LogMap 2.0: towards logic-based, scalable and interactive ontology matching

doi:10.1145/2166896.2166911

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LogMap 2.0: towards logic-based, scalable and interactive ontology matching

Proceedings Article•DOI•

LogMap 2.0: towards logic-based, scalable and interactive ontology matching

Ernesto Jiménez-Ruiz¹, Bernardo Cuenca Grau¹, Yujiao Zhou¹•Institutions (1)

University of Oxford¹

07 Dec 2011-pp 45-46

TL;DR: A much improved version of LogMap, a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities, and provides the necessary infrastructure for domain experts to interactively contribute to the matching process.

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Abstract: In this paper we present a much improved version of LogMap, a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities. LogMap 2.0 is not only more scalable and robust than its predecessor, but it also provides the necessary infrastructure for domain experts to interactively contribute to the matching process.

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Journal Article•DOI•

Ontology matching

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Lorena Otero-Cerdeira¹, Francisco Javier Rodríguez-Martínez¹, Alma Gómez-Rodríguez¹•Institutions (1)

University of Vigo¹

01 Feb 2015-Expert Systems With Applications

TL;DR: A literature review regarding articles on ontology matching published in the last decade serves the purpose of offering an up-to-date review of the field and showing its evolution trends.

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Abstract: We present a literature review regarding articles on ontology matching published in the last decade.It serves the purpose of offering an up-to-date review of the field and showing its evolution trends.Over 1600 papers have been sorted according to a classification framework that we have defined.This framework helps in identifying the distribution of the load work in the last decade.Practitioners have been consulted to contrast and validate the results of the review. The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines.To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners.

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352 citations

Book Chapter•DOI•

The AgreementMakerLight Ontology Matching System

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Daniel Faria¹, Catia Pesquita¹, Emanuel Santos¹, Matteo Palmonari², Isabel F. Cruz³, Francisco M. Couto¹ - Show less +2 more•Institutions (3)

University of Lisbon¹, University of Milan², University of Illinois at Chicago³

09 Sep 2013-Lecture Notes in Computer Science

TL;DR: A new core framework, AgreementMakerLight, focused on computational efficiency and designed to handle very large ontologies, while preserving most of the flexibility and extensibility of the original AgreementMaker framework is developed.

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Abstract: AgreementMaker is one of the leading ontology matching systems, thanks to its combination of a flexible and extensible framework with a comprehensive user interface. In many domains, such as the biomedical, ontologies are becoming increasingly large thus presenting new challenges. We have developed a new core framework, AgreementMakerLight, focused on computational efficiency and designed to handle very large ontologies, while preserving most of the flexibility and extensibility of the original AgreementMaker framework. We evaluated the efficiency of AgreementMakerLight in two OAEI tracks: Anatomy and Large Biomedical Ontologies, obtaining excellent run time results. In addition, for the Anatomy track, AgreementMakerLight is now the best system as measured in terms of F-measure. Also in terms of F-measure, AgreementMakerLight is competitive with the best OAEI performers in two of the three tasks of the Large Biomedical Ontologies track that match whole ontologies.

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231 citations

Cites background from "LogMap 2.0: towards logic-based, sc..."

...As a consequence the more recent ontology matching systems incorporate more elaborate approaches including scaling strategies [14,15,18], ontology repair techniques to ensure the coherence of the alignments [15], and the use of external resources and ontologies to increase the amount of available knowledge to support matching [14]....
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...Recently, ontology matching systems have begun to include approaches to ensure the ontological quality of their outputs, such as the application of rules to prune out illogical mappings [14] or the use of full-fledged repair approaches that strive to ensure the coherence of the final alignment, that is, that all classes are satisfiable [15]....
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Proceedings Article•DOI•

Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery

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Raul Fernandez¹, Essam Mansour², Abdulhakim Qahtan², Ahmed K. Elmagarmid², Ihab F. Ilyas³, Samuel Madden¹, Mourad Ouzzani², Michael Stonebraker¹, Nan Tang² - Show less +5 more•Institutions (3)

Massachusetts Institute of Technology¹, Qatar Computing Research Institute², University of Waterloo³

01 Apr 2018

TL;DR: This work proposes SEMPROP, a DAG of different components that find links based on syntactic and semantic similarities, and introduces coherent group, a technique to combine word embeddings that works better than other state of the art combination alternatives.

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Abstract: Employees that spend more time finding relevant data than analyzing it suffer from a data discovery problem. The large volume of data in enterprises, and sometimes the lack of knowledge of the schemas aggravates this problem. Similar to how we navigate the Web, we propose to identify semantic links that assist analysts in their discovery tasks. These links relate tables to each other, to facilitate navigating the schemas. They also relate data to external data sources, such as ontologies and dictionaries, to help explain the schema meaning. We materialize the links in an enterprise knowledge graph, where they become available to analysts. The main challenge is how to find pairs of objects that are semantically related. We propose SEMPROP, a DAG of different components that find links based on syntactic and semantic similarities. SEMPROP is commanded by a semantic matcher which leverages word embeddings to find objects that are semantically related. We introduce coherent group, a technique to combine word embeddings that works better than other state of the art combination alternatives. We implement SEMPROP as part of Aurum, a data discovery system we are building, and conduct user studies, real deployments and a quantitative evaluation to understand the benefits of links for data discovery tasks, as well as the benefits of SEMPROP and coherent groups to find those links.

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95 citations

Cites methods from "LogMap 2.0: towards logic-based, sc..."

...Tools such as CODI [41] and LogMap [42] were developed for this purpose....
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Journal Article•DOI•

Overview of YAM++ - (not) Yet Another Matcher for ontology alignment task

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Duy Hoa Ngo, Zohra Bellahsene

01 Dec 2016-Journal of Web Semantics

TL;DR: YAM++ is described, an ontology matching tool aimed at solving issues of scalability, efficiency, and configuration tuning, and was one of the best ontological matching systems in terms of F -measure.

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47 citations

Cites background or methods from "LogMap 2.0: towards logic-based, sc..."

...owl—66,724 classes) have in common only 2898 aligned pairs [80], which is much smaller than the size of the both ontologies....
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...LogMapBK [80] removes the inconsistency by using existing specialized background knowledge such as based on the UMLS lexicon....
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Journal Article•DOI•

Tackling the challenges of matching biomedical ontologies

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Daniel Faria¹, Catia Pesquita², Isabela Mott², Catarina Martins³, Francisco M. Couto², Isabel F. Cruz⁴ - Show less +2 more•Institutions (4)

Instituto Gulbenkian de Ciência¹, University of Lisbon², University of Manchester³, University of Illinois at Chicago⁴

15 Jan 2018-Journal of Biomedical Semantics

TL;DR: It is demonstrated that the linear complexity of the hash-based searching strategy implemented by most state-of-the-art ontology matching systems is essential for matching large biomedical ontologies efficiently, and that translating traditional matching algorithms to the hash -based searching paradigm will be a critical direction for the future development of the field.

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Abstract: Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Ontology Matching Evaluation Initiative (OAEI) have spurred the development of matching systems able to tackle these challenges, and benchmarked their general performance. In this study, we dissect the strategies employed by matching systems to tackle the challenges of matching biomedical ontologies and gauge the impact of the challenges themselves on matching performance, using the AgreementMakerLight (AML) system as the platform for this study. We demonstrate that the linear complexity of the hash-based searching strategy implemented by most state-of-the-art ontology matching systems is essential for matching large biomedical ontologies efficiently. We show that accounting for all lexical annotations (e.g., labels and synonyms) in biomedical ontologies leads to a substantial improvement in F-measure over using only the primary name, and that accounting for the reliability of different types of annotations generally also leads to a marked improvement. Finally, we show that cross-references are a reliable source of information and that, when using biomedical ontologies as background knowledge, it is generally more reliable to use them as mediators than to perform lexical expansion. We anticipate that translating traditional matching algorithms to the hash-based searching paradigm will be a critical direction for the future development of the field. Improving the evaluation carried out in the biomedical tracks of the OAEI will also be important, as without proper reference alignments there is only so much that can be ascertained about matching systems or strategies. Nevertheless, it is clear that, to tackle the various challenges posed by biomedical ontologies, ontology matching systems must be able to efficiently combine multiple strategies into a mature matching approach.

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41 citations

Cites background or methods from "LogMap 2.0: towards logic-based, sc..."

...Thus, a few matching systems such as FCA-Map [16] and LogMap [13] opt for the domain specific UMLS SPECIALIST Lexicon [17]....
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...In 2016, only AML and LogMap [13] did so out of 8 independent participants [14]....
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...LogMap [13] +++ WN; UMLS Logic Bio; Auto; M/E all...
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...To enable modularization and reduce the complexity of the repair problem, repair algorithms tend to consider simplifications of the Description Logic of OWL—for instance, the repair algorithms of both AML and LogMap are based on propositional logic [13, 23]....
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...Most systems that use background knowledge employ fixed manually selected sources, with only AML, GOMMA [9] and the LogMapBio variant [24] implementing an automatic selection algorithm....
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Results of the Ontology Alignment Evaluation Initiative

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Jérôme Euzenat, Malgorzata Mochol, Pavel Shvaiko, Heiner Stuckenschmidt, Ondřej Šváb, Vojtěch Svátek, W.R. van Hage, Mikalai Yatskevich - Show less +4 more

01 Jan 2007

635 citations

"LogMap 2.0: towards logic-based, sc..." refers background in this paper

...For example, the input ontologies in the largest test case of the OAEI 2011 initiative contain 2000-3000 classes, and only 6 out of 16 tools were able to process these ontologies [2]....
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Book Chapter•DOI•

LogMap: logic-based and scalable ontology matching

[...]

Ernesto Jiménez-Ruiz¹, Bernardo Cuenca Grau¹•Institutions (1)

University of Oxford¹

23 Oct 2011

TL;DR: This paper presents LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities, and is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds of thousands of classes).

...read moreread less

Abstract: In this paper, we present LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for 'on the fly' unsatisfiability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMED CT confirm that our system can efficiently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a 'clean' set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap's output mappings with the input ontologies is consistent and does not contain unsatisfiable classes.

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473 citations

"LogMap 2.0: towards logic-based, sc..." refers background or methods or result in this paper

...Then, it constructs an ‘inverted’ lexical index for each of these ontologies (see [5] for details), which will be exploited to efficiently compute an initial set of candidate mappings....
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...The classified ontologies together with the mappings in M are indexed using an interval labelling schema (see [5] for details)....
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...838, which improves our previous results over FMA and NCI in terms of precision and F-measure (see [5])....
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...0 efficiently computes a set of initial candidate mappings by intersecting the inverted indices of O′ 1 and O′ 2 (see [5] for details)....
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...The LogMap tool(1) successfully addresses the first two aforementioned challenges [5, 7]....
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Journal Article•DOI•

Ontology matching with semantic verification

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Yves R. Jean-Mary, E. Patrick Shironoshita, Mansur R. Kabuka¹•Institutions (1)

University of Miami¹

01 Sep 2009-Journal of Web Semantics

TL;DR: Experimental results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.

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415 citations

"LogMap 2.0: towards logic-based, sc..." refers background in this paper

..., [4, 10, 9]), reasoning is known to severely aggravate the scalability problem....
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Book Chapter•DOI•

Ten Challenges for Ontology Matching

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Pavel Shvaiko, Jérôme Euzenat¹•Institutions (1)

French Institute for Research in Computer Science and Automation¹

09 Nov 2008

TL;DR: The basics of ontology matching are provided with the help of examples and general trends of the field are presented, thereby aiming to direct research into the critical path and to facilitate progress in the field.

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Abstract: This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough to be the basis for future development, and which is usable by non expert users. In this paper we first provide the basics of ontology matching with the help of examples. Then, we present general trends of the field and discuss ten challenges for ontology matching, thereby aiming to direct research into the critical path and to facilitate progress of the field.

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337 citations

"LogMap 2.0: towards logic-based, sc..." refers background in this paper

...Despite the impressive state of the art, large-scale biomedical ontologies still pose serious challenges to existing ontology matching tools [11, 3]....
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Book Chapter•DOI•

Ontology alignment evaluation initiative: six years of experience

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Jérôme Euzenat¹, Christian Meilicke², Heiner Stuckenschmidt², Pavel Shvaiko, Cassia Trojahn¹ - Show less +1 more•Institutions (2)

French Institute for Research in Computer Science and Automation¹, University of Mannheim²

01 Jan 2011-Journal on Data Semantics

TL;DR: This paper reports results and lessons learned from the Ontology Alignment Evaluation Initiative (OAEI), a benchmarking initiative for ontology matching, and describes the evaluation design used in the OAEI campaigns in terms of datasets, evaluation criteria and workflows.

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Abstract: In the area of semantic technologies, benchmarking and systematic evaluation is not yet as established as in other areas of computer science, e.g., information retrieval. In spite of successful attempts, more effort and experience are required in order to achieve such a level of maturity. In this paper, we report results and lessons learned from the Ontology Alignment Evaluation Initiative (OAEI), a benchmarking initiative for ontology matching. The goal of this work is twofold: on the one hand, we document the state of the art in evaluating ontology matching methods and provide potential participants of the initiative with a better understanding of the design and the underlying principles of the OAEI campaigns. On the other hand, we report experiences gained in this particular area of semantic technologies to potential developers of benchmarking for other kinds of systems. For this purpose, we describe the evaluation design used in the OAEI campaigns in terms of datasets, evaluation criteria and workflows, provide a global view on the results of the campaigns carried out from 2005 to 2010 and discuss upcoming trends, both specific to ontology matching and generally relevant for the evaluation of semantic technologies. Finally, we argue that there is a need for a further automation of benchmarking to shorten the feedback cycle for tool developers.

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290 citations

"LogMap 2.0: towards logic-based, sc..." refers background in this paper

...Despite the impressive state of the art, large-scale biomedical ontologies still pose serious challenges to existing ontology matching tools [11, 3]....
[...]