Білім графы

AbstractThe European project NewsReader develops technology to process daily news streams in 4 languages, extracting what happened, when, where and who was involved. NewsReader does not just read a single …...

Knowledge graphs are recently used for enriching query representations in an entity-aware way for the rich facts or- ganized around entities in it. How- ever, few of the methods pay …...

Many knowledge repositories nowadays con- tain billions of triplets, i.e. (head-entity, re- lationship, tail-entity), as relation instances. These triplets form a directed graph with enti- ties as nodes and relationships …...

Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting …...

We describe how a question-answering sys- tem can learn about its domain from conver- sational dialogs. Our system learns to relate concepts in science questions to propositions in a fact …...

AbstractRepresenting structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn …...

AbstractIn recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore …...

AbstractDistantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful …...

AbstractQuestion answering over knowledge graphs is an important problem of interest both commercially and academically. There is substantial interest in the class of natural language questions that can be answered …...

AbstractMachine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge …...

AbstractLarge scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other …...

AbstractIdentifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an …...

AbstractEntity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented …...

AbstractThough great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, …...

AbstractGenerating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, …...

AbstractComplex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs. Traditional knowledge representation learning (KRL) …...

AbstractThis paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can …...

AbstractVisual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. A VQA model combines visual and …...

AbstractCross-lingual Entity alignment is an essential part of building a knowledge graph, which can help integrate knowledge among different language knowledge graphs. In the real KGs, there exists an imbalance …...

AbstractPrevious works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such …...

AbstractEntity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling …...

AbstractCognates are variants of the same lexical form across different languages; for example “fonema” in Spanish and “phoneme” in English are cognates, both of which mean “a unit of sound”. …...

AbstractCapturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations. Recent related methods built on Euclidean embeddings …...

AbstractKnowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings …...

AbstractPrevious studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training …...

AbstractOperationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core – such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) …...

AbstractNatural Language Inference (NLI) is a vital task in natural language processing. It aims to identify the logical relationship between two sentences. Most of the existing approaches make such inference …...

AbstractWalk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a …...

AbstractTo exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical. However, most …...

AbstractMisinformation spreads across media, community, and knowledge graphs in the Web by not only human agents but also information extraction algorithms that extract factual statements from unstructured textual data to …...

AbstractMedical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error …...

AbstractGenerating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs …...

AbstractKnowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers …...

AbstractBiomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. …...

Commonsense question answering (QA) re- quires a model to grasp commonsense and factual knowledge to answer questions about world events. Many prior methods couple lan- guage modeling with knowledge graphs …...

AbstractGiven the diversity of the candidates and complexity of job requirements, and since interviewing is an inherently subjective process, it is an important task to ensure consistent, uniform, efficient and …...

AbstractExisting entity alignment models mainly use the topology structure of the original knowledge graph and have achieved promising performance. However they are still challenged by the heterogeneous topological neighborhood structures …...

AbstractKnowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as …...

Graph Attention Networks (GATs) have proven a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data, e.g., Knowledge Graphs (KGs). While …...

ion, Concretization and Completion Jie Zhou1,2∗, Shengding Hu1,2∗, Xin Lv1,2, Cheng Yang4, Zhiyuan Liu1,2,3†, Wei Xu5, Jie Jiang5, Juanzi Li1,2, Maosong Sun1,2,3 1Department of Computer Science and Technology 2Institute for …...

AbstractThis paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across …...

Interactive argument pair identification is es- sential in the context of dialogical argumenta- tion mining. Existing research treats it as a problem of sentence matching and largely re- lies on …...

Lifelong topic models mainly focus on in- domain text streams in which each chunk only contains documents from a single domain. To overcome data diversity of the in-domain cor- pus, …...

AbstractEntity linking (EL) is the task of disambiguating mentions appearing in text by linking them to entities in a knowledge graph, a crucial task for text understanding, question answering or …...

Semantic embedding has been widely investi- gated for aligning knowledge graph (KG) en- tities. Current methods have explored and uti- lized the graph structure, the entity names, and attributes, but …...

AbstractTemporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has …...

AbstractKnowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge …...

AbstractThere is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing …...

AbstractRelational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including …...

In challenging economic times, obtaining value for money by ensuring financial integrity and fairer distribution of services are among the top priorities for social and health-care systems globally. However, healthcare …...

AbstractThis paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a large-scale …...

AbstractIn pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic …...

AbstractThe need for manual review of various financial texts, such as company filings and news, presents a major bottleneck in financial analysts’ work. Thus, there is great potential for the …...

AbstractWe propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the …...

AbstractIn this paper, we report experiments on Few- and Zero-shot Knowledge Graph completion, where the objective is to add missing relational links between entities into an existing Knowledge Graph with …...

AbstractConversational recommendation systems (CRS) aim to determine a goal item by sequentially tracking users’ interests through multi-turn conversation. In CRS, implicit patterns of user interest sequence guide the smooth transition …...

AbstractFew-shot knowledge graph completion (FKGC) has become a new research focus in the field of knowledge graphs in recent years, which aims to predict the missing links for relations that …...

AbstractCurrent Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module, where the retriever selects potentially relevant passages from open-source documents for a given question, and …...

AbstractMulti-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper …...

AbstractKnowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector …...

AbstractRule mining is an effective approach for reasoning over knowledge graph (KG). Existing works mainly concentrate on mining rules. However, there might be several rules that could be applied for …...

AbstractKnowledge Graphs (KGs) stores world knowledge that benefits various reasoning-based applications. Due to their incompleteness, a fundamental task for KGs, which is known as Knowledge Graph Completion (KGC), is to …...

AbstractUsers interacting with voice assistants today need to phrase their requests in a very specific manner to elicit an appropriate response. This limits the user experience, and is partly due …...

AbstractRecently, knowledge graphs (KGs) have won noteworthy success in commonsense question answering. Existing methods retrieve relevant subgraphs in the KGs through key entities and reason about the answer with language …...

AbstractIncorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs), message passing on a graph is independent …...

AbstractLogical reasoning over incomplete knowledge graphs to answer complex logical queries is a challenging task. With the emergence of new entities and relations in constantly evolving KGs, inductive logical reasoning …...

AbstractIn this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions …...