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Ultra-fine entity typing (UFET) is the task of inferring the semantic types, from a large set of fine-grained candidates, that apply to a given entity mention. This task is especially challenging because we only have a small number of training examples for many of the types, even with distant supervision strategies. State-of-the-art models, therefore, have to rely on prior knowledge about the type labels in some way. In this paper, we show that the performance of existing methods can be improved using a simple technique: we use pre-trained label embeddings to cluster the labels into semantic domains and then treat these domains as additional types. We show that this strategy consistently leads to improved results, as long as high-quality label embeddings are used. We furthermore use the label clusters as part of a simple post-processing technique, which results in further performance gains. Both strategies treat the UFET model as a black box and can thus straightforwardly be used to improve a wide range of existing models.

Ontologies and vector space embeddings are among the most popular frameworks for encoding conceptual knowledge. Ontologies excel at capturing the logical dependencies between concepts in a precise and clearly defined way. Vector space embeddings excel at modelling similarity and analogy. Given these complementary strengths, there is a clear need for frameworks that can combine the best of both worlds. In this paper, we present an overview of our recent work in this area. We first discuss the theory of conceptual spaces, which was proposed in the 1990s by Gärdenfors as an intermediate representation layer in between embeddings and symbolic knowledge bases. We particularly focus on a number of recent strategies for learning conceptual space representations from data. Next, building on the idea of conceptual spaces, we discuss approaches where relational knowledge is modelled in terms of geometric constraints. Such approaches aim at a tight integration of symbolic and geometric representations, which unfortunately comes with a number of limitations. For this reason, we finally also discuss methods in which similarity, and other forms of conceptual relatedness, are derived from vector space embeddings and subsequently used to support flexible forms of reasoning with ontologies, thus enabling a looser integration between embeddings and symbolic knowledge., OASIcs, Vol. 99, International Research School in Artificial Intelligence in Bergen (AIB 2022), pages 3:1-3:30

This paper introduces a novel method for merging opendomain terminological knowledge. It takes advantage of the Region Connection Calculus (RCC5), a formalism used to represent regions in a topological space and to reason about their set-theoretic relationships. To this end, we first propose a faithful translation of terminological knowledge provided by several and potentially conflicting sources into region spaces. The merging is then performed on these spaces, and the result is translated back into the underlying language of the input sources. Our approach allows us to benefit from the expressivity and the flexibility of RCC5 while dealing with conflicting knowledge in a principled way.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate., 53 pages

International audience

International audience; One of the main aims of Ontology-Based Data Access is to uniform querying data using knowledge encoded in an ontol-ogy. Data are often provided by several information sources, and this has led to a number of methods that merge them in order to get a unified point of view. Existing merging approaches relie on the fact that the content of the datasets is known. However in many applications such as in recommendation sites, this prior knowledge about the content of the datasets is not available. This paper investigates the problem of querying multiple information sources without knowing the content of the datasets. We first provide several strategies to answer queries from datasets. We then study how these strategies can be compared to each other from a productivity point of view.

International audience

International audience; Ontology is an essential resource to enhance the performance of information processing system as well as isan intelligent storage area served for management of large-scale heterogeneous digital contents resulting. In this paper, we propose the initial steps for reconstructing a significant schema of Vietnamese traditional dances. Most of the typical dances of Vietnamese community are recorded in multimedia format, in raw videos. Accordingly, we concentrated on analyzing and collecting knowledge of the dance experts at art schools in Vietnam to classify and to determine the primary features that would be stored in the ontology. We propose an ontology-based modelling for the cultural heritage domain of Vietnames etraditional dance.

International audience

International audience

This thesis investigates the dynamics of beliefs and uncertainty management in DL-Lite, one of the most important lightweight description logics. The first part of the thesis concerns the problem of han- dling uncertainty in DL-Lite. First, we propose an extension of the main fragments of DL-Lite to deal with the uncertainty associated with axioms using a possibility theory framework without additional ex- tra computational costs. We then study the revision of possibilistic DL-Lite bases when a new piece of information is available. Lastly, we propose a min-based assertional merging operator when assertions of ABox are provided by several sources of information having different levels of priority. The second part of the thesis concerns the problem of inconsistency handling in flat and prioritized DL-Lite knowledge bases. We first propose how to reason from a flat DL-Lite knowledge base, with a multiple ABox, which can be either issued from multiple information sources or resulted from revising DL-Lite knowledge bases. This is done by introducing the notions of modifiers and inference strategies. The combination of modifiers plus inference strategies can be mapped out in order to provide a principled and exhaustive list of techniques for inconsistency management. We then give an approach based on selecting multiple repairs using a cardinality-based criterion, and we identified suitable strategies for handling inconsistency in the prioritized case. Lastly, we perform a comparative analysis, followed by experimental studies, of the proposed inconsistency handling techniques. A tool for representing and reasoning in possibilistic DL-Lite framework is implemented., Cette thèse étudie la dynamique des croyances et la gestion de l’incertitude dans DL-Lite, une des plus importantes familles des logiques de description légères. La première partie de la thèse porte sur la gestion de l’incertitude dans DL-Lite. En premier lieu, nous avons proposé une extension des prin- cipaux fragments de DL-Lite pour faire face à l’incertitude associée aux axiomes en utilisant le cadre de la théorie des possibilités. Cette extension est réalisée sans engendrer des coûts calculatoires sup- plémentaires. Nous avons étudié ensuite la révision des bases DL-Lite possibilistes en présence d’une nouvelle information. Enfin, nous avons proposé un opérateur de fusion lorsque les assertions de ABox sont fournies par plusieurs sources d’information ayant différents niveaux de priorité. La deuxième partie de la thèse traite le problème de la gestion d’incohérence dans les bases de connaissances DL-Lite. Nous avons étudié, tout d’abord, comment raisonner à partir d’une base DL-Lite standard avec des ABox mul- tiples en introduisant les notions de modificateurs et de stratégies d’inférence. La combinaison des mod- ificateurs et de stratégies d’inférence fournit une liste exhaustive des principales techniques de gestion de l’incohérence. Nous avons proposé ensuite une approche, basée sur un critère de cardinalité, de sélection des réparations, et nous avons identifié les stratégies appropriées pour la gestion de l’incohérence pour les bases DL-Lite stratifiées. Enfin, nous avons effectué une analyse comparative, suivie par des études expérimentales, des différentes techniques de gestion d’incohérence proposées. Finalement, un outil de représentation et de raisonnement à partir des bases DL-Lite possibiliste est réalisé.

International audience; In many applications, such as video processing, assertions are often provided by several and potentially conflicting sources having different reliability levels. This paper deals with the problem of handling inconsistency in lightweight ontologies when the set of assertions (facts) is prioritized. We propose a safe and efficient way to restore consistency using the concept of free assertions; assertions that are not involved in conflicts. Our approach allows the selection of one consistent assertional base, called a preferred repair. Selecting a unique repair is important since it allows an efficient handling of queries. The last part of the paper contains an illustrative example using video data.

International audience; Repair-based techniques are a standard way of dealing with inconsistency in the context of ontology-based data access where several inconsistency-tolerant semantics have been mainly proposed for lightweight description logics. In this paper we present a generic transformation from knowledge bases expressed within existential rules formalism into an ASP program. We propose different strategies for this transformation, and highlight the ones for which answer sets of the generated program correspond to various kinds of repairs used in inconsistency-tolerant inferences.

International audience

International audience; Repair based techniques are a standard way of deal- ing with inconsistency in the context of ontology based data access. We propose a novel non-objection inference relation (along with its variants) where a query is considered as valid if it follows from at least one repair and it is consistent with all the repairs. These inferences are strictly more productive than universal inference while preserving the consistency of its set of conclusions. We study the productivity and properties of the new inferences. We also give experimental results of the proposed non-objection inference.

International audience; We propose a general framework for inconsistency-tolerant query answering within existential rule setting. This framework unifies the main semantics proposed by the state of art and introduces new ones based on cardinality and majority principles. It relies on two key notions: modifiers and inference strategies. An inconsistency-tolerant semantics is seen as a composite modifier plus an inference strategy. We compare the obtained semantics from a productivity point of view.

This thesis investigates the dynamics of beliefs and uncertainty management in DL-Lite, one of the most important lightweight description logics. The first part of the thesis concerns the problem of han- dling uncertainty in DL-Lite. First, we propose an extension of the main fragments of DL-Lite to deal with the uncertainty associated with axioms using a possibility theory framework without additional ex- tra computational costs. We then study the revision of possibilistic DL-Lite bases when a new piece of information is available. Lastly, we propose a min-based assertional merging operator when assertions of ABox are provided by several sources of information having different levels of priority. The second part of the thesis concerns the problem of inconsistency handling in flat and prioritized DL-Lite knowledge bases. We first propose how to reason from a flat DL-Lite knowledge base, with a multiple ABox, which can be either issued from multiple information sources or resulted from revising DL-Lite knowledge bases. This is done by introducing the notions of modifiers and inference strategies. The combination of modifiers plus inference strategies can be mapped out in order to provide a principled and exhaustive list of techniques for inconsistency management. We then give an approach based on selecting multiple repairs using a cardinality-based criterion, and we identified suitable strategies for handling inconsistency in the prioritized case. Lastly, we perform a comparative analysis, followed by experimental studies, of the proposed inconsistency handling techniques. A tool for representing and reasoning in possibilistic DL-Lite framework is implemented.; Cette thèse étudie la dynamique des croyances et la gestion de l’incertitude dans DL-Lite, une des plus importantes familles des logiques de description légères. La première partie de la thèse porte sur la gestion de l’incertitude dans DL-Lite. En premier lieu, nous avons proposé une extension des prin- cipaux fragments de DL-Lite pour faire face à l’incertitude associée aux axiomes en utilisant le cadre de la théorie des possibilités. Cette extension est réalisée sans engendrer des coûts calculatoires sup- plémentaires. Nous avons étudié ensuite la révision des bases DL-Lite possibilistes en présence d’une nouvelle information. Enfin, nous avons proposé un opérateur de fusion lorsque les assertions de ABox sont fournies par plusieurs sources d’information ayant différents niveaux de priorité. La deuxième partie de la thèse traite le problème de la gestion d’incohérence dans les bases de connaissances DL-Lite. Nous avons étudié, tout d’abord, comment raisonner à partir d’une base DL-Lite standard avec des ABox mul- tiples en introduisant les notions de modificateurs et de stratégies d’inférence. La combinaison des mod- ificateurs et de stratégies d’inférence fournit une liste exhaustive des principales techniques de gestion de l’incohérence. Nous avons proposé ensuite une approche, basée sur un critère de cardinalité, de sélection des réparations, et nous avons identifié les stratégies appropriées pour la gestion de l’incohérence pour les bases DL-Lite stratifiées. Enfin, nous avons effectué une analyse comparative, suivie par des études expérimentales, des différentes techniques de gestion d’incohérence proposées. Finalement, un outil de représentation et de raisonnement à partir des bases DL-Lite possibiliste est réalisé.

International audience

International audience

International audience

International audience