SemTag and seeker

Dill, Stephen; Eiron, Nadav; Gibson, David; Gruhl, Daniel; Guha, Ramanathan V.; Jhingran, Anant; Kanungo, Tapas; Rajagopalan, Sridhar; Tomkins, Andrew; Tomlin, John A.; Zien, Jason Y.

doi:10.1145/775152.775178

Cited by 259 publications

(15 citation statements)

References 17 publications

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“…RELATED WORK A significant amount of prior work has considered the automated annotation of Web content using shallow natural language processing (e.g. morphological analysis, named entity recognition) and disambiguation techniques [EMS+00,DEG+03]. Semi-automatic and automatic approaches to full-text semantic annotation have proved to be relatively scalable.…”

Section: Resultsmentioning

confidence: 99%

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Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces

Küngas

Dumas

2009

2009 IEEE International Conference on Services Computing

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Abstract-Research in the field of semantic Web services aims at automating the discovery, selection, composition and management of Web services based on semantic descriptions. However, the applicability of many solutions developed in this field is hampered by the costs associated with semantically annotating large repositories of Web services. To overcome this gap we propose a practical method for semantically annotating collections of XML Schemas and Web service interfaces. We have evaluated this method on a large repository of governmental Web services. The evaluation shows that relatively simple techniques are surprisingly cost-effective, saving hundreds of man-hours of semantic annotation effort. Moreover, the proposed method does not assume the availability of a preexisting ontology or controlled vocabulary. Instead, the space of annotations is dynamically built during the annotation process.

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Section: Resultsmentioning

confidence: 99%

“…Semi-automatic and automatic approaches to full-text semantic annotation have proved to be relatively scalable. Dill et al [DEG+03] present a case study in which they apply such techniques to annotate a corpus of 264 million web pages. However, the precision of these techniques is limited by the fact that they deal with unstructured content.…”

Section: Resultsmentioning

confidence: 99%

Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces

Küngas

Dumas

2009

2009 IEEE International Conference on Services Computing

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“…Semi-automatic approaches to facilitate the annotation process exist for many of the annotation tasks, for example, human activity recognition [9], video object detection and segmentation [41], semantic tagging of large corpora [15], and animal behaviour [23]. Most available methods are interactive and require a human-in-the-loop approach.…”

Section: Related Workmentioning

confidence: 99%

Semi-Automatic Annotation of Objects in Visual-Thermal Video

Berg

Johnander

Gevigney

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Deep learning requires large amounts of annotated data. Manual annotation of objects in video is, regardless of annotation type, a tedious and time-consuming process. In particular, for scarcely used image modalities human annotation is hard to justify. In such cases, semi-automatic annotation provides an acceptable option. In this work, a recursive, semi-automatic annotation method for video is presented. The proposed method utilizes a state-of-the-art video object segmentation method to propose initial annotations for all frames in a video based on only a few manual object segmentations. In the case of a multi-modal dataset, the multi-modality is exploited to refine the proposed annotations even further. The final tentative annotations are presented to the user for manual correction. The method is evaluated on a subset of the RGBT-234 visual-thermal dataset reducing the workload for a human annotator with approximately 78% compared to full manual annotation. Utilizing the proposed pipeline, sequences are annotated for the VOT-RGBT 2019 challenge.

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“…To this camp the only real option is automation. The other camp points out that automation is even more error-prone than manual creation, as current efforts to automatic semantic annotation on massive scales produces only moderate results of between 80% and 90% correct, at the very best [1]. They claim that the remaining 10% will always be beyond reach because it requires significant amounts of human-level intelligence to be done correctly.…”

Section: Related Workmentioning

confidence: 99%

The Semantic Web: From Representation to Realization

Þórisson

Spivack

Wissner

2010

Lecture Notes in Computer Science

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Abstract. A semantically-linked web of electronic information -the Semantic Web -promises numerous benefits including increased precision in automated information sorting, searching, organizing and summarizing. Realizing this requires significantly more reliable meta-information than is readily available today. It also requires a better way to represent information that supports unified management of diverse data and diverse Manipulation methods: from basic keywords to various types of artificial intelligence, to the highest level of intelligent manipulation -the human mind. How this is best done is far from obvious. Relying solely on hand-crafted annotation and ontologies, or solely on artificial intelligence techniques, seems less likely for success than a combination of the two. In this paper describe an integrated, complete solution to these challenges that has already been implemented and tested with hundreds of thousands of users. It is based on an ontological representational level we call SemCards that combines ontological rigour with flexible user interface constructs. SemCards are machine-and human-readable digital entities that allow non-experts to create and use semantic content, while empowering machines to better assist and participate in the process. SemCards enable users to easily create semantically-grounded data that in turn acts as examples for automation processes, creating a positive iterative feedback loop of metadata creation and refinement between user and machine. They provide a holistic solution to the Semantic Web, supporting powerful management of the full lifecycle of data, including its creation, retrieval, classification, sorting and sharing. We have implemented the SemCard technology on the semantic Web site Twine.com, showing that the technology is indeed versatile and scalable. Here we present the key ideas behind SemCards and describe the initial implementation of the technology.

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SemTag and seeker

Cited by 259 publications

References 17 publications

Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces

Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces

Semi-Automatic Annotation of Objects in Visual-Thermal Video

The Semantic Web: From Representation to Realization

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