Type-Constrained Representation Learning in Knowledge Graphs
Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for
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Siemens AG Corporate Technology, Munich, Germany {Denis.Krompass,Volker.Tresp}@siemens.com Ludwig Maximilian University, 80538 Munich, Germany [email protected]
Abstract. Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in linkprediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, typeconstraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data. Keywords: Knowledge graph · Representation learning · Latent variable models · Type-constraints · Local closed-world assumption · Linkprediction
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Introduction
Knowledge graphs (KGs), i.e., graph-based knowledge-bases, have proven to be sources of valuable information that have become important for various applications like web-search or question answering. Whereas, KGs were initially driven by academic efforts which resulted in KGs like Freebase [4], DBpedia [3], Nell [6] or YAGO [9], more recently commercial applications have evolved; a significant c Springer International Publishing Switzerland 2015 M. Arenas et al. (Eds.): ISWC 2015, Part I, LNCS 9366, pp. 640–655, 2015. DOI: 10.1007/978-3-319-25007-6 37
Type-Constrained Representation Learning in Knowledge Graphs
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commercial application is the Freebase powered Google Knowledge Graph that supports Google’s web search and the smart assistant Google Now, or Microsoft’s Satori that supports Bing and Cortana. A related activity is the linked open data initiative which interlinks data sources using the W3C Resource Description Framework (RDF) [13] and thus also generates a huge KG accessible via querying [2]. Even though these graphs have reached an impressive size, containing billions of facts about the world, they are not error-free and far from complete. In Freebase and DBpedia for example a vast amount of p
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