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Academic literature on the topic 'Reasoning over temporal knowledge graphs'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Reasoning over temporal knowledge graphs"

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Mavromatis, Costas, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Adesoji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, and George Karypis. "TempoQR: Temporal Question Reasoning over Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5825–33. http://dx.doi.org/10.1609/aaai.v36i5.20526.

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Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding-based framework for answering complex questions over TKGs. Our method termed temporal question reasoning (TempoQR) exploits TKG embeddings to ground the question to the specific entities and time scope it refers to. It does so by augmenting the question embeddings with context, entity and time-aware information by employing three specialized modules. The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings. Finally, a transformer-based encoder learns to fuse the generated temporal information with the question representation, which is used for answer predictions. Extensive experiments show that TempoQR improves accuracy by 25--45 percentage points on complex temporal questions over state-of-the-art approaches and it generalizes better to unseen question types.
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Sadeghian, Ali, Mohammadreza Armandpour, Anthony Colas, and Daisy Zhe Wang. "ChronoR: Rotation Based Temporal Knowledge Graph Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 6471–79. http://dx.doi.org/10.1609/aaai.v35i7.16802.

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Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a k-dimensional rotation transformation parametrized by relation and time, such that after each fact's head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction.
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Chen, Xiaojun, Shengbin Jia, Ling Ding, and Yang Xiang. "Reasoning over temporal knowledge graph with temporal consistency constraints." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11941–50. http://dx.doi.org/10.3233/jifs-210064.

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Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson, spouse Of, Ryan Reynolds) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP, which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10.
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Bai, Luyi, Wenting Yu, Mingzhuo Chen, and Xiangnan Ma. "Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning." Applied Soft Computing 103 (May 2021): 107144. http://dx.doi.org/10.1016/j.asoc.2021.107144.

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Mirtaheri, Mehrnoosh. "Relational Learning to Capture the Dynamics and Sparsity of Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15724–25. http://dx.doi.org/10.1609/aaai.v35i18.17859.

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The rapid growth of large scale event datasets with timestamps has given rise to the dynamically evolving multi-relational knowledge graphs. Temporal reasoning over such data brings on many challenges and is still not well understood. Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This observation has given rise to the recent interest in low-shot learning methods that are able to generalize from only a few examples. The existing approaches, however, are tailored to static knowledge graphs and not easily generalized to temporal settings, where data scarcity poses even bigger problems, due to the occurrence of new, previously unseen relations. The goal of my doctoral research is to introduce new approaches for learning meaningful representation that capture the dynamics of temporal knowledge graphs while tackling various existing challenges such as data scarcity.
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Rajeshwari, T., and C. Thangamani. "Attack Impact Discovery and Recovery with Dynamic Bayesian Networks." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.

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The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the data communication over the objects. The attack ramification models are designed with intrusion root information. The attack ramifications are applied to identify the malicious objects and contaminated objects. The attack ramifications are discovered with the information flows from the attack sources. The Attack Ramification with Bayesian Network (ARBN) scheme discovers the attack impact without the knowledge of the intrusion root. The probabilistic reasoning approach is employed to analyze the object state for ramification process. The objects lifetime is divided into temporal slices to verify the object state changes. The system call traces and object slices are correlated to construct the Temporal Dependency Network (TDN). The Bayesian Network (BN) is constructed with the uncertain data communication activities extracted from the TDN. The attack impact is fetched with loopy belief propagation on the BN model. The network security system is built with attack impact analysis and recovery operations. Live traffic data analysis process is carried out with improved temporal slicing concepts. Attack Ramification and Recovery with Dynamic Bayesian Network (ARRDBN) is built to support attack impact analysis and recovery tasks. The unsupervised attack handling mechanism automatically discovers the feasible solution for the associated attacks.
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Wang, Xiang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. "Explainable Reasoning over Knowledge Graphs for Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5329–36. http://dx.doi.org/10.1609/aaai.v33i01.33015329.

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Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.
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Bai, Luyi, Wenting Yu, Die Chai, Wenjun Zhao, and Mingzhuo Chen. "Temporal knowledge graphs reasoning with iterative guidance by temporal logical rules." Information Sciences 621 (April 2023): 22–35. http://dx.doi.org/10.1016/j.ins.2022.11.096.

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Zhu, Wencheng, Yucheng Han, Jiwen Lu, and Jie Zhou. "Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization." IEEE Transactions on Image Processing 31 (2022): 3017–31. http://dx.doi.org/10.1109/tip.2022.3163855.

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Mantle, Matthew, Sotirios Batsakis, and Grigoris Antoniou. "Large scale distributed spatio-temporal reasoning using real-world knowledge graphs." Knowledge-Based Systems 163 (January 2019): 214–26. http://dx.doi.org/10.1016/j.knosys.2018.08.035.

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Books on the topic "Reasoning over temporal knowledge graphs"

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Corbett, Dan. Reasoning and unification over conceptual graphs. New York: Kluwer Academic/Plenum Publishers, 2003.

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Corbett, Dan. Reasoning and Unification over Conceptual Graphs. Springer, 2003.

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Book chapters on the topic "Reasoning over temporal knowledge graphs"

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Corbett, Dan. "Unification, Knowledge Structures and Constraints." In Reasoning and Unification over Conceptual Graphs, 29–47. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0087-2_2.

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Stepanova, Daria, Mohamed H. Gad-Elrab, and Vinh Thinh Ho. "Rule Induction and Reasoning over Knowledge Graphs." In Lecture Notes in Computer Science, 142–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00338-8_6.

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Long, Shaonan, Jinzhi Liao, Shiyu Yang, Xiang Zhao, and Xuemin Lin. "Complex Question Answering Over Temporal Knowledge Graphs." In Web Information Systems Engineering – WISE 2022, 65–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20891-1_6.

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Zhang, Yiming, Yiyue Qian, Yanfang Ye, and Chuxu Zhang. "Adapting Distilled Knowledge for Few-shot Relation Reasoning over Knowledge Graphs." In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), 666–74. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2022. http://dx.doi.org/10.1137/1.9781611977172.75.

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Xiao, Yao, Guangyou Zhou, and Jin Liu. "Modeling Temporal-Sensitive Information for Complex Question Answering over Knowledge Graphs." In Natural Language Processing and Chinese Computing, 418–30. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17120-8_33.

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Koner, Rajat, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, and Stephan Günnemann. "Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering." In The Semantic Web – ISWC 2021, 111–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88361-4_7.

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AbstractVisual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to associate it with various objects that are present in the image, it is an ambitious task and requires multi-modal reasoning from both computer vision and natural language processing. We propose Graphhopper, a novel method that approaches the task by integrating knowledge graph reasoning, computer vision, and natural language processing techniques. Concretely, our method is based on performing context-driven, sequential reasoning based on the scene entities and their semantic and spatial relationships. As a first step, we derive a scene graph that describes the objects in the image, as well as their attributes and their mutual relationships. Subsequently, a reinforcement learning agent is trained to autonomously navigate in a multi-hop manner over the extracted scene graph to generate reasoning paths, which are the basis for deriving answers. We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs. Our results show that we keep up with human performance on manually curated scene graphs. Moreover, we find that Graphhopper outperforms another state-of-the-art scene graph reasoning model on both manually curated and automatically generated scene graphs by a significant margin.
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Davis, Ernest. "Qualitative Reasoning and Spatio-Temporal Continuity." In Advances in Geospatial Technologies, 97–146. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61692-868-1.ch003.

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This chapter discusses the use of transition graphs for reasoning about continuous spatial change over time. The chapter first presents a general definition of a transition graph for a partition of a topological space. Then it defines the path-connected and the homogeneous refinements of such a partition. The qualitative behavior of paths through the space corresponds to the structure of paths through the associated transition graphs, and of associated interval label sequences, and the authors prove a number of metalogical theorems that characterize these correspondences in terms of the expressivity of associated first-order languages. They then turn to specific real-world problems and show how this theory can be applied to domains such as rigid objects, strings, and liquids.
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Schiffel, Jeffrey A. "Organizational Semiotics Complements Knowledge Management." In Intelligent, Adaptive and Reasoning Technologies, 104–22. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-595-7.ch006.

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Inserting the human element into an Information System leads to interpreting the Information System as an information field. Organizational semiotics provides a means to analyze this alternate interpretation. The semantic normal forms of organizational semiotics extract structures from natural language texts that may be stored electronically. In themselves, the SNFs are only canonic descriptions of the patterns of behavior observed in a culture. Conceptual graphs and dataflow graphs, their dynamic variety, provide means to reason over propositions in first order logics. Conceptual graphs, however, do not of themselves capture the ontological entities needed for such reasoning. The culture of an organization contains natural language entities that can be extracted for use in knowledge representation and reasoning. Together in a rigorous, two-step process, ontology charting from organizational semiotics and dataflow graphs from knowledge engineering provide a means to extract entities of interest from a subject domain such as the culture of organizations and then to represent these entities in formal logic reasoning. This chapter presents this process, and concludes with an example of how process improvement in an IT organization may be measured in this two-step process.
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Schiffel, Jeffrey A. "Using Organizational Semiotics and Conceptual Graphs in a Two-Step Method for Knowledge Management Process Improvement Measurement." In Information Resources Management, 427–42. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-965-1.ch216.

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The semantic normal forms of organizational semiotics extract structures from natural language texts that may be stored electronically. In themselves, the SNFs are only canonic descriptions of the patterns of behavior observed in a culture. Conceptual graphs and dataflow graphs, their dynamic variety, provide means to reason over propositions in first order logics. Conceptual graphs, however, do not of themselves capture the ontological entities needed for such reasoning. The culture of an organization contains natural language entities that can be extracted for use in knowledge representation and reasoning. Together in a rigorous, two-step process, ontology charting from organizational semiotics and dataflow graphs from knowledge engineering provide a means to extract entities of interest from a subject domain such as the culture of organizations and then to represent these entities in formal logic reasoning. This paper presents this process, and concludes with an example of how process improvement in an IT organization may be measured in this two-step process.
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Conference papers on the topic "Reasoning over temporal knowledge graphs"

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Geng, Yipeng, Yali Shao, Shanwen Zhang, and Xiaoyun He. "Multi-hop Temporal Knowledge Graph Reasoning over Few-Shot Relations with Novel Method." In 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2022. http://dx.doi.org/10.1109/iccece54139.2022.9712786.

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Garg, Sankalp, Navodita Sharma, Woojeong Jin, and Xiang Ren. "Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/386.

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Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowledge graphs available, we can use this additional information to predict the time series better. Recently, there has been a focus on the application of deep representation learning on dynamic graphs. These methods predict the structure of the graph by reasoning over the interactions in the graph at previous time steps. In this paper, we propose a new framework to incorporate the information from dynamic knowledge graphs for time series prediction. We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy. Our framework, DArtNet, learns a static embedding for every node in the graph as well as a dynamic embedding which is dependent on the dynamic attribute value (time-series). Then it captures the information from the neighborhood by taking a relation specific mean and encodes the history information using RNN. We jointly train the model link prediction and attribute prediction. We evaluate our method on five specially curated datasets for this problem and show a consistent improvement in time series prediction results. We release the data and code of model DArtNet for future research.
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Gao, Yifu, Linhui Feng, Zhigang Kan, Yi Han, Linbo Qiao, and Dongsheng Li. "Modeling Precursors for Temporal Knowledge Graph Reasoning via Auto-encoder Structure." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/284.

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Temporal knowledge graph (TKG) reasoning that infers missing facts in the future is an essential and challenging task. When predicting a future event, there must be a narrative evolutionary process composed of closely related historical facts to support the event's occurrence, namely fact precursors. However, most existing models employ a sequential reasoning process in an auto-regressive manner, which cannot capture precursor information. This paper proposes a novel auto-encoder architecture that introduces a relation-aware graph attention layer into transformer (rGalT) to accommodate inference over the TKG. Specifically, we first calculate the correlation between historical and predicted facts through multiple attention mechanisms along intra-graph and inter-graph dimensions, then constitute these mutually related facts into diverse fact segments. Next, we borrow the translation generation idea to decode in parallel the precursor information associated with the given query, which enables our model to infer future unknown facts by progressively generating graph structures. Experimental results on four benchmark datasets demonstrate that our model outperforms other state-of-the-art methods, and precursor identification provides supporting evidence for prediction.
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Saxena, Apoorv, Soumen Chakrabarti, and Partha Talukdar. "Question Answering Over Temporal Knowledge Graphs." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.520.

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Huang, Zijian, Meng-Fen Chiang, and Wang-Chien Lee. "LinE: Logical Query Reasoning over Hierarchical Knowledge Graphs." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539338.

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Wu, Hong, Zhe Wang, Kewen Wang, and Yi-Dong Shen. "Learning Typed Rules over Knowledge Graphs." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/51.

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Rule learning from large datasets has regained extensive interest as rules are useful for developing explainable approaches to many applications in knowledge graphs. However, existing methods for rule learning are still limited in terms of scalability and rule quality. This paper presents a new method for learning typed rules by employing entity class information. Our experimental evaluation shows the superiority of our system compared to state-of-the-art rule learners. In particular, we demonstrate the usefulness of typed rules in reasoning for link prediction.
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Zhao, Kangzhi, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, and Xing Xie. "Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397271.3401171.

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Liu, Zhibin, Zheng-Yu Niu, Hua Wu, and Haifeng Wang. "Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1187.

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Hu, Zhiwei, Victor Gutierrez Basulto, Zhiliang Xiang, Xiaoli Li, Ru Li, and Jeff Z. Pan. "Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/427.

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Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. Recently, to address this problem a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities has emerged. However, existing proposals ignore critical semantic knowledge inherently available in KGs, such as type information. To leverage type information, we propose a novel type-aware model, TypE-aware Message Passing (TEMP), which enhances the entity and relation representation in queries, and simultaneously improves generalization, and deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP’s effectiveness.
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Fu, Dongqi, Liri Fang, Ross Maciejewski, Vetle I. Torvik, and Jingrui He. "Meta-Learned Metrics over Multi-Evolution Temporal Graphs." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539313.

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Reports on the topic "Reasoning over temporal knowledge graphs"

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Bourgaux, Camille, and Anni-Yasmin Turhan. Temporal Query Answering in DL-Lite over Inconsistent Data. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2022.236.

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In ontology-based systems that process data stemming from different sources and that is received over time, as in context-aware systems, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontology-based query answering. We consider a recently proposed temporal query language that combines conjunctive queries with operators of propositional linear temporal logic and extend to this setting three inconsistency-tolerant semantics that have been introduced for querying inconsistent description logic knowledge bases. We investigate their complexity for DL-LiteR temporal knowledge bases, and furthermore complete the picture for the consistent case.
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Kriegel, Francesco. Learning description logic axioms from discrete probability distributions over description graphs (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.247.

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Description logics in their standard setting only allow for representing and reasoning with crisp knowledge without any degree of uncertainty. Of course, this is a serious shortcoming for use cases where it is impossible to perfectly determine the truth of a statement. For resolving this expressivity restriction, probabilistic variants of description logics have been introduced. Their model-theoretic semantics is built upon so-called probabilistic interpretations, that is, families of directed graphs the vertices and edges of which are labeled and for which there exists a probability measure on this graph family. Results of scientific experiments, e.g., in medicine, psychology, or biology, that are repeated several times can induce probabilistic interpretations in a natural way. In this document, we shall develop a suitable axiomatization technique for deducing terminological knowledge from the assertional data given in such probabilistic interpretations. More specifically, we consider a probabilistic variant of the description logic EL⊥, and provide a method for constructing a set of rules, so-called concept inclusions, from probabilistic interpretations in a sound and complete manner.
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