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Register a new userData Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage?
جمع البيانات مقابل معرفة الرسم البياني المعرفة: ما هو مطلوب لتحسين التغطية؟
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تناقش ورقة الاستقصاء / المركبة هذه الطرق لتحسين تغطية الموارد مثل WordNet.RAPP تقدر الارتباطات، RHO، بين إحصائيات كوربوس ومعايير الهاجولية.RHO يحسن مع الكمية (حجم كوربوس) والجودة (التوازن).1M الكلمات تكفي لتقديرات بسيطة (ترددات غير منغرام)، ولكن 100x على الأقل مطلوب لتقديرات جيدة للجمعيات والمواد المدمجة.نظرا مثل هذه التقديرات، فإن تغطية Wordnet رائعة.تم تطوير WordNET في SEMCOR، عينة صغيرة (كلمات 200K) من كوربوس البني.محاولات إكمال الرسم البياني المعرفي (KGC) تعلم الروابط المفقودة من مجموعات فرعية من مجموعات فرعية.لكن تقديرات Rapp للأحجام تشير إلى أنها ستكون أكثر ربحية لجمع المزيد من البيانات من استنتاج المعلومات المفقودة التي ليست موجودة.
This survey/position paper discusses ways to improve coverage of resources such as WordNet. Rapp estimated correlations, rho, between corpus statistics and pyscholinguistic norms. rho improves with quantity (corpus size) and quality (balance). 1M words is enough for simple estimates (unigram frequencies), but at least 100x more is required for good estimates of word associations and embeddings. Given such estimates, WordNet's coverage is remarkable. WordNet was developed on SemCor, a small sample (200k words) from the Brown Corpus. Knowledge Graph Completion (KGC) attempts to learn missing links from subsets. But Rapp's estimates of sizes suggest it would be more profitable to collect more data than to infer missing information that is not there.
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Abstract To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs and many research efforts have been devoted to the automatic construc
tion of CKGs. Previous approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an capability to generate unobvious concepts. In this work, we aim to bridge this gap. We propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-order relationships between concepts. We instantiate this general framework to four special cases: long path, path-to-path, router, and graph-node-path. Experiments on two datasets demonstrate the effectiveness of our methods. The code will be released via the public GitHub repository.
Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in rece
nt years. That is, they investigate better representational models to fit symmetry/antisymmetry and combination relationships. The current embedding models are more inclined to utilize the identical vector for the same entity in various triples to measure the matching performance. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Inspired by this fact, this paper designs Semantic Filter Based on Relations(SFBR) to extract the required attributes of the entities. Then the rationality of triples is compared under these extracted attributes through the traditional embedding models. The semantic filter module can be added to most geometric and tensor decomposition models with minimal additional memory. experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance. The tensor decomposition models with SFBR have achieved state-of-the-art.
The last years have shown rapid developments in the field of multimodal machine learning, combining e.g., vision, text or speech. In this position paper we explain how the field uses outdated definitions of multimodality that prove unfit for the mach
ine learning era. We propose a new task-relative definition of (multi)modality in the context of multimodal machine learning that focuses on representations and information that are relevant for a given machine learning task. With our new definition of multimodality we aim to provide a missing foundation for multimodal research, an important component of language grounding and a crucial milestone towards NLU.
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events
in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
Various temporal knowledge graph (KG) completion models have been proposed in the recent literature. The models usually contain two parts, a temporal embedding layer and a score function derived from existing static KG modeling approaches. Since the
approaches differ along several dimensions, including different score functions and training strategies, the individual contributions of different temporal embedding techniques to model performance are not always clear. In this work, we systematically study six temporal embedding approaches and empirically quantify their performance across a wide range of configurations with about 3000 experiments and 13159 GPU hours. We classify the temporal embeddings into two classes: (1) timestamp embeddings and (2) time-dependent entity embeddings. Despite the common belief that the latter is more expressive, an extensive experimental study shows that timestamp embeddings can achieve on-par or even better performance with significantly fewer parameters. Moreover, we find that when trained appropriately, the relative performance differences between various temporal embeddings often shrink and sometimes even reverse when compared to prior results. For example, TTransE (CITATION), one of the first temporal KG models, can outperform more recent architectures on ICEWS datasets. To foster further research, we provide the first unified open-source framework for temporal KG completion models with full composability, where temporal embeddings, score functions, loss functions, regularizers, and the explicit modeling of reciprocal relations can be combined arbitrarily.
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