Extracting historical time periods from the Web

Boer, Victor de; Someren, Maarten van; Wielinga, B.J.

doi:10.1002/asi.21378

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…Secondly, we look for ontologies in order to represent temporal information in RDF. [ 6 ] study TimeML to annotate historical periods, but its XML focus is out of our scope. Time Ontology in OWL 7 reflects classical works of [ 7 , 8 ] and [ 9 ], overcoming problems of the original OWL-Time 8 that defined instant (point of time) and interval (period of time), but limited itself to the Gregorian calendar [ 10 ].…”

Section: Related Work and Unsolved Issuesmentioning

confidence: 99%

Building Linked Open Date Entities for Historical Research

Sugimoto

2021

Metadata and Semantic Research

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Time is a focal point for historical research. Although existing Linked Open Data (LOD) resources hold time entities, they are often limited to modern period and year-month precision at most. Therefore, researchers are currently unable to execute co-reference resolution through entity linking to integrate different datasets which contain information on the day level or remote past. This paper aims to build an RDF model and lookup service for historical time at the lowest granularity level of a single day at a specific point in time, for the duration of 6000 years. The project, Linked Open Date Entities (LODE), generates stable URIs for over 2.2 million entities, which include essential information and links to other LOD resources. The value of date entities is discussed in a couple of use cases with existing datasets. LODE facilitates improved access and connectivity to unlock the potential for the data integration in interdisciplinary research.

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Section: Related Work and Unsolved Issuesmentioning

confidence: 99%

Building Linked Open Date Entities for Historical Research

Sugimoto

2021

Metadata and Semantic Research

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“…Some attempt to homogenize chronological values to some extend as far as they conform to specific date formats ([2] [11]). The work in [14] presents an automatic method for the extraction of time periods related to ontological concepts from the web. The method involves an information extraction phase which uses simple regular expressions to extract years from documents.…”

Section: Related Workmentioning

confidence: 99%

Using type and temporal semantic enrichment to boost content discoverability and multilingualism in the Greek cultural aggregator SearchCulture.gr

Georgiadis

Papanoti

Paschou

et al. 2018

IJMSO

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Most aggregators face challenges regarding searchability, discoverability and visual presentation of their content due to metadata heterogeneity across the collections. Particularly for cultural and historical material, keyword-based searching is far from sufficient. Structured item types and temporal information are key metadata for the discoverability of cultural heritage content. We developed an innovative metadata enrichment and homogenization scheme for types and temporal information that is both effective and user-friendly and we embedded it in the ingestion workflow of SearchCulture.gr, the greek cultural heritage aggregator developed by the National Documentation Centre (EKT). Two key components of the enrichment scheme are Semantics.gr, a platform for publishing vocabularies that contains a mapping tool for massive semantic enrichment, and a parametric tool for chronological normalization. We enriched and homogenized the aggregated content with respect to types and temporal information which subsequently allowed us to develop advanced multilingual search and browsing features, including hierarchical navigation on types and historical periods, searching and faceting on types, time spans and historical periods, a tag cloud of types and an interactive timeline/histogram.

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“…The affect of terminology evolution over time is addressed in [13,28,55,56,118]. In [13], Berberich et al proposed a method based on a hidden Markov model for reformulating a query into terminology prevalent in the past.…”

Section: Searching With the Awareness Of Terminology Changesmentioning

confidence: 99%

“…They proposed to discover concepts that evolve over time using association rule mining, and used the discovered concepts to translate time-sensitive queries and answered appropriately. In [28], de Boer et al presented a method for automatically extracting event time periods related to concepts from web documents. In their approach, event time periods are extracted from different documents using regular expressions, such as, numerical notations for years.…”

Section: Searching With the Awareness Of Terminology Changesmentioning

confidence: 99%

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Time-aware approaches to information retrieval

Kanhabua

2012

SIGIR Forum

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In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on term-matching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query.Our contributions in this thesis are different time-aware approaches within three topics in IR: content analysis, query analysis, and retrieval and ranking models. In particular, we aim at improving the retrieval effectiveness by 1) analyzing the contents of temporal document collections, 2) performing an analysis of temporal queries, and 3) explicitly modeling the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is available. In this thesis, we analyze the contents of documents in order to determine the time of non-timestamped documents using temporal language models. We subsequently employ the temporal language models for determining the time of implicit temporal queries, and the determined time is used for re-ranking search results in order to improve the retrieval effectiveness.We study the effect of terminology changes over time and propose an approach to handling terminology changes using time-based synonyms. In addition, we propose different methods for predicting the effectiveness of temporal queries, so that a particular query enhancement technique can be performed to improve the overall performance. When the time dimension is incorporated into ranking, documents will be ranked according to both textual and temporal similarity. In this case, time uncertainty should also be taken into account. Thus, we propose a ranking model that considers the time uncertainty, and improve ranking by combining multiple features using learning-to-rank techniques.Through extensive evaluation, we show that our proposed time-aware approaches outperform traditional retrieval methods and improve the retrieval effectiveness in searching temporal document collections.

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Extracting historical time periods from the Web

Cited by 4 publications

References 16 publications

Building Linked Open Date Entities for Historical Research

Building Linked Open Date Entities for Historical Research

Using type and temporal semantic enrichment to boost content discoverability and multilingualism in the Greek cultural aggregator SearchCulture.gr

Time-aware approaches to information retrieval

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