Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Ten Ways of Leveraging Ontologies for Rapid Natural Language Processing Customization for Multiple Use Cases in Disjoint Domains

With the ever-growing adoption of AI technologies by large enterprises, purely data-driven approaches have dominated the field in the recent years. For a single use case, a development process looks simple: agreeing on an annotation schema, labeling the data, and training the models. As the number of use cases and their complexity increases, the development teams face issues with collective governance of the models, scalability and reusablity of data and models. These issues are widely addressed on the engineering side, but not so much on the knowledge side. Ontologies have been a well-researched approach for capturing knowledge and can be used to augment a data-driven methodology. In this paper, we discuss 10 ways of leveraging ontologies for Natural Language Processing (NLP) and its applications. We use ontologies for rapid customization of a NLP pipeline, ontologyrelated standards to power a rule engine and provide standard output format. We also discuss various use cases for medical, enterprise, financial, legal, and security domains, centered around three NLP-based applications: semantic search, question answering and natural language querying

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

European Language Grid

This open access book provides an in-depth description of the EU project European Language Grid (ELG). Its motivation lies in the fact that Europe is a multilingual society with 24 official European Union Member State languages and dozens of additional languages including regional and minority languages. The only meaningful way to enable multilingualism and to benefit from this rich linguistic heritage is through Language Technologies (LT) including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Technologies and language-centric Artificial Intelligence (AI) applications. The European Language Grid provides a single umbrella platform for the European LT community, including research and industry, effectively functioning as a virtual home, marketplace, showroom, and deployment centre for all services, tools, resources, products and organisations active in the field. Today the ELG cloud platform already offers access to more than 13,000 language processing tools and language resources. It enables all stakeholders to deposit, upload and deploy their technologies and datasets. The platform also supports the long-term objective of establishing digital language equality in Europe by 2030 – to create a situation in which all European languages enjoy equal technological support. This is the very first book dedicated to Language Technology and NLP platforms. Cloud technology has only recently matured enough to make the development of a platform like ELG feasible on a larger scale. The book comprehensively describes the results of the ELG project. Following an introduction, the content is divided into four main parts: (I) ELG Cloud Platform; (II) ELG Inventory of Technologies and Resources; (III) ELG Community and Initiative; and (IV) ELG Open Calls and Pilot Projects

Clinical practice knowledge acquisition and interrogation using natural language: aquisição e interrogação de conhecimento de prática clínica usando linguagem natural

Os conceitos científicos, metodologias e ferramentas no sub-dominio da Representação de Conhecimento da área da Inteligência Artificial Aplicada têm sofrido avanços muito significativos nos anos recentes. A utilização de Ontologias como conceptualizações de domínios é agora suficientemente poderosa para aspirar ao raciocínio computacional sobre realidades complexas. Uma das tarefas científica e tecnicamente mais desafiante é prestação de cuidados pelos profissionais de saúde na especialidade cardiovascular. Um domínio de tal forma complexo pode beneficiar largamente da possibilidade de ajudas ao raciocínio clínico que estão neste momento a beira de ficarem disponíveis. Investigamos no sentido de desenvolver uma infraestrutura sólida e completa para a representação de conhecimento na prática clínica bem como os processes associados para adquirir o conhecimento a partir de textos clínicos e raciocinar automaticamente sobre esse conhecimento; ABSTRACT: The scientific concepts, methodologies and tools in the Knowledge Representation (KR) subdomain of applied Artificial Intelligence (AI) came a long way with enormous strides in recent years. The usage of domain conceptualizations that are Ontologies is now powerful enough to aim at computable reasoning over complex realities. One of the most challenging scientific and technical human endeavors is the daily Clinical Practice (CP) of Cardiovascular (C V) specialty healthcare providers. Such a complex domain can benefit largely from the possibility of clinical reasoning aids that are now at the edge of being available. We research into al complete end-to-end solid ontological infrastructure for CP knowledge representation as well as the associated processes to automatically acquire knowledge from clinical texts and reason over it

Aquisição e Interrogação de Conhecimento de Prática Clínica usando Linguagem Natural

The scientific concepts, methodologies and tools in the Knowledge Representation (KR) sub- domain of applied Artificial Intelligence (AI) came a long way with enormous strides in recent years. The usage of domain conceptualizations that are Ontologies is now powerful enough to aim at computable reasoning over complex realities. One of the most challenging scientific and technical human endeavors is the daily Clinical Prac- tice (CP) of Cardiovascular (CV) specialty healthcare providers. Such a complex domain can benefit largely from the possibility of clinical reasoning aids that are now at the edge of being available. We research into a complete end-to-end solid ontological infrastructure for CP knowledge represen- tation as well as the associated processes to automatically acquire knowledge from clinical texts and reason over it

European Language Grid

This open access book provides an in-depth description of the EU project European Language Grid (ELG). Its motivation lies in the fact that Europe is a multilingual society with 24 official European Union Member State languages and dozens of additional languages including regional and minority languages. The only meaningful way to enable multilingualism and to benefit from this rich linguistic heritage is through Language Technologies (LT) including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Technologies and language-centric Artificial Intelligence (AI) applications. The European Language Grid provides a single umbrella platform for the European LT community, including research and industry, effectively functioning as a virtual home, marketplace, showroom, and deployment centre for all services, tools, resources, products and organisations active in the field. Today the ELG cloud platform already offers access to more than 13,000 language processing tools and language resources. It enables all stakeholders to deposit, upload and deploy their technologies and datasets. The platform also supports the long-term objective of establishing digital language equality in Europe by 2030 – to create a situation in which all European languages enjoy equal technological support. This is the very first book dedicated to Language Technology and NLP platforms. Cloud technology has only recently matured enough to make the development of a platform like ELG feasible on a larger scale. The book comprehensively describes the results of the ELG project. Following an introduction, the content is divided into four main parts: (I) ELG Cloud Platform; (II) ELG Inventory of Technologies and Resources; (III) ELG Community and Initiative; and (IV) ELG Open Calls and Pilot Projects

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models