10,519 research outputs found
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering,
researchers usually make use of side information, such as social networks or
item attributes, to improve recommendation performance. This paper considers
the knowledge graph as the source of side information. To address the
limitations of existing embedding-based and path-based methods for
knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end
framework that naturally incorporates the knowledge graph into recommender
systems. Similar to actual ripples propagating on the surface of water, Ripple
Network stimulates the propagation of user preferences over the set of
knowledge entities by automatically and iteratively extending a user's
potential interests along links in the knowledge graph. The multiple "ripples"
activated by a user's historically clicked items are thus superposed to form
the preference distribution of the user with respect to a candidate item, which
could be used for predicting the final clicking probability. Through extensive
experiments on real-world datasets, we demonstrate that Ripple Network achieves
substantial gains in a variety of scenarios, including movie, book and news
recommendation, over several state-of-the-art baselines.Comment: CIKM 201
Vermeidung von ReprÀsentationsheterogenitÀten in realweltlichen Wissensgraphen
Knowledge graphs are repositories providing factual knowledge about entities. They are a great source of knowledge to support modern AI applications for Web search, question answering, digital assistants, and online shopping. The advantages of machine learning techniques and the Web's growth have led to colossal knowledge graphs with billions of facts about hundreds of millions of entities collected from a large variety of sources. While integrating independent knowledge sources promises rich information, it inherently leads to heterogeneities in representation due to a large variety of different conceptualizations. Thus, real-world knowledge graphs are threatened in their overall utility. Due to their sheer size, they are hardly manually curatable anymore. Automatic and semi-automatic methods are needed to cope with these vast knowledge repositories. We first address the general topic of representation heterogeneity by surveying the problem throughout various data-intensive fields: databases, ontologies, and knowledge graphs. Different techniques for automatically resolving heterogeneity issues are presented and discussed, while several open problems are identified. Next, we focus on entity heterogeneity. We show that automatic matching techniques may run into quality problems when working in a multi-knowledge graph scenario due to incorrect transitive identity links. We present four techniques that can be used to improve the quality of arbitrary entity matching tools significantly. Concerning relation heterogeneity, we show that synonymous relations in knowledge graphs pose several difficulties in querying. Therefore, we resolve these heterogeneities with knowledge graph embeddings and by Horn rule mining. All methods detect synonymous relations in knowledge graphs with high quality. Furthermore, we present a novel technique for avoiding heterogeneity issues at query time using implicit knowledge storage. We show that large neural language models are a valuable source of knowledge that is queried similarly to knowledge graphs already solving several heterogeneity issues internally.Wissensgraphen sind eine wichtige Datenquelle von EntitĂ€tswissen. Sie unterstĂŒtzen viele moderne KI-Anwendungen. Dazu gehören unter anderem Websuche, die automatische Beantwortung von Fragen, digitale Assistenten und Online-Shopping. Neue Errungenschaften im maschinellen Lernen und das auĂerordentliche Wachstum des Internets haben zu riesigen Wissensgraphen gefĂŒhrt. Diese umfassen hĂ€ufig Milliarden von Fakten ĂŒber Hunderte von Millionen von EntitĂ€ten; hĂ€ufig aus vielen verschiedenen Quellen. WĂ€hrend die Integration unabhĂ€ngiger Wissensquellen zu einer groĂen Informationsvielfalt fĂŒhren kann, fĂŒhrt sie inhĂ€rent zu HeterogenitĂ€ten in der WissensreprĂ€sentation. Diese HeterogenitĂ€t in den Daten gefĂ€hrdet den praktischen Nutzen der Wissensgraphen. Durch ihre GröĂe lassen sich die Wissensgraphen allerdings nicht mehr manuell bereinigen. DafĂŒr werden heutzutage hĂ€ufig automatische und halbautomatische Methoden benötigt. In dieser Arbeit befassen wir uns mit dem Thema ReprĂ€sentationsheterogenitĂ€t. Wir klassifizieren HeterogenitĂ€t entlang verschiedener Dimensionen und erlĂ€utern HeterogenitĂ€tsprobleme in Datenbanken, Ontologien und Wissensgraphen. Weiterhin geben wir einen knappen Ăberblick ĂŒber verschiedene Techniken zur automatischen Lösung von HeterogenitĂ€tsproblemen. Im nĂ€chsten Kapitel beschĂ€ftigen wir uns mit EntitĂ€tsheterogenitĂ€t. Wir zeigen Probleme auf, die in einem Multi-Wissensgraphen-Szenario aufgrund von fehlerhaften transitiven Links entstehen. Um diese Probleme zu lösen stellen wir vier Techniken vor, mit denen sich die QualitĂ€t beliebiger Entity-Alignment-Tools deutlich verbessern lĂ€sst. Wir zeigen, dass RelationsheterogenitĂ€t in Wissensgraphen zu Problemen bei der Anfragenbeantwortung fĂŒhren kann. Daher entwickeln wir verschiedene Methoden um synonyme Relationen zu finden. Eine der Methoden arbeitet mit hochdimensionalen Wissensgrapheinbettungen, die andere mit einem Rule Mining Ansatz. Beide Methoden können synonyme Relationen in Wissensgraphen mit hoher QualitĂ€t erkennen. DarĂŒber hinaus stellen wir eine neuartige Technik zur Vermeidung von HeterogenitĂ€tsproblemen vor, bei der wir eine implizite WissensreprĂ€sentation verwenden. Wir zeigen, dass groĂe neuronale Sprachmodelle eine wertvolle Wissensquelle sind, die Ă€hnlich wie Wissensgraphen angefragt werden können. Im Sprachmodell selbst werden bereits viele der HeterogenitĂ€tsprobleme aufgelöst, so dass eine Anfrage heterogener Wissensgraphen möglich wird
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks
into low-dimensional vector spaces, which is useful in many tasks such as
visualization, node classification, and link prediction. Most existing graph
embedding methods do not scale for real world information networks which
usually contain millions of nodes. In this paper, we propose a novel network
embedding method called the "LINE," which is suitable for arbitrary types of
information networks: undirected, directed, and/or weighted. The method
optimizes a carefully designed objective function that preserves both the local
and global network structures. An edge-sampling algorithm is proposed that
addresses the limitation of the classical stochastic gradient descent and
improves both the effectiveness and the efficiency of the inference. Empirical
experiments prove the effectiveness of the LINE on a variety of real-world
information networks, including language networks, social networks, and
citation networks. The algorithm is very efficient, which is able to learn the
embedding of a network with millions of vertices and billions of edges in a few
hours on a typical single machine. The source code of the LINE is available
online.Comment: WWW 201
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Learning knowledge graph (KG) embeddings is an emerging technique for a
variety of downstream tasks such as summarization, link prediction, information
retrieval, and question answering. However, most existing KG embedding models
neglect space and, therefore, do not perform well when applied to (geo)spatial
data and tasks. For those models that consider space, most of them primarily
rely on some notions of distance. These models suffer from higher computational
complexity during training while still losing information beyond the relative
distance between entities. In this work, we propose a location-aware KG
embedding model called SE-KGE. It directly encodes spatial information such as
point coordinates or bounding boxes of geographic entities into the KG
embedding space. The resulting model is capable of handling different types of
spatial reasoning. We also construct a geographic knowledge graph as well as a
set of geographic query-answer pairs called DBGeo to evaluate the performance
of SE-KGE in comparison to multiple baselines. Evaluation results show that
SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic
query answering task. This demonstrates the effectiveness of our
spatially-explicit model and the importance of considering the scale of
different geographic entities. Finally, we introduce a novel downstream task
called spatial semantic lifting which links an arbitrary location in the study
area to entities in the KG via some relations. Evaluation on DBGeo shows that
our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI
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