Learning Sequence Patterns in Knowledge Graph Triples to Predict Inconsistencies Mahmoud Elbattah1,2 and Conor Ryan1 1Department of Computer Science and Information Systems, University of Limerick, Ireland 2Laboratoire MIS, Université de Picardie Jules Verne, France Keywords: Knowledge Graphs, Knowledgebase, Semantic Web, Machine Learning. Abstract: The current trend towards the Semantic Web and Linked Data has resulted in an unprecedented volume of data being continuously published on the Linked Open Data (LOD) cloud. Massive Knowledge Graphs (KGs) are increasingly constructed and enriched based on large amounts of unstructured data. However, the data quality of KGs can still suffer from a variety of inconsistencies, misinterpretations or incomplete information as well. This study investigates the feasibility of utilising the subject-predicate-object (SPO) structure of KG triples to detect possible inconsistencies. The key idea is hinged on using the Freebase-defined entity types for extracting the unique SPO patterns in the KG. Using Machine learning, the problem of predicting inconsistencies could be approached as a sequence classification task. The approach applicability was experimented using a subset of the Freebase KG, which included about 6M triples. The experiments proved promising results using Convnet and LSTM models for detecting inconsistent sequences. 1 INTRODUCTION KG that can inspect semantic relations among entities (e.g. persons, places), which are all inter-linked in a The vision of the Semantic Web is to allow for huge social graph. storing, publishing, and querying knowledge in a Equally important, many large-scale KGs have semantically structured form (Berners-Lee, Hendler, been made available thanks to the movement of and Lassila, 2001). To this end, a diversity of Linked Open Data (LOD) (Bizer, Heath, and Berners- technologies has been developed, which contributed Lee, 2011). Examples include the DBpedia KG, to facilitating the processing and integration of data which consists of about 1.5B facts describing more on the Web. Knowledge graphs (KGs) have come into than 10M entities (Lehmann et al. 2015). Further prominence particularly as one of the key instruments openly available KGs were introduced over past years to realise the Semantic Web. including Freebase, YAGO, Wikidata, and others. As KGs can be loosely defined as large networks of such, the use of KGs has now become a mainstream entities, their semantic types, properties, and for knowledge representation on the Web. relationships connecting entities (Kroetsch and However, the data quality of KGs remains an Weikum, 2015). At its inception, the Semantic Web issue of ongoing investigation. KGs are largely has promoted such a graph-based representation of constructed by extracting contents using web knowledge through the Resource Description scarpers, or through crowdsourcing. Therefore, the Framework (RDF) standards. The concept was extracted knowledge could unavoidably contain reinforced by Google in 2012, which utilised a vast inconsistencies, misinterpretations, or incomplete KG to process its web queries (Singhal, 2012). The information. Moreover, data sources may include use of KG empowered rich semantics that could yield conflicting data for the same exact entity. For a significant improvement in search results. example, (Yasseri et al., 2014) analysed the top Other major companies (e.g. Facebook, controversial topics in 10 different language versions Microsoft) pursued the same path and created their of Wikipedia. Such controversial entities can lead to own KGs to enable semantic queries and smarter inconsistencies in KGs as well. delivery of data. For instance, Facebook provides a In this respect, this study explores a Machine Learning-based approach to detect possible 435 Elbattah, M. and Ryan, C. Learning Triple Sequence Patterns in Knowledge Graphs to Predict Inconsistencies. DOI: 10.5220/0008494604350441 In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 435-441 ISBN: 978-989-758-382-7 Copyright c 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems inconsistencies within KGs. In particular, we investigated the feasibility of learning the sequence 2.2 Refinement of KGs patterns of triples in terms of subject-predicate-object (SPO). The approach presented deemed successful Various methods were proposed for the validation for classifying triples including inconsistent SPO and refinement of the quality of KGs. In general, there patterns. Our experiments were conducted using a have been two main goals of KG refinement large subset of Freebase data that comprised about including (Paulheim, 2017): i) Adding missing 6M triples. knowledge to the graph, and ii) Identifying wrong information in the graph. The review here is more focused on studies that addressed the latter case. 2 BACKGROUND AND RELATED One of the early efforts was the DeFacto (Deep Fact Validation) method (Gerber et al., 2015). The WORK DeFacto approach was based on finding trustworthy sources on the Web in order to validate KG triples. This section reviews the literature from a two-fold This could be achieved by collecting and combining perspective. Initially, the first part aims to get the evidence from webpages in several languages. In the reader’s acquaintance with the quality aspects of KGs. same manner, (Liu, d’Aquin, and Motta, 2017) The review particularly explores how the consistency presented an approach for the automatic validation of of KGs was described in literature. Subsequently, we KG triples. Named as Triples Accuracy Assessment present selective studies that introduced methods to (TAA), their approach works by finding a consensus deal with consistency-related issues in KGs. of matched triples from other KGs. A confidence score can be calculated to indicate the correctness of 2.1 Quality Metrics of KGs source triples. Other studies attempted to use Machine Learning With ongoing initiatives for publishing data into the to detect the quality deficiencies in KGs. In this Linked Data cloud, there has been a growing interest regard, association rule mining has been considered in assessing the quality of KGs. Part of the efforts was as a suitable technique for discovering frequent basically directed towards discussing the different patterns within RDF triples. For instance, (Paulheim, quality aspects of KGs (e.g. Zaveri et al., 2016; 2012) used rule mining to find common patterns Debattista et al. 2018; Färber et al. 2018). This section between types, which can be applied to briefly reviews representative studies in this regard. knowledgebases. The approach was experimented on Particular attention is placed on the consistency of DBpedia providing results at an accuracy of 85.6%. KGs, which is the focus of this study. Likewise, (Abedjan. and Naumann, 2013) proposed a (Zaveri et al. 2016) defined three categories of KG rule-based approach for improving the quality of RDF quality including: i) Accuracy, ii) Consistency, and datasets. Their approach can help avoid iii) Conciseness. The consistency category contained inconsistencies through providing predicate several metrics such as misplaced classes or suggestions, and enrichment with missing facts. properties, or misuse of predicates. Likewise, (Färber Further studies continued to develop similar rule- et al., 2018) attempted to define a comprehensive set based approaches such as (Barati, Bai, and Liu, of dimensions and criteria to evaluate the data quality 2016), and others. of KGs. The quality dimensions were organised into More recently, (Rico et al., 2018) developed a four broad categories as follows: i) Intrinsic, ii) classification model for predicting incorrect Contextual, iii) Representational data quality, and iv) mappings in DBpedia. Different classifiers were Accessibility. The consistency of KGs was included experimented, and the highest accuracy (≈ 93%) under the Intrinsic Category. In particular, could be achieved using a Random Forest Model. consistency-related criteria were given as follows: However, further potentials for using Machine Check of schema restrictions during inserting Learning may have not been explored in literature yet. new statements. For instance, utilising the sequence-based nature of Consistency of statements against class triples for learning, which is the focus of this study. constraints. Our approach is based on applying sequence Consistency of statements against relation classification techniques to the SPO triples in KGs. constraints. To the best of our knowledge, such approach has not been applied before. 436 Learning Triple Sequence Patterns in Knowledge Graphs to Predict Inconsistencies 3 DATA DESCRIPTION of Relativity). Figure 1 illustrates a basic example of triples in Freebase KG. As mentioned earlier, the study’s approach was Furthermore, Freebase was thoroughly experimented on the Freebase KG. The following architected around a well-structured schema. Entities sections describe the dataset used along with a brief in the KG are mapped to abstract types. A type serves review of the key features of Freebase. as a conceptual container of properties commonly needed for characterising a particular class of entities 3.1 Overview (e.g. Author, Book, Location etc.). In this manner, entities can be considered as instances of the schema Freebase was introduced