Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.Comment: Journal Pape

USING LINGUISTIC ANALYSIS TO TRANSLATE ARABIC NATURAL LANGUAGE QUERIES TO SPARQL

ABSTRAC

Semantic Interpretation of User Queries for Question Answering on Interlinked Data

The Web of Data contains a wealth of knowledge belonging to a large number of domains. Retrieving data from such precious interlinked knowledge bases is an issue. By taking the structure of data into account, it is expected that upcoming generation of search engines is approaching to question answering systems, which directly answer user questions. But developing a question answering over these interlinked data sources is still challenging because of two inherent characteristics: First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between these datasets on both the schema and instance levels. In this respect, several challenges such as resource disambiguation, vocabulary mismatch, inference, link traversal are raised. In this dissertation, we address these challenges in order to build a question answering system for Linked Data. We present our question answering system Sina, which transforms user-supplied queries (i.e. either natural language queries or keyword queries) into conjunctive SPARQL queries over a set of interlinked data sources. The contributions of this work are as follows: 1. A novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation approach). We employed a Hidden Markov Model, whose parameters were bootstrapped with different distribution functions. 2. A novel method for constructing federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph, which ultimately renders a corresponding SPARQL query. 3. Regarding the problem of vocabulary mismatch, our contribution is divided into two parts, First, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Data. We evaluate the effectiveness of each feature individually as well as their combinations, employing Support Vector Machines and Decision Trees. Second, we propose a novel method for automatic query expansion, which employs a Hidden Markov Model to obtain the optimal tuples of derived words. 4. We provide two benchmarks for two different tasks to the community of question answering systems. The first one is used for the task of question answering on interlinked datasets (i.e. federated queries over Linked Data). The second one is used for the vocabulary mismatch task. We evaluate the accuracy of our approach using measures like mean reciprocal rank, precision, recall, and F-measure on three interlinked life-science datasets as well as DBpedia. The results of our accuracy evaluation demonstrate the effectiveness of our approach. Moreover, we study the runtime of our approach in its sequential as well as parallel implementations and draw conclusions on the scalability of our approach on Linked Data

Keyword-Driven SPARQL Query Generation Leveraging Background Knowledge

The search for information on the Web of Data is becoming increasingly difficult due to its dramatic growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal queries (e. g. SPARQL queries) to retrieve information from Linked Data sources. So as to simplify and automate the querying and retrieval of information from such sources, we present in this paper a novel approach for constructing SPARQL queries based on user-supplied keywords. Our approach utilizes a set of predefined basic graph pattern templates for generating adequate interpretations of user queries. This is achieved by obtaining ranked lists of candidate resource identifiers for the supplied keywords and then injecting these identifiers into suitable positions in the graph pattern templates. The main advantages of our approach are that it is completely agnostic of the underlying knowledge base and ontology schema, that it scales to large knowledge bases and is simple to use. We evaluate 17 possible valid graph pattern templates by measuring their precision and recall on 53 queries against DBpedia. Our results show that 8 of these basic graph pattern templates return results with a precision above 70%. Our approach is implemented as a Web search interface and performs sufficiently fast to return instant answers to the user even with large knowledge bases

User Interfaces to the Web of Data based on Natural Language Generation

We explore how Virtual Research Environments based on Semantic Web technologies support research interactions with RDF data in various stages of corpus-based analysis, analyze the Web of Data in terms of human readability, derive labels from variables in SPARQL queries, apply Natural Language Generation to improve user interfaces to the Web of Data by verbalizing SPARQL queries and RDF graphs, and present a method to automatically induce RDF graph verbalization templates via distant supervision

Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data

This thesis is a compendium of scientific works and engineering specifications that have been contributed to a large community of stakeholders to be copied, adapted, mixed, built upon and exploited in any way possible to achieve a common goal: Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data The explosion of information technology in the last two decades has led to a substantial growth in quantity, diversity and complexity of web-accessible linguistic data. These resources become even more useful when linked with each other and the last few years have seen the emergence of numerous approaches in various disciplines concerned with linguistic resources and NLP tools. It is the challenge of our time to store, interlink and exploit this wealth of data accumulated in more than half a century of computational linguistics, of empirical, corpus-based study of language, and of computational lexicography in all its heterogeneity. The vision of the Giant Global Graph (GGG) was conceived by Tim Berners-Lee aiming at connecting all data on the Web and allowing to discover new relations between this openly-accessible data. This vision has been pursued by the Linked Open Data (LOD) community, where the cloud of published datasets comprises 295 data repositories and more than 30 billion RDF triples (as of September 2011). RDF is based on globally unique and accessible URIs and it was specifically designed to establish links between such URIs (or resources). This is captured in the Linked Data paradigm that postulates four rules: (1) Referred entities should be designated by URIs, (2) these URIs should be resolvable over HTTP, (3) data should be represented by means of standards such as RDF, (4) and a resource should include links to other resources. Although it is difficult to precisely identify the reasons for the success of the LOD effort, advocates generally argue that open licenses as well as open access are key enablers for the growth of such a network as they provide a strong incentive for collaboration and contribution by third parties. In his keynote at BNCOD 2011, Chris Bizer argued that with RDF the overall data integration effort can be “split between data publishers, third parties, and the data consumer”, a claim that can be substantiated by observing the evolution of many large data sets constituting the LOD cloud. As written in the acknowledgement section, parts of this thesis has received numerous feedback from other scientists, practitioners and industry in many different ways. The main contributions of this thesis are summarized here: Part I – Introduction and Background. During his keynote at the Language Resource and Evaluation Conference in 2012, Sören Auer stressed the decentralized, collaborative, interlinked and interoperable nature of the Web of Data. The keynote provides strong evidence that Semantic Web technologies such as Linked Data are on its way to become main stream for the representation of language resources. The jointly written companion publication for the keynote was later extended as a book chapter in The People’s Web Meets NLP and serves as the basis for “Introduction” and “Background”, outlining some stages of the Linked Data publication and refinement chain. Both chapters stress the importance of open licenses and open access as an enabler for collaboration, the ability to interlink data on the Web as a key feature of RDF as well as provide a discussion about scalability issues and decentralization. Furthermore, we elaborate on how conceptual interoperability can be achieved by (1) re-using vocabularies, (2) agile ontology development, (3) meetings to refine and adapt ontologies and (4) tool support to enrich ontologies and match schemata. Part II - Language Resources as Linked Data. “Linked Data in Linguistics” and “NLP & DBpedia, an Upward Knowledge Acquisition Spiral” summarize the results of the Linked Data in Linguistics (LDL) Workshop in 2012 and the NLP & DBpedia Workshop in 2013 and give a preview of the MLOD special issue. In total, five proceedings – three published at CEUR (OKCon 2011, WoLE 2012, NLP & DBpedia 2013), one Springer book (Linked Data in Linguistics, LDL 2012) and one journal special issue (Multilingual Linked Open Data, MLOD to appear) – have been (co-)edited to create incentives for scientists to convert and publish Linked Data and thus to contribute open and/or linguistic data to the LOD cloud. Based on the disseminated call for papers, 152 authors contributed one or more accepted submissions to our venues and 120 reviewers were involved in peer-reviewing. “DBpedia as a Multilingual Language Resource” and “Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Linked Data Cloud” contain this thesis’ contribution to the DBpedia Project in order to further increase the size and inter-linkage of the LOD Cloud with lexical-semantic resources. Our contribution comprises extracted data from Wiktionary (an online, collaborative dictionary similar to Wikipedia) in more than four languages (now six) as well as language-specific versions of DBpedia, including a quality assessment of inter-language links between Wikipedia editions and internationalized content negotiation rules for Linked Data. In particular the work described in created the foundation for a DBpedia Internationalisation Committee with members from over 15 different languages with the common goal to push DBpedia as a free and open multilingual language resource. Part III - The NLP Interchange Format (NIF). “NIF 2.0 Core Specification”, “NIF 2.0 Resources and Architecture” and “Evaluation and Related Work” constitute one of the main contribution of this thesis. The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. The core specification is included in and describes which URI schemes and RDF vocabularies must be used for (parts of) natural language texts and annotations in order to create an RDF/OWL-based interoperability layer with NIF built upon Unicode Code Points in Normal Form C. In , classes and properties of the NIF Core Ontology are described to formally define the relations between text, substrings and their URI schemes. contains the evaluation of NIF. In a questionnaire, we asked questions to 13 developers using NIF. UIMA, GATE and Stanbol are extensible NLP frameworks and NIF was not yet able to provide off-the-shelf NLP domain ontologies for all possible domains, but only for the plugins used in this study. After inspecting the software, the developers agreed however that NIF is adequate enough to provide a generic RDF output based on NIF using literal objects for annotations. All developers were able to map the internal data structure to NIF URIs to serialize RDF output (Adequacy). The development effort in hours (ranging between 3 and 40 hours) as well as the number of code lines (ranging between 110 and 445) suggest, that the implementation of NIF wrappers is easy and fast for an average developer. Furthermore the evaluation contains a comparison to other formats and an evaluation of the available URI schemes for web annotation. In order to collect input from the wide group of stakeholders, a total of 16 presentations were given with extensive discussions and feedback, which has lead to a constant improvement of NIF from 2010 until 2013. After the release of NIF (Version 1.0) in November 2011, a total of 32 vocabulary employments and implementations for different NLP tools and converters were reported (8 by the (co-)authors, including Wiki-link corpus, 13 by people participating in our survey and 11 more, of which we have heard). Several roll-out meetings and tutorials were held (e.g. in Leipzig and Prague in 2013) and are planned (e.g. at LREC 2014). Part IV - The NLP Interchange Format in Use. “Use Cases and Applications for NIF” and “Publication of Corpora using NIF” describe 8 concrete instances where NIF has been successfully used. One major contribution in is the usage of NIF as the recommended RDF mapping in the Internationalization Tag Set (ITS) 2.0 W3C standard and the conversion algorithms from ITS to NIF and back. One outcome of the discussions in the standardization meetings and telephone conferences for ITS 2.0 resulted in the conclusion there was no alternative RDF format or vocabulary other than NIF with the required features to fulfill the working group charter. Five further uses of NIF are described for the Ontology of Linguistic Annotations (OLiA), the RDFaCE tool, the Tiger Corpus Navigator, the OntosFeeder and visualisations of NIF using the RelFinder tool. These 8 instances provide an implemented proof-of-concept of the features of NIF. starts with describing the conversion and hosting of the huge Google Wikilinks corpus with 40 million annotations for 3 million web sites. The resulting RDF dump contains 477 million triples in a 5.6 GB compressed dump file in turtle syntax. describes how NIF can be used to publish extracted facts from news feeds in the RDFLiveNews tool as Linked Data. Part V - Conclusions. provides lessons learned for NIF, conclusions and an outlook on future work. Most of the contributions are already summarized above. One particular aspect worth mentioning is the increasing number of NIF-formated corpora for Named Entity Recognition (NER) that have come into existence after the publication of the main NIF paper Integrating NLP using Linked Data at ISWC 2013. These include the corpora converted by Steinmetz, Knuth and Sack for the NLP & DBpedia workshop and an OpenNLP-based CoNLL converter by Brümmer. Furthermore, we are aware of three LREC 2014 submissions that leverage NIF: NIF4OGGD - NLP Interchange Format for Open German Governmental Data, N^3 – A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format and Global Intelligent Content: Active Curation of Language Resources using Linked Data as well as an early implementation of a GATE-based NER/NEL evaluation framework by Dojchinovski and Kliegr. Further funding for the maintenance, interlinking and publication of Linguistic Linked Data as well as support and improvements of NIF is available via the expiring LOD2 EU project, as well as the CSA EU project called LIDER, which started in November 2013. Based on the evidence of successful adoption presented in this thesis, we can expect a decent to high chance of reaching critical mass of Linked Data technology as well as the NIF standard in the field of Natural Language Processing and Language Resources.:CONTENTS i introduction and background 1 1 introduction 3 1.1 Natural Language Processing . . . . . . . . . . . . . . . 3 1.2 Open licenses, open access and collaboration . . . . . . 5 1.3 Linked Data in Linguistics . . . . . . . . . . . . . . . . . 6 1.4 NLP for and by the Semantic Web – the NLP Inter- change Format (NIF) . . . . . . . . . . . . . . . . . . . . 8 1.5 Requirements for NLP Integration . . . . . . . . . . . . 10 1.6 Overview and Contributions . . . . . . . . . . . . . . . 11 2 background 15 2.1 The Working Group on Open Data in Linguistics (OWLG) 15 2.1.1 The Open Knowledge Foundation . . . . . . . . 15 2.1.2 Goals of the Open Linguistics Working Group . 16 2.1.3 Open linguistics resources, problems and chal- lenges . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.4 Recent activities and on-going developments . . 18 2.2 Technological Background . . . . . . . . . . . . . . . . . 18 2.3 RDF as a data model . . . . . . . . . . . . . . . . . . . . 21 2.4 Performance and scalability . . . . . . . . . . . . . . . . 22 2.5 Conceptual interoperability . . . . . . . . . . . . . . . . 22 ii language resources as linked data 25 3 linked data in linguistics 27 3.1 Lexical Resources . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Linguistic Corpora . . . . . . . . . . . . . . . . . . . . . 30 3.3 Linguistic Knowledgebases . . . . . . . . . . . . . . . . 31 3.4 Towards a Linguistic Linked Open Data Cloud . . . . . 32 3.5 State of the Linguistic Linked Open Data Cloud in 2012 33 3.6 Querying linked resources in the LLOD . . . . . . . . . 36 3.6.1 Enriching metadata repositories with linguistic features (Glottolog → OLiA) . . . . . . . . . . . 36 3.6.2 Enriching lexical-semantic resources with lin- guistic information (DBpedia (→ POWLA) → OLiA) . . . . . . . . . . . . . . . . . . . . . . . . 38 4 DBpedia as a multilingual language resource: the case of the greek dbpedia edition. 39 4.1 Current state of the internationalization effort . . . . . 40 4.2 Language-specific design of DBpedia resource identifiers 41 4.3 Inter-DBpedia linking . . . . . . . . . . . . . . . . . . . 42 4.4 Outlook on DBpedia Internationalization . . . . . . . . 44 5 leveraging the crowdsourcing of lexical resources for bootstrapping a linguistic linked data cloud 47 5.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2 Problem Description . . . . . . . . . . . . . . . . . . . . 50 5.2.1 Processing Wiki Syntax . . . . . . . . . . . . . . 50 5.2.2 Wiktionary . . . . . . . . . . . . . . . . . . . . . . 52 5.2.3 Wiki-scale Data Extraction . . . . . . . . . . . . . 53 5.3 Design and Implementation . . . . . . . . . . . . . . . . 54 5.3.1 Extraction Templates . . . . . . . . . . . . . . . . 56 5.3.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . 56 5.3.3 Language Mapping . . . . . . . . . . . . . . . . . 58 5.3.4 Schema Mediation by Annotation with lemon . 58 5.4 Resulting Data . . . . . . . . . . . . . . . . . . . . . . . . 58 5.5 Lessons Learned . . . . . . . . . . . . . . . . . . . . . . . 60 5.6 Discussion and Future Work . . . . . . . . . . . . . . . 60 5.6.1 Next Steps . . . . . . . . . . . . . . . . . . . . . . 61 5.6.2 Open Research Questions . . . . . . . . . . . . . 61 6 nlp & dbpedia, an upward knowledge acquisition spiral 63 6.1 Knowledge acquisition and structuring . . . . . . . . . 64 6.2 Representation of knowledge . . . . . . . . . . . . . . . 65 6.3 NLP tasks and applications . . . . . . . . . . . . . . . . 65 6.3.1 Named Entity Recognition . . . . . . . . . . . . 66 6.3.2 Relation extraction . . . . . . . . . . . . . . . . . 67 6.3.3 Question Answering over Linked Data . . . . . 67 6.4 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4.1 Gold and silver standards . . . . . . . . . . . . . 69 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 iii the nlp interchange format (nif) 73 7 nif 2.0 core specification 75 7.1 Conformance checklist . . . . . . . . . . . . . . . . . . . 75 7.2 Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.2.1 Definition of Strings . . . . . . . . . . . . . . . . 78 7.2.2 Representation of Document Content with the nif:Context Class . . . . . . . . . . . . . . . . . . 80 7.3 Extension of NIF . . . . . . . . . . . . . . . . . . . . . . 82 7.3.1 Part of Speech Tagging with OLiA . . . . . . . . 83 7.3.2 Named Entity Recognition with ITS 2.0, DBpe- dia and NERD . . . . . . . . . . . . . . . . . . . 84 7.3.3 lemon and Wiktionary2RDF . . . . . . . . . . . 86 8 nif 2.0 resources and architecture 89 8.1 NIF Core Ontology . . . . . . . . . . . . . . . . . . . . . 89 8.1.1 Logical Modules . . . . . . . . . . . . . . . . . . 90 8.2 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . 91 8.2.1 Access via REST Services . . . . . . . . . . . . . 92 8.2.2 NIF Combinator Demo . . . . . . . . . . . . . . 92 8.3 Granularity Profiles . . . . . . . . . . . . . . . . . . . . . 93 8.4 Further URI Schemes for NIF . . . . . . . . . . . . . . . 95 8.4.1 Context-Hash-based URIs . . . . . . . . . . . . . 99 9 evaluation and related work 101 9.1 Questionnaire and Developers Study for NIF 1.0 . . . . 101 9.2 Qualitative Comparison with other Frameworks and Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 9.3 URI Stability Evaluation . . . . . . . . . . . . . . . . . . 103 9.4 Related URI Schemes . . . . . . . . . . . . . . . . . . . . 104 iv the nlp interchange format in use 109 10 use cases and applications for nif 111 10.1 Internationalization Tag Set 2.0 . . . . . . . . . . . . . . 111 10.1.1 ITS2NIF and NIF2ITS conversion . . . . . . . . . 112 10.2 OLiA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 10.3 RDFaCE . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 10.4 Tiger Corpus Navigator . . . . . . . . . . . . . . . . . . 121 10.4.1 Tools and Resources . . . . . . . . . . . . . . . . 122 10.4.2 NLP2RDF in 2010 . . . . . . . . . . . . . . . . . . 123 10.4.3 Linguistic Ontologies . . . . . . . . . . . . . . . . 124 10.4.4 Implementation . . . . . . . . . . . . . . . . . . . 125 10.4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . 126 10.4.6 Related Work and Outlook . . . . . . . . . . . . 129 10.5 OntosFeeder – a Versatile Semantic Context Provider for Web Content Authoring . . . . . . . . . . . . . . . . 131 10.5.1 Feature Description and User Interface Walk- through . . . . . . . . . . . . . . . . . . . . . . . 132 10.5.2 Architecture . . . . . . . . . . . . . . . . . . . . . 134 10.5.3 Embedding Metadata . . . . . . . . . . . . . . . 135 10.5.4 Related Work and Summary . . . . . . . . . . . 135 10.6 RelFinder: Revealing Relationships in RDF Knowledge Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 10.6.1 Implementation . . . . . . . . . . . . . . . . . . . 137 10.6.2 Disambiguation . . . . . . . . . . . . . . . . . . . 138 10.6.3 Searching for Relationships . . . . . . . . . . . . 139 10.6.4 Graph Visualization . . . . . . . . . . . . . . . . 140 10.6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . 141 11 publication of corpora using nif 143 11.1 Wikilinks Corpus . . . . . . . . . . . . . . . . . . . . . . 143 11.1.1 Description of the corpus . . . . . . . . . . . . . 143 11.1.2 Quantitative Analysis with Google Wikilinks Cor- pus . . . . . . . . . . . . . . . . . . . . . . . . . . 144 11.2 RDFLiveNews . . . . . . . . . . . . . . . . . . . . . . . . 144 11.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . 145 11.2.2 Mapping to RDF and Publication on the Web of Data . . . . . . . . . . . . . . . . . . . . . . . . . 146 v conclusions 149 12 lessons learned, conclusions and future work 151 12.1 Lessons Learned for NIF . . . . . . . . . . . . . . . . . . 151 12.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 151 12.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 15

Yavaa: supporting data workflows from discovery to visualization

Recent years have witness an increasing number of data silos being opened up both within organizations and to the general public: Scientists publish their raw data as supplements to articles or even standalone artifacts to enable others to verify and extend their work. Governments pass laws to open up formerly protected data treasures to improve accountability and transparency as well as to enable new business ideas based on this public good. Even companies share structured information about their products and services to advertise their use and thus increase revenue. Exploiting this wealth of information holds many challenges for users, though. Oftentimes data is provided as tables whose sheer endless rows of daunting numbers are barely accessible. InfoVis can mitigate this gap. However, offered visualization options are generally very limited and next to no support is given in applying any of them. The same holds true for data wrangling. Only very few options to adjust the data to the current needs and barely any protection are in place to prevent even the most obvious mistakes. When it comes to data from multiple providers, the situation gets even bleaker. Only recently tools emerged to search for datasets across institutional borders reasonably. Easy-to-use ways to combine these datasets are still missing, though. Finally, results generally lack proper documentation of their provenance. So even the most compelling visualizations can be called into question when their coming about remains unclear. The foundations for a vivid exchange and exploitation of open data are set, but the barrier of entry remains relatively high, especially for non-expert users. This thesis aims to lower that barrier by providing tools and assistance, reducing the amount of prior experience and skills required. It covers the whole workflow ranging from identifying proper datasets, over possible transformations, up until the export of the result in the form of suitable visualizations