Theophrastus: On demand and real-time automatic annotation and exploration of (web) documents using open linked data

Fafalios, Pavlos; Papadakos, Panagiotis

doi:10.1016/j.websem.2014.07.009

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…Entity Linking is also considered a way of Named Entity Disambiguation (NED), since a resource (e.g., a URI or a Wikipedia page) can determine the identity of an entity. NEE is useful in several tasks, e.g., in question answering [153], for annotating (Web) documents [91,137], as well as in the context of our approach, i.e., in semantic analysis of search results [83,88]. We should also stress that the importance of NEE, especially for the Semantic Web, is justified by the fact that the Semantic Web realization highly depends on the availability of metadata (structured content in general) describing Web content, defined through a formal semantic structure.…”

Section: Named Entity Extractionmentioning

confidence: 99%

Exploiting Linked Data in Exploratory Search

Fafalios

2017

SIGIR Forum

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In recent years we have witnessed an explosion in publishing data on the Web, mostly in the form of Linked Data. An important question is how typical users, who mainly use keyword search queries, can access and exploit this constantly increasing body of knowledge. Although existing interaction paradigms in Semantic Search hide their complexity behind easy-to-use interfaces, they have not managed to cover common search needs. At the same time, according to several studies, a large number of search tasks are of exploratory nature. However, in such tasks the traditional "ranked list" approach for interacting with the retrieved results is often inadequate. The objective of this thesis is to enable effective exploratory search services which can bridge the gap between the classic responses of non-semantic search systems (e.g., Professional Search Systems, Web Search Engines) and semantic information expressed in the form of Linked Open Data (LOD). Towards this direction, we introduce an approach in which named entities (like names of persons, locations, chemical substances, etc.) are exploited as the glue for automatically connecting documents (search results) with data and knowledge. We study an approach where this entity-based integration is performed at real-time, without any human intervention and without the need of prebuilt indexes. This allows the provision of "fresh" information, the easy configuration of this functionality according to the needs of the underlying search application, as well as its easy exploitation by existing search systems. The provision of the aforementioned functionality is challenging. At first, the LOD that are available on the Web are big, are distributed in many knowledge bases, are increased and updated continuously, and also cover many domains. Consequently, there is the need of an interoperability model that will allow the specification of the entities of interest as well as of the related and useful semantic data. In addition, the number of extractable entities from the search results can be very high and the same is true for the amount of semantic information that can be retrieved from the LOD for these entities (i.e., the number of their attributes and of their associations with other entities). Thus, there is also the need of methods that can estimate the important (for the search context) entities, attributes and associations. To cope with the above challenges, this thesis proposes a semantic analysis process in which the search results are connected with data and knowledge at real-time without any human intervention. For describing the entities of interest, as well as the related (and useful for the application context) semantic information, we propose a generic model for configuring a Named Entity Extraction (NEE) system, while for specifying the semantics of this model, we introduce an RDF/S vocabulary, called "Open NEE Configuration Model", which allows a NEE system to describe (and publish as LOD) its entity-mining capabilities. To enable associating the result of a NEE process with an applied configuration, we propose an extension of the Open Annotation Data Model which also allows publishing the annotation results as LOD. To examine the feasibility of this model, we developed the system X-Link which, contrary to existing NEE systems, allows its easy configuration by exploiting one or more semantic Knowledge Bases. To identify the important semantic information related to the search results, we introduce and study a ranking method that is based on the Random Walk model and which exploits the extracted entities and their connectivity. The exploitation of the selected semantic information is achieved either through the visualization of the related semantic graph and/or in the context of a faceted interaction model that allows the user to gradually restrict the search space. Besides, this thesis studied the exploitation of such graphs for re-ranking the list of retrieved results aiming to promote relevant but low-ranked hits. The dissertation reports extensive evaluation results of the proposed functionalities and methods. Regarding the system X-Link, a task-based evaluation with users showed its ease of configuration, while a case study illustrated the efficiency of the supported operations. The comparative evaluation of the proposed probabilistic scheme for ranking entities and semantic data showed that the proposed approach is more effective compared to other ranking approaches (producing a more than 20% better ranking). Regarding the presentation of the important entities (and of their associations), the conducted survey in a marine-related search context demonstrated that the majority of participants (more than 70%) prefer to see a graph representation of entities related to the retrieved results regardless of the type of the submitted query. The evaluation of the proposed probabilistic algorithm for re-ranking the retrieved search results (using TREC datasets related to the medical domain) showed that this approach can notably improve the list of results by promoting relevant hits in higher positions. Finally, the implementation and the experimental results of the proposed search process demonstrated its feasibility and efficiency, and also enabled us to reveal its limitations.

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Section: Named Entity Extractionmentioning

confidence: 99%

Exploiting Linked Data in Exploratory Search

Fafalios

2017

SIGIR Forum

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“…Moreover, ANNIE contains a variety of natural language processing techniques to analyse information based on semantic rules, such as gazetteer, NE transducer, POS tagger, English Tokeniser, OrthoMatcher etc. [17].…”

Section: B Semantic Formalismmentioning

confidence: 99%

Ontology based semantic-predictive model for reconfigurable automation systems

Zhang

Ahmad

Vera

et al. 2016

2016 IEEE 14th International Conference on Industrial Informatics (INDIN)

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Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here is a working paper or pre-print that may be later published elsewhere. If a published version is known of, the above WRAP url will contain details on finding it. Abstract-Due to increasing product variety and complexity, capability to support reconfiguration is a key competitiveness indicator for current automation system within large enterprises. Reconfigurable manufacturing systems could efficiently reuse existing knowledge in order to decrease the required skills and design time to launch new products. However, most of the software tools developed to support design of reconfigurable manufacturing system lack integration of product, process and resource knowledge, and the design data is not transferred from domain-specific engineering tools to a collaborative and intelligent platform to capture and reuse design knowledge. The focus of this research study is to enable integrated automation systems design to support a knowledge reuse approach to predict process and resource changes when product requirements change. The proposed methodology is based on a robust semantic-predictive model supported by ontology representations and predictive algorithms for the integration of Product, Process, Resource and Requirement (PPRR) data, so that future automation system changes can be identified at early design stages.

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“…X-Link ranks the detected entities according to their frequency in the document as described in §3. 4. Note also that Gate Annie internally "cleans" the document contents by removing useless text (like the HTML tags of a Web page).…”

Section: Entity Mining and Disambiguationmentioning

confidence: 99%

“…4. In addition, it computes a subgraph of Graph(D, r), which is denoted by ConnectGraph(D, r), for making more evident how the entity URIs are associated.…”

Section: Entity Enrichmentmentioning

confidence: 99%

Exploiting Linked Data for Open and Configurable Named Entity Extraction

Fafalios

Baritakis

Tzitzikas

2015

Int. J. Artif. Intell. Tools

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Named Entity Extraction (NEE) is the process of identifying entities in texts and, very commonly, linking them to related (Web) resources. This task is useful in several applications, e.g. for question answering, annotating documents, post-processing of search results, etc. However, existing NEE tools lack an open or easy configuration although this is very important for building domain-specific applications. For example, supporting a new category of entities, or specifying how to link the detected entities with online resources, is either impossible or very laborious. In this paper, we show how we can exploit semantic information (Linked Data) at real-time for configuring (handily) a NEE system and we propose a generic model for configuring such services. To explicitly define the semantics of the proposed model, we introduce an RDF/S vocabulary, called "Open NEE Configuration Model", which allows a NEE service to describe (and publish as Linked Data) its entity mining capabilities, but also to be dynamically configured. To allow relating the output of a NEE process with an applied configuration, we propose an extension of the Open Annotation Data Model which also enables an application to run advanced queries over the annotated data. As a proof of concept, we present X-Link, a fully-configurable NEE framework that realizes this approach. Contrary to the existing tools, X-Link allows the user to easily define the categories of entities that are interesting for the application at hand by exploiting one or more semantic Knowledge Bases. The user is also able to update a category and specify how to semantically link and enrich the identified entities. This enhanced configurability allows X-Link to be easily configured for different contexts for building domain-specific applications. To test the approach, we conducted a task-based evaluation with users that demonstrates its usability, and a case study that demonstrates its feasibility.This task usually includes the Entity Linking process which tries to link the named entity with a resource (reference) in a Knowledge Base (KB). a Entity Linking is also considered a way of Named Entity Disambiguation (NED), since a resource (e.g. a URI or a Wikipedia page) can determine the identity of an entity. NEE is useful in several tasks, e.g. for question answering, 1 post-processing of search results, 2,3 annotating (Web) documents. 4,5 In addition, the importance of NEE, especially for the Semantic Web, is justified by the fact that the Semantic Web realization highly depends on the availability of metadata (structured content in general) describing Web content, defined through a formal semantic structure. Thus, a major challenge for the Semantic Web is the extraction of structured data through the development of automated NEE tools.There are already several tools that support NEE, e.g. DBpedia Spotlight, 6 AlchemyAPI 7 and OpenCalais. 8 However, these tools do not allow the user/developer to easily configure them, e.g. to define their own interesting types (categories) of ent...

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Theophrastus: On demand and real-time automatic annotation and exploration of (web) documents using open linked data

Cited by 13 publications

References 16 publications

Exploiting Linked Data in Exploratory Search

Exploiting Linked Data in Exploratory Search

Ontology based semantic-predictive model for reconfigurable automation systems

Exploiting Linked Data for Open and Configurable Named Entity Extraction

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