An Information Extraction Case Study . This paper develops a new paradigm for relational learning which allows for the representation and learning of relational information using propositional means . This paradigm suggests different tradeoffs than those in the traditional approach to this problem - the ILP approach - and as a result it enjoys several significant advantages over it . These models assume a latent variable for each entity and then the probability distribution over relationships between entities is modeled via a function over latent variables . One important example of relational learning via latent variables is text data modeling . In text data modeling , we are interested in modeling the relationship between words and documents . Next , to learn the remaining parts of the FOBN structure and its conditional probability tables by a standard Bayesian network learner , we also propose an efficient propositionalisation algorithm for translating the original data into the single table format . In this work , we provide a preliminary evaluation on the mutagenesis problem , a standard dataset for relational learning problem . The results are compared with the state-of-the-art ILP learner , the PROGOL system . Sentences may give rise to multiple spatial relations between words representing landmarks , trajectors and spatial indicators . Our contribution is to formulate the extraction task as a relational learning problem , for which we employ the recently introduced kLog framework . We discuss representational and modeling aspects , kLog 's flexibility in our task and we present current experimental results . The paper is structured in four parts . In the first , a theoretical reference on individual knowledge on the relational learning process will be established . In the second part , some hypothesis and the necessary methodology will be proposed . This investigation establishes important conclusions on the role of individual knowledge in the generation of the customer capital . Concretely , the explicit knowledge of the employees is the most meaningful in the relational learning process , although it is also true that the tacit knowledge and individual learning capacities have a special importance in the harvesting phase of knowledge . This paper proposes a novel grammar-based framework of concept representation for randomized search in Relational Learning ( RL ) , namely for Inductive Logic Programming . The utilization of grammars guarantees that the search operations produce syntactically correct concepts and that the background knowledge encoded in the grammar can be used both for directing the search and for restricting the space of possible concepts to relevant candidate concepts ( semantically valid concepts ) . CADS is based on the observation that typical users tend to form community structures , such that users with low affinity to such communities are indicative of anomalous and potentially illicit behavior . The model consists of two primary components : relational pattern extraction and anomaly detection . For relational pattern extraction , CADS infers community structures from CIS access logs , and subsequently derives communities , which serve as the CADS pattern core . CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies . Abstract : Learning relational patterns without supervision is a challenging and open problem for machine learning , yet a very desirable feature for real world applications . In this paper , we present a new algorithm for unsupervised learning of relational patterns . Given a relational database , this algorithm can find function-free Horn clauses without requiring users to label the data as positive or negative examples , and specify what target concepts to learn . It can suggest new metapatterns for humans to choose and test , or pursue these metapatterns on its own . This ability not only makes the process of data mining more efficient and productive , but also provide a new method for unsupervised learning of relational patterns . We have applied this method to several simple databases and obtained some encouraging results . These methods use multiplicative interactions between pixels or between features to represent correlation patterns across multiple images . In this paper , we review the recent work on relational feature learning , and we provide an analysis of the role that multiplicative interactions play in learning to encode relations . We also discuss how square-pooling and complex cell models can be viewed as a way to represent multiplicative interactions and thereby as a way to encode relations . There are many applications that depend on clustering relational data — e.g. , information retrieval , data mining in relational databases , and numerical taxonomy , so methods in this category are important . Several network methods for relational pattern recognition are given in Chapter 5 . A typical such situation arises in rule learning when truth values of some atoms are missing in the example description while we benefit from background knowledge . We first give a sample complexity result for learning from ambiguous examples , then we propose a framework for relational rule learning from ambiguous examples and describe the learning system LEAR . Finally we discuss various experiments in which we observe how LEAR copes with increasing degrees of incompleteness . In this section we discuss related work in network classification and unsupervised feature learning . section : Relational Learning Relational classification ( or collective classification ) methods [ reference ][ reference ][ reference ][ reference ] use links between data items as part of the classification process . Knowledge graph embedding models aim to represent entities and relations in continuous low-dimensional vector space , benefiting many research areas such as knowledge graph completion and web searching . However , previous works do not consider controlling information flow , which makes them hard to obtain useful latent information and limits model performance . Knowledge Graph Completion ( KGC ) aims at automatically predicting missing links for large-scale knowledge graphs . A vast number of state-of-the-art KGC techniques have been published in top conferences in several research fields including data mining , machine learning , and natural language processing . In the past years , knowledge graphs have shown to be beneficial for recommender systems , efficiently addressing paramount issues such as new items and data sparsity . In this paper , we show that the item recommendation problem can be seen as a specific case of knowledge graph completion problem , where the “ feedback ” property , which connects users to items that they like , has to be predicted . We empirically compare a set of state-of-the-art knowledge graph embeddings algorithms on the task of item recommendation on the Movielens 1 M and on the LibraryThing dataset . While there is a wide variety of graph completion techniques , there is no standard evaluation setup , so each proposal is evaluated using different datasets and metrics . In this paper we present AYNEC , a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow . It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs , the splitting into training and testing examples , and the generation of negative examples . We extend our previous work on entity and keyphrase extraction with relation extraction to populate the Knowledge Graph using an ontological vocabulary for notarial deeds . Furthermore , we perform Knowledge Graph completeness using link-prediction and inference . Our work was evaluated using different translational distance and semantic matching models to predict relations amongst literals by promoting them to entities and to infer new knowledge from existing entities . For the Knowledge Curation process , we establish sub-steps , such as knowledge assessment , cleaning , and enrichment . For each of them , we determine various categories and dimensions that have been developed and described in the literature and identify tasks which can be applied ( e.g. , Knowledge Graph completion and correctness , error detection and correction , identifying and resolving duplicates ) . Finally , we describe the deployment process of a Knowledge Graph based on the following principles : findability , accessibility , interoperability , and reusability . Domain-specific knowledge graph is an effective way to represent complex domain knowledge in a structured format and has shown great success in real-world applications . Most existing work on knowledge graph construction and completion shares several limitations in that sufficient external resources such as large-scale knowledge graphs and concept ontologies are required as the starting point . However , such extensive domain-specific labeling is highly time-consuming and requires special expertise , especially in biomedical domains . While there exist techniques ( such as reification ) that convert the non-binary relations of a knowledge hypergraph into binary ones , current embedding-based methods for knowledge graph completion do not work well out of the box for knowledge graphs obtained through these techniques . Thus we introduce HypE , a convolution-based embedding method for knowledge hypergraph completion . We also develop public benchmarks and baselines for our task and show experimentally that HypE is more effective than proposed baselines and existing methods . Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion . So far , the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph . This paper addresses machine learning models that embed knowledge graph entities and relationships toward the goal of predicting unseen triples , which is an important task because most knowledge graphs are by nature incomplete . We posit that while offline link prediction accuracy using embeddings has been steadily improving on benchmark datasets , such embedding models have limited practical utility in real-world knowledge graph completion tasks because it is not clear when their predictions should be accepted or trusted . To this end , we propose to calibrate knowledge graph embedding models to output reliable confidence estimates for predicted triples . We use vanilla policy gradient ( REINFORCE ) as the baseline , with task success rate as the evaluation metric . paragraph : Knowledge Base Completion We use WN18RR and NELL995 knowledge graph datasets for evaluation . Relation extraction plays an important role in many natural language processing tasks , such as knowledge graph and question answering system . Up to the present , many of the former relation methods work directly on the raw word sequence , so it is often subject to a major limitation : the lack of semantic information , which leads to the problem of the wrong category . Knowledge base completion has been an active research topic for knowledge graph . However , existing methods are of low learning and generalization abilities , and neglect the rich internal logic between entities and relationships . ( 3 ) It researches knowledge graph construction , and the knowledge graph is a semantic network graph which takes concepts and relationships respectively as vertices and edges . ( 4 ) It researches knowledge graph analysis . It includes construction effect analysis , evolution characteristics and rules analysis and application effect analysis . Then , pre-processing of all the titles was conducted by title management tool SATI to generate co-occurrence matrix . The results were visualized with embedded plug-in NetDraw for knowledge graph analysis . The results of comprehensive analysis show a general rising trend in the number of literatures . The paper discusses how the platform architecture design built on open standards enables its reusability in various application domains and use cases as well as facilitates integration of the knowledge graph with other parts of the organizational data and software infrastructure . We highlight the capabilities of the platform by describing its usage in four different knowledge graph application domains and share the lessons learnt from the practical experience of building knowledge graph applications in the enterprise context . In this paper , we give an overview of the knowledge graph evaluation tract at CCKS 2019 . By reviewing the task definition , successful methods , useful resources , good strategies and research challenges associated with each task in CCKS 2019 , this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches . Understanding , representing , and reasoning about Points Of Interest ( POI ) types such as Auto Repair , Body Shop , Gas Stations , or Planetarium , is a key aspect of geographic information retrieval , recommender systems , geographic knowledge graphs , as well as studying urban spaces in general , e.g. , for extracting functional or vague cognitive regions from user-generated content . One prerequisite to these tasks is the ability to capture the similarity and relatedness between POI types . In this study , we improved a novel interactive visualization platform , ALOHA , for the general public to obtain DS-related information through two UCD design iterations . The lessons learned from the two design iterations could serve as a guide to further enhance ALOHA and the development of other knowledge graph-based applications . Our study also showed that graph-based interactive visualization is a novel and acceptable approach to end-users who are interested in seeking online health information of various domains . Markov Logic Networks ( MLNs ) , which elegantly combine logic rules and probabilistic graphical models , can be used to address many knowledge graph problems . However , inference in MLN is computationally intensive , making the industrial-scale application of MLN very difficult . With the growing number and size of Linked Data datasets , it is crucial to make the data accessible and useful for users without knowledge of formal query languages . Two approaches towards this goal are knowledge graph visualization and natural language interfaces . Here , we investigate specifically question answering ( QA ) over Linked Data by comparing a diagrammatic visual approach with existing natural language-based systems . Entity typing is a necessary step in building knowledge graphs . So far , plenty of efforts have been made in mining type information for entities from online encyclopedias , but usually only coarse-grained type information could be obtained for entities , which are not fine enough for the purpose of knowledge graphs construction or query answering . The situation becomes even worse for mining type information for entities in Chinese . However , far from all available information is stored in existing knowledge graphs and manually adding new information is costly , which creates the need for algorithms that are able to automatically infer missing facts based on existing ones . Knowledge graphs can be represented by a third - order binary tensor , where each element corresponds to a triple , 1 indicating a true fact and 0 indicating the unknown ( either a false or a missing fact ) . One of the most important tasks in relational machine learning is link prediction : predicting whether two entities are related , based on known relations already present in a knowledge graph . Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization . Existing works either only utilize entity features , or rely on structured annotations from a single piece of context where the entity is mentioned .
An Information Extraction Case Study . This paper develops a new paradigm for relational learning which allows for the representation and learning of relational information using propositional means . This paradigm suggests different tradeoffs than those in the traditional approach to this problem - the ILP approach - and as a result it enjoys several significant advantages over it .
These models assume a latent variable for each entity and then the probability distribution over relationships between entities is modeled via a function over latent variables . One important example of relational learning via latent variables is text data modeling . In text data modeling , we are interested in modeling the relationship between words and documents .
Next , to learn the remaining parts of the FOBN structure and its conditional probability tables by a standard Bayesian network learner , we also propose an efficient propositionalisation algorithm for translating the original data into the single table format . In this work , we provide a preliminary evaluation on the mutagenesis problem , a standard dataset for relational learning problem . The results are compared with the state-of-the-art ILP learner , the PROGOL system .
Sentences may give rise to multiple spatial relations between words representing landmarks , trajectors and spatial indicators . Our contribution is to formulate the extraction task as a relational learning problem , for which we employ the recently introduced kLog framework . We discuss representational and modeling aspects , kLog 's flexibility in our task and we present current experimental results .
The paper is structured in four parts . In the first , a theoretical reference on individual knowledge on the relational learning process will be established . In the second part , some hypothesis and the necessary methodology will be proposed .
This investigation establishes important conclusions on the role of individual knowledge in the generation of the customer capital . Concretely , the explicit knowledge of the employees is the most meaningful in the relational learning process , although it is also true that the tacit knowledge and individual learning capacities have a special importance in the harvesting phase of knowledge .
This paper proposes a novel grammar-based framework of concept representation for randomized search in Relational Learning ( RL ) , namely for Inductive Logic Programming . The utilization of grammars guarantees that the search operations produce syntactically correct concepts and that the background knowledge encoded in the grammar can be used both for directing the search and for restricting the space of possible concepts to relevant candidate concepts ( semantically valid concepts ) .
CADS is based on the observation that typical users tend to form community structures , such that users with low affinity to such communities are indicative of anomalous and potentially illicit behavior . The model consists of two primary components : relational pattern extraction and anomaly detection . For relational pattern extraction , CADS infers community structures from CIS access logs , and subsequently derives communities , which serve as the CADS pattern core . CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies .
Abstract : Learning relational patterns without supervision is a challenging and open problem for machine learning , yet a very desirable feature for real world applications . In this paper , we present a new algorithm for unsupervised learning of relational patterns . Given a relational database , this algorithm can find function-free Horn clauses without requiring users to label the data as positive or negative examples , and specify what target concepts to learn .
It can suggest new metapatterns for humans to choose and test , or pursue these metapatterns on its own . This ability not only makes the process of data mining more efficient and productive , but also provide a new method for unsupervised learning of relational patterns . We have applied this method to several simple databases and obtained some encouraging results .
These methods use multiplicative interactions between pixels or between features to represent correlation patterns across multiple images . In this paper , we review the recent work on relational feature learning , and we provide an analysis of the role that multiplicative interactions play in learning to encode relations . We also discuss how square-pooling and complex cell models can be viewed as a way to represent multiplicative interactions and thereby as a way to encode relations .
There are many applications that depend on clustering relational data — e.g. , information retrieval , data mining in relational databases , and numerical taxonomy , so methods in this category are important . Several network methods for relational pattern recognition are given in Chapter 5 .
A typical such situation arises in rule learning when truth values of some atoms are missing in the example description while we benefit from background knowledge . We first give a sample complexity result for learning from ambiguous examples , then we propose a framework for relational rule learning from ambiguous examples and describe the learning system LEAR . Finally we discuss various experiments in which we observe how LEAR copes with increasing degrees of incompleteness .
In this section we discuss related work in network classification and unsupervised feature learning . section : Relational Learning Relational classification ( or collective classification ) methods [ reference ][ reference ][ reference ][ reference ] use links between data items as part of the classification process .
Knowledge graph embedding models aim to represent entities and relations in continuous low-dimensional vector space , benefiting many research areas such as knowledge graph completion and web searching . However , previous works do not consider controlling information flow , which makes them hard to obtain useful latent information and limits model performance .
Knowledge Graph Completion ( KGC ) aims at automatically predicting missing links for large-scale knowledge graphs . A vast number of state-of-the-art KGC techniques have been published in top conferences in several research fields including data mining , machine learning , and natural language processing .
In the past years , knowledge graphs have shown to be beneficial for recommender systems , efficiently addressing paramount issues such as new items and data sparsity . In this paper , we show that the item recommendation problem can be seen as a specific case of knowledge graph completion problem , where the “ feedback ” property , which connects users to items that they like , has to be predicted . We empirically compare a set of state-of-the-art knowledge graph embeddings algorithms on the task of item recommendation on the Movielens 1 M and on the LibraryThing dataset .
While there is a wide variety of graph completion techniques , there is no standard evaluation setup , so each proposal is evaluated using different datasets and metrics . In this paper we present AYNEC , a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow . It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs , the splitting into training and testing examples , and the generation of negative examples .
We extend our previous work on entity and keyphrase extraction with relation extraction to populate the Knowledge Graph using an ontological vocabulary for notarial deeds . Furthermore , we perform Knowledge Graph completeness using link-prediction and inference . Our work was evaluated using different translational distance and semantic matching models to predict relations amongst literals by promoting them to entities and to infer new knowledge from existing entities .
For the Knowledge Curation process , we establish sub-steps , such as knowledge assessment , cleaning , and enrichment . For each of them , we determine various categories and dimensions that have been developed and described in the literature and identify tasks which can be applied ( e.g. , Knowledge Graph completion and correctness , error detection and correction , identifying and resolving duplicates ) . Finally , we describe the deployment process of a Knowledge Graph based on the following principles : findability , accessibility , interoperability , and reusability .
Domain-specific knowledge graph is an effective way to represent complex domain knowledge in a structured format and has shown great success in real-world applications . Most existing work on knowledge graph construction and completion shares several limitations in that sufficient external resources such as large-scale knowledge graphs and concept ontologies are required as the starting point . However , such extensive domain-specific labeling is highly time-consuming and requires special expertise , especially in biomedical domains .
While there exist techniques ( such as reification ) that convert the non-binary relations of a knowledge hypergraph into binary ones , current embedding-based methods for knowledge graph completion do not work well out of the box for knowledge graphs obtained through these techniques . Thus we introduce HypE , a convolution-based embedding method for knowledge hypergraph completion . We also develop public benchmarks and baselines for our task and show experimentally that HypE is more effective than proposed baselines and existing methods .
Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion . So far , the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph .
This paper addresses machine learning models that embed knowledge graph entities and relationships toward the goal of predicting unseen triples , which is an important task because most knowledge graphs are by nature incomplete . We posit that while offline link prediction accuracy using embeddings has been steadily improving on benchmark datasets , such embedding models have limited practical utility in real-world knowledge graph completion tasks because it is not clear when their predictions should be accepted or trusted . To this end , we propose to calibrate knowledge graph embedding models to output reliable confidence estimates for predicted triples .
We use vanilla policy gradient ( REINFORCE ) as the baseline , with task success rate as the evaluation metric . paragraph : Knowledge Base Completion We use WN18RR and NELL995 knowledge graph datasets for evaluation .
Relation extraction plays an important role in many natural language processing tasks , such as knowledge graph and question answering system . Up to the present , many of the former relation methods work directly on the raw word sequence , so it is often subject to a major limitation : the lack of semantic information , which leads to the problem of the wrong category .
Knowledge base completion has been an active research topic for knowledge graph . However , existing methods are of low learning and generalization abilities , and neglect the rich internal logic between entities and relationships .
( 3 ) It researches knowledge graph construction , and the knowledge graph is a semantic network graph which takes concepts and relationships respectively as vertices and edges . ( 4 ) It researches knowledge graph analysis . It includes construction effect analysis , evolution characteristics and rules analysis and application effect analysis .
Then , pre-processing of all the titles was conducted by title management tool SATI to generate co-occurrence matrix . The results were visualized with embedded plug-in NetDraw for knowledge graph analysis . The results of comprehensive analysis show a general rising trend in the number of literatures .
The paper discusses how the platform architecture design built on open standards enables its reusability in various application domains and use cases as well as facilitates integration of the knowledge graph with other parts of the organizational data and software infrastructure . We highlight the capabilities of the platform by describing its usage in four different knowledge graph application domains and share the lessons learnt from the practical experience of building knowledge graph applications in the enterprise context .
In this paper , we give an overview of the knowledge graph evaluation tract at CCKS 2019 . By reviewing the task definition , successful methods , useful resources , good strategies and research challenges associated with each task in CCKS 2019 , this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches .
Understanding , representing , and reasoning about Points Of Interest ( POI ) types such as Auto Repair , Body Shop , Gas Stations , or Planetarium , is a key aspect of geographic information retrieval , recommender systems , geographic knowledge graphs , as well as studying urban spaces in general , e.g. , for extracting functional or vague cognitive regions from user-generated content . One prerequisite to these tasks is the ability to capture the similarity and relatedness between POI types .
In this study , we improved a novel interactive visualization platform , ALOHA , for the general public to obtain DS-related information through two UCD design iterations . The lessons learned from the two design iterations could serve as a guide to further enhance ALOHA and the development of other knowledge graph-based applications . Our study also showed that graph-based interactive visualization is a novel and acceptable approach to end-users who are interested in seeking online health information of various domains .
Markov Logic Networks ( MLNs ) , which elegantly combine logic rules and probabilistic graphical models , can be used to address many knowledge graph problems . However , inference in MLN is computationally intensive , making the industrial-scale application of MLN very difficult .
With the growing number and size of Linked Data datasets , it is crucial to make the data accessible and useful for users without knowledge of formal query languages . Two approaches towards this goal are knowledge graph visualization and natural language interfaces . Here , we investigate specifically question answering ( QA ) over Linked Data by comparing a diagrammatic visual approach with existing natural language-based systems .
Entity typing is a necessary step in building knowledge graphs . So far , plenty of efforts have been made in mining type information for entities from online encyclopedias , but usually only coarse-grained type information could be obtained for entities , which are not fine enough for the purpose of knowledge graphs construction or query answering . The situation becomes even worse for mining type information for entities in Chinese .
However , far from all available information is stored in existing knowledge graphs and manually adding new information is costly , which creates the need for algorithms that are able to automatically infer missing facts based on existing ones . Knowledge graphs can be represented by a third - order binary tensor , where each element corresponds to a triple , 1 indicating a true fact and 0 indicating the unknown ( either a false or a missing fact ) . One of the most important tasks in relational machine learning is link prediction : predicting whether two entities are related , based on known relations already present in a knowledge graph .
Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization . Existing works either only utilize entity features , or rely on structured annotations from a single piece of context where the entity is mentioned .
Relational Learning via Propositional Algorithms: An Information Extraction Case Study Latent feature networks for statistical relational learning Learning First-Order Bayesian Networks Relational Learning for Spatial Relation Extraction from Natural Language Individual Knowledge as a Bridge between Human and Customer Capital Grammatical Concept Representation for Randomised Optimisation Algorithms in Relational Learning Detection of anomalous insiders in collaborative environments via relational analysis of access logs Unsupervised Learning of Relational Patterns. Metapattern Generation for Integrated Data Mining Learning to Relate Images Cluster Analysis for Relational Data Learning First Order Rules from Ambiguous Examples DeepWalk: online learning of social representations MSGE: A Multi-step Gated Model for Knowledge Graph Completion A Re-evaluation of Knowledge Graph Completion Methods Translational Models for Item Recommendation AYNEC: All You Need for Evaluating Completion Techniques in Knowledge Graphs NotaryPedia: A Knowledge Graph of Historical Notarial Manuscripts How to Build a Knowledge Graph Constructing biomedical domain-specific knowledge graph with minimum supervision Knowledge Hypergraphs: Extending Knowledge Graphs Beyond Binary Relations Modeling Complex Relationship Paths for Knowledge Graph Completion Improving the Utility of Knowledge Graph Embeddings with Calibration M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Syntactic and Semantic Features Based Relation Extraction in Agriculture Domain Modeling of complex internal logic for knowledge base completion Research on Constructing the Knowledge Graph Based on Microblog Research on Dietary Health Preserving Based on Bibliometrics and Knowledge Graph metaphactory: A platform for knowledge graph management Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA From ITDL to Place2Vec: Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts ALOHA: developing an interactive graph-based visualization for dietary supplement knowledge graph through user-centered design Efficient Probabilistic Logic Reasoning with Graph Neural Networks A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data Mining High-Quality Fine-Grained Type Information from Chinese Online Encyclopedias TuckER: Tensor Factorization for Knowledge Graph Completion Entity Synonym Discovery via Multipiece Bilateral Context Matching
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IJCAI Canadian Conference on AI ILP ISDA CODASPY KDD ICTAI KDD '14 PAKDD (1) ESWC (Satellite Events) ESWC OTM Conferences NeurIPS WISA SIGSPATIAL/GIS ICLR KEOD WISE (2) EMNLP/IJCNLP
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