enriched the reasoning capabilities of applications in infor-
