Building and Using a Knowledge Graph to Combat Human Trafficking

Szekely, Pedro; Knoblock, Craig A.; Slepicka, Jason; Philpot, Andrew; Singh, Amandeep; Yin, Chengye; Kapoor, Dipsy; Natarajan, Prem; Marcu, Daniel; Knight, Kevin; Stallard, David; Karunamoorthy, Subessware S.; Bojanapalli, Rajagopal; Minton, Steven; Amanatullah, Brian; Hughes, Todd; Tamayo, Mike; Flynt, David; Artiss, Rachel; Chang, Shih-Fu; Chen, Tao; Hiebel, Gerald; Ferreira, L. S.

doi:10.1007/978-3-319-25010-6_12

Cited by 76 publications

(56 citation statements)

References 6 publications

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“…Gawriljuk, et al [6] suggest a scalable framework for building knowledge graphs. Szekely, et al [13] propose an approach for building knowledge graphs and devise the DIG system which resorts to KARMA [7], a semantic data integration system proposed by Knoblock et al, for integration at the level of schema. Collarana et al introduce MINTE [3], a semantic integration technique for RDF graphs.…”

Section: Related Workmentioning

confidence: 99%

MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

Jozashoori

Vidal

2019

Lecture Notes in Computer Science

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Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.

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Section: Related Workmentioning

confidence: 99%

MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

Jozashoori

Vidal

2019

Lecture Notes in Computer Science

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“…The idea is to enhance the behavior of classical Web crawlers, that consider all pages reachable from a given page, to be more selective; selectivity is obtained by considering e.g., a set of predefined topics [36] or meta data within HTML pages [29]. A more recent line of related research looks into building (domain-specific) knowledge graphs by exploiting semantic technologies to reconcile the data continuously crawled from diverse sources [35]. In a way, these approaches mimic the process of filtering performed by our approach but on a less expressive scale due to the limited expressiveness of the filtering mechanism as compared to our language.…”

Section: Related Workmentioning

confidence: 99%

SPARQL with property paths on the Web

Hartig

Pirrò

2017

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Abstract. Linked Data on the Web represents an immense source of knowledge suitable to be automatically processed and queried. In this respect, there are different approaches for Linked Data querying that differ on the degree of centralization adopted. On one hand, the SPARQL query language, originally defined for querying single datasets, has been enhanced with features to query federations of datasets; however, this attempt is not sufficient to cope with the distributed nature of data sources available as Linked Data. On the other hand, extensions or variations of SPARQL aim to find trade-offs between centralized and fully distributed querying. The idea is to partially move the computational load from the servers to the clients. Despite the variety and the relative merits of these approaches, as of today, there is no standard language for querying Linked Data on the Web. A specific requirement for such a language to capture the distributed, graph-like nature of Linked Data sources on the Web is a support of graph navigation. Recently, SPARQL has been extended with a navigational feature called property paths (PPs). However, the semantics of SPARQL restricts the scope of navigation via PPs to single RDF graphs. This restriction limits the applicability of PPs for querying distributed Linked Data sources on the Web. To fill this gap, in this paper we provide formal foundations for evaluating PPs on the Web, thus contributing to the definition of a query language for Linked Data. We first introduce a family of reachability-based query semantics for PPs that distinguish between navigation on the Web and navigation at the data level. Thereafter, we consider another, alternative query semantics that couples Web graph navigation and data level navigation; we call it context-based semantics. Given these semantics, we find that for some PP-based SPARQL queries a complete evaluation on the Web is not possible. To study this phenomenon we introduce a notion of Web-safeness of queries, and prove a decidable syntactic property that enables systems to identify queries that are Web-safe. In addition to establishing these formal foundations, we conducted an experimental comparison of the context-based semantics and a reachability-based semantics. Our experiments show that when evaluating a PP-based query under the context-based semantics one experiences a significantly smaller number of dereferencing operations, but the computed query result may contain less solutions.

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“…Two papers have won Best Paper awards [9], [10]. More broadly, our experience has spurred us to write a graduate-level textbook on knowledge graphs, which will be published by MIT Press later this year.…”

Section: Research Outputsmentioning

confidence: 99%

“…DIG has yielded over 15 peer-reviewed publications over the course of three years, some examples being [9], [10], [6], [8], [5], [2], [4], [3], [1] with several more under review, and at least one dataset resource [7]. Two papers have won Best Paper awards [9], [10].…”

Section: Research Outputsmentioning

confidence: 99%

Domain-specific Insight Graphs (DIG)

Szekely¹,

Kejriwal²

2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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The DARPA Memex program was established with the goal of funding research into building domain-specific search systems that integrated state-of-the-art focused crawling ('domain discovery') information extraction and semantic search, and that could be used by users and domain experts with no programming or technical experience. Domain-specific Insight Graphs (DIG) was proposed and funded under Memex and has led to an end-to-end search system currently being used by over 200 law enforcement for combating human trafficking, by investigators from the Securities and Exchange Commission (SEC) in the US for investigating securities fraud, and for numerous other domains of a difficult, socially consequential (e.g., investigative) and unusual nature.

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Building and Using a Knowledge Graph to Combat Human Trafficking

Cited by 76 publications

References 6 publications

MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

SPARQL with property paths on the Web

Domain-specific Insight Graphs (DIG)

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