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definitions, concept cards, and chapter explanations from digital textbook web pages. It covers question answering about term definitions, distinctions between terms, hierarchical relationships, and discipline-specific contexts, with records that can include source material, answers, related terms, and supporting excerpts. It supports specialized vocabulary learning, domain-focused question answering, and textbook knowledge services.\u003C\u002Fp>","2026-10-15 18:04:50","2026-10-19 17:18:40","0.00",28,0,5000,"digital textbook QA dataset,terminology glossary data,subject term question answering,specialized knowledge training data,textbook knowledge base,term definition dataset,term relation identification,domain SFT data","Digital textbook terminology QA data covering definitions, distinctions, hierarchical relations, and subject context for domain model training and evaluation.","JSON","0.3GB","Digital Textbook Learning, Terminology Understanding, Knowledge Relationship Analysis","Question Answering, Term Definition, Knowledge Reasoning","V1.0","rivo\u002Fsample-packages\u002F2d0aba59-35cf-4ee0-90a3-961f0064eaa8\u002F20261015.zip",[584,589,594,600,605,611,616,621,626,631,637,642],{"id":573,"name":585,"field":586,"intro":587,"type":588},"File Name","file_name","File name","string",{"id":590,"name":591,"field":592,"intro":593,"type":588},8988,"Answer","answer","An accurate and complete response grounded in the textbook material and appropriate to the relevant disciplinary context.",{"id":595,"name":596,"field":597,"intro":598,"type":599},8989,"Keywords","keywords","Terms, concepts, disciplines, and relationships extracted from the question-answer content.","array",{"id":601,"name":602,"field":603,"intro":604,"type":588},8990,"Question","question","A user question about a term definition, a distinction between terms, a hierarchical relationship, or a discipline-specific context.",{"id":606,"name":607,"field":608,"intro":609,"type":610},8991,"Answerable","is_answerable","Indicates whether the source textbook material supports a sufficiently clear and complete answer to the question.","boolean",{"id":612,"name":613,"field":614,"intro":615,"type":588},8992,"Question Type","question_type","The knowledge question category assigned to this question-answer sample.",{"id":617,"name":618,"field":619,"intro":620,"type":599},8993,"Related Terms","related_terms","A list of other terms connected to the core term through definitions, distinctions, or hierarchical relationships.",{"id":622,"name":623,"field":624,"intro":625,"type":588},8994,"Term Relationship Type","relation_type","The semantic or hierarchical relationship between the core term and related terms; use no explicit relationship when none applies.",{"id":627,"name":628,"field":629,"intro":630,"type":588},8995,"Evidence Excerpt","evidence_quote","A quotation or key explanatory passage from the textbook that supports the answer.",{"id":632,"name":633,"field":634,"intro":635,"type":636},8996,"Source Textbook Material","source_material","Terminology and its original context from a digital textbook web page or textbook page; provided as an independent input source for generating questions and answers.","object",{"id":638,"name":639,"field":640,"intro":641,"type":588},8997,"Question Difficulty","difficulty_level","The difficulty level assigned according to the term's specialization, relationship complexity, and dependence on context.",{"id":643,"name":644,"field":645,"intro":646,"type":588},8998,"Disciplinary Context","discipline_context","An explanation of the term's meaning and usage boundaries within a specific discipline, chapter topic, or professional setting.",[648,652,656,660],{"id":649,"question":650,"answer":651},6383,"What content does the dataset include?","The data is built from terminology lists, sidebar definitions, concept cards, and chapter explanations found on digital textbook web pages or textbook pages. It covers definitions, distinctions between terms, hierarchical relationships, and discipline-specific contexts, and records may include source material, answers, related terms, and supporting textbook excerpts.",{"id":653,"question":654,"answer":655},6384,"How can I verify the basis for an answer?","Records include fields such as source material and evidence quotes to help check answers against their textbook context. The available fields depend on the individual record; a separate answerability field indicates whether the source material can sufficiently support an answer.",{"id":657,"question":658,"answer":659},6385,"Which training and application scenarios does it support?","The dataset can support domain-specific supervised fine-tuning, terminology QA evaluation, educational assistants, subject tutoring, and textbook knowledge retrieval. It is also useful for studying how well models understand specialized vocabulary and terminology relationships.",{"id":661,"question":662,"answer":663},6386,"How can I access the data, and what should I check before use?","Check the current product page for available access methods, delivery specifications, and licensing terms. Because the data is sourced from textbook web pages or textbook pages, verify the relevant copyright, permitted use, and downstream usage requirements before applying it.",[665,684,698],{"id":666,"relate_table":667,"relate_id":668,"file_size":573,"file_name":669,"path":8,"duration":573,"cate":483,"file_format":670,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":671,"cate_name":672,"answer":673,"keywords":674,"question":675,"is_answerable":676,"question_type":677,"related_terms":678,"relation_type":679,"evidence_quote":680,"source_material":681,"difficulty_level":682,"discipline_context":683},15099,"union_cubo",1817505669,"样例_3.json","json","{\"answer\": \"Deep learning is a subordinate concept of machine learning; that is, deep learning falls within the scope of machine learning. The two are not in a parallel relationship: machine learning is a broader umbrella term, while deep learning specifically refers to a class of machine learning methods that use neural networks with multi-layer representation structures to learn features layer by layer.\", \"keywords\": \"[\\\"Deep learning\\\", \\\"Machine learning\\\", \\\"Hypernym-hyponym relation\\\", \\\"Neural networks\\\", \\\"Feature representation\\\", \\\"Artificial intelligence\\\"]\", \"question\": \"From the perspective of hypernym-hyponym relations, what is the relationship between deep learning and machine learning?\", \"is_answerable\": \"true\", \"question_type\": \"Hypernym-hyponym relation\", \"related_terms\": \"[\\\"Machine learning\\\"]\", \"relation_type\": \"Hyponym\", \"evidence_quote\": \"Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\", \"source_material\": \"{\\\"source_fragment\\\": \\\"\u003Carticle>\u003Ch3>Deep learning\u003C\u002Fh3>\u003Cp>Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\u003C\u002Fp>\u003Cp>In artificial intelligence courses, machine learning is a higher-level concept, and deep learning falls within the scope of machine learning; convolutional neural networks and recurrent neural networks are common model structures in deep learning.\u003C\u002Fp>\u003C\u002Farticle>\\\", \\\"term\\\": \\\"Deep learning\\\", \\\"definition\\\": \\\"Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\\\", \\\"context_text\\\": \\\"In artificial intelligence courses, machine learning is a higher-level concept, and deep learning falls within the scope of machine learning; convolutional neural networks and recurrent neural networks are common model structures in deep learning.\\\", \\\"source_kind\\\": \\\"Chapter explanation\\\", \\\"chapter_title\\\": \\\"Main Paradigms of Machine Learning\\\"}\", \"difficulty_level\": \"Intermediate\", \"discipline_context\": \"In the disciplinary context of artificial intelligence and machine learning, machine learning covers a variety of methods for learning patterns from data, and deep learning is one branch that relies on multi-layer neural networks for representation learning.\"}","文本","Deep learning is a subordinate concept of machine learning; that is, deep learning falls within the scope of machine learning. The two are not in a parallel relationship: machine learning is a broader umbrella term, while deep learning specifically refers to a class of machine learning methods that use neural networks with multi-layer representation structures to learn features layer by layer.","[\"Deep learning\", \"Machine learning\", \"Hypernym-hyponym relation\", \"Neural networks\", \"Feature representation\", \"Artificial intelligence\"]","From the perspective of hypernym-hyponym relations, what is the relationship between deep learning and machine learning?","true","Hypernym-hyponym relation","[\"Machine learning\"]","Hyponym","Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.","{\"source_fragment\": \"\u003Carticle>\u003Ch3>Deep learning\u003C\u002Fh3>\u003Cp>Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\u003C\u002Fp>\u003Cp>In artificial intelligence courses, machine learning is a higher-level concept, and deep learning falls within the scope of machine learning; convolutional neural networks and recurrent neural networks are common model structures in deep learning.\u003C\u002Fp>\u003C\u002Farticle>\", \"term\": \"Deep learning\", \"definition\": \"Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\", \"context_text\": \"In artificial intelligence courses, machine learning is a higher-level concept, and deep learning falls within the scope of machine learning; convolutional neural networks and recurrent neural networks are common model structures in deep learning.\", \"source_kind\": \"Chapter explanation\", \"chapter_title\": \"Main Paradigms of Machine Learning\"}","Intermediate","In the disciplinary context of artificial intelligence and machine learning, machine learning covers a variety of methods for learning patterns from data, and deep learning is one branch that relies on multi-layer neural networks for representation learning.",{"id":685,"relate_table":667,"relate_id":686,"file_size":573,"file_name":687,"path":8,"duration":573,"cate":483,"file_format":670,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":688,"cate_name":672,"answer":689,"keywords":690,"question":691,"is_answerable":676,"question_type":692,"related_terms":693,"relation_type":694,"evidence_quote":695,"source_material":696,"difficulty_level":682,"discipline_context":697},15098,194024964,"样例_2.json","{\"answer\": \"Formative assessment occurs during the teaching process, focusing on continuously collecting learning information and using it to promptly adjust teachers' teaching and students' learning; summative assessment usually occurs at the end of a teaching stage, focusing on determining the overall achievement of learning objectives. The main difference between the two lies in the timing of implementation and the purpose of assessment, rather than in completely different assessment objects.\", \"keywords\": \"[\\\"Formative assessment\\\", \\\"Summative assessment\\\", \\\"Educational assessment\\\", \\\"Teaching process\\\", \\\"Stage-based assessment\\\", \\\"Feedback and improvement\\\"]\", \"question\": \"What is the difference between formative assessment and summative assessment?\", \"is_answerable\": \"true\", \"question_type\": \"Term differentiation\", \"related_terms\": \"[\\\"Summative assessment\\\"]\", \"relation_type\": \"Related concepts\", \"evidence_quote\": \"Formative assessment is assessment that is continuously conducted during the teaching process, with the purpose of obtaining learning information in a timely manner and adjusting teaching. Summative assessment is usually conducted at the end of a teaching stage, and is used to determine the overall achievement of learning objectives.\", \"source_material\": \"{\\\"source_fragment\\\": \\\"\u003Csection>\u003Ch3>Formative assessment\u003C\u002Fh3>\u003Cp>Formative assessment is assessment that is continuously conducted during the teaching process, with the purpose of obtaining learning information in a timely manner and adjusting teaching.\u003C\u002Fp>\u003Ch3>Summative assessment\u003C\u002Fh3>\u003Cp>Summative assessment is usually conducted at the end of a teaching stage, and is used to determine the overall achievement of learning objectives.\u003C\u002Fp>\u003C\u002Fsection>\\\", \\\"term\\\": \\\"Formative assessment\\\", \\\"definition\\\": \\\"Formative assessment is assessment that is continuously conducted during the teaching process, with the purpose of obtaining learning information in a timely manner and adjusting teaching.\\\", \\\"context_text\\\": \\\"Both formative assessment and summative assessment are used to judge learning situations, but the former runs throughout the teaching process and serves timely improvement, while the latter usually makes a judgment at the end of a stage about the overall achievement of learning objectives.\\\", \\\"source_kind\\\": \\\"Sidebar definition\\\", \\\"chapter_title\\\": \\\"Educational Measurement and Classroom Assessment\\\"}\", \"difficulty_level\": \"Intermediate\", \"discipline_context\": \"In educational assessment studies, formative assessment serves feedback and improvement during the teaching process, while summative assessment serves stage-level outcome judgment. The two can be used together and should not be simply understood as mutually exclusive assessment methods.\"}","Formative assessment occurs during the teaching process, focusing on continuously collecting learning information and using it to promptly adjust teachers' teaching and students' learning; summative assessment usually occurs at the end of a teaching stage, focusing on determining the overall achievement of learning objectives. The main difference between the two lies in the timing of implementation and the purpose of assessment, rather than in completely different assessment objects.","[\"Formative assessment\", \"Summative assessment\", \"Educational assessment\", \"Teaching process\", \"Stage-based assessment\", \"Feedback and improvement\"]","What is the difference between formative assessment and summative assessment?","Term differentiation","[\"Summative assessment\"]","Related concepts","Formative assessment is assessment that is continuously conducted during the teaching process, with the purpose of obtaining learning information in a timely manner and adjusting teaching. Summative assessment is usually conducted at the end of a teaching stage, and is used to determine the overall achievement of learning objectives.","{\"source_fragment\": \"\u003Csection>\u003Ch3>Formative assessment\u003C\u002Fh3>\u003Cp>Formative assessment is assessment that is continuously conducted during the teaching process, with the purpose of obtaining learning information in a timely manner and adjusting teaching.\u003C\u002Fp>\u003Ch3>Summative assessment\u003C\u002Fh3>\u003Cp>Summative assessment is usually conducted at the end of a teaching stage, and is used to determine the overall achievement of learning objectives.\u003C\u002Fp>\u003C\u002Fsection>\", \"term\": \"Formative assessment\", \"definition\": \"Formative assessment is assessment that is continuously conducted during the teaching process, with the purpose of obtaining learning information in a timely manner and adjusting teaching.\", \"context_text\": \"Both formative assessment and summative assessment are used to judge learning situations, but the former runs throughout the teaching process and serves timely improvement, while the latter usually makes a judgment at the end of a stage about the overall achievement of learning objectives.\", \"source_kind\": \"Sidebar definition\", \"chapter_title\": \"Educational Measurement and Classroom Assessment\"}","In educational assessment studies, formative assessment serves feedback and improvement during the teaching process, while summative assessment serves stage-level outcome judgment. The two can be used together and should not be simply understood as mutually exclusive assessment methods.",{"id":699,"relate_table":667,"relate_id":700,"file_size":573,"file_name":701,"path":8,"duration":573,"cate":483,"file_format":670,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":702,"cate_name":672,"answer":703,"keywords":704,"question":705,"is_answerable":676,"question_type":706,"related_terms":707,"relation_type":708,"evidence_quote":709,"source_material":710,"difficulty_level":711,"discipline_context":712},15097,2138492937,"样例_1.json","{\"answer\": \"An ontology is a formal, explicit specification of the concepts and relationships in a specific domain. In knowledge representation systems, it is used to uniformly specify the concepts, properties, and relationships among concepts in a domain, thereby supporting consistent semantic understanding and information exchange across different systems.\", \"keywords\": \"[\\\"Ontology\\\", \\\"Knowledge representation\\\", \\\"Semantic modeling\\\", \\\"Conceptual relations\\\", \\\"Formal specification\\\"]\", \"question\": \"What does ontology refer to in knowledge organization and artificial intelligence knowledge representation?\", \"is_answerable\": \"true\", \"question_type\": \"Term definition\", \"related_terms\": \"[]\", \"relation_type\": \"No explicit relation\", \"evidence_quote\": \"An ontology is a formal, explicit specification of the concepts and relationships in a specific domain.\", \"source_material\": \"{\\\"source_fragment\\\": \\\"\u003Cdiv>Term: Ontology\u003C\u002Fdiv>\u003Cdiv>Definition: An ontology is a formal, explicit specification of the concepts and relationships in a specific domain.\u003C\u002Fdiv>\u003Cdiv>Context: In knowledge representation systems, an ontology is used to specify concepts, properties, and the relationships among concepts, enabling different systems to understand and exchange information according to a unified semantics.\u003C\u002Fdiv>\\\", \\\"term\\\": \\\"Ontology\\\", \\\"definition\\\": \\\"An ontology is a formal, explicit specification of the concepts and relationships in a specific domain.\\\", \\\"context_text\\\": \\\"In knowledge representation systems, an ontology is used to specify concepts, properties, and the relationships among concepts, enabling different systems to understand and exchange information according to a unified semantics.\\\", \\\"source_kind\\\": \\\"Concept card\\\", \\\"chapter_title\\\": \\\"Knowledge Representation and Semantic Modeling\\\"}\", \"difficulty_level\": \"Basic\", \"discipline_context\": \"This term belongs to the fields of artificial intelligence knowledge representation and semantic modeling, emphasizing formal modeling of domain knowledge rather than referring generally to a specific knowledge base or database.\"}","An ontology is a formal, explicit specification of the concepts and relationships in a specific domain. In knowledge representation systems, it is used to uniformly specify the concepts, properties, and relationships among concepts in a domain, thereby supporting consistent semantic understanding and information exchange across different systems.","[\"Ontology\", \"Knowledge representation\", \"Semantic modeling\", \"Conceptual relations\", \"Formal specification\"]","What does ontology refer to in knowledge organization and artificial intelligence knowledge representation?","Term definition","[]","No explicit relation","An ontology is a formal, explicit specification of the concepts and relationships in a specific domain.","{\"source_fragment\": \"\u003Cdiv>Term: Ontology\u003C\u002Fdiv>\u003Cdiv>Definition: An ontology is a formal, explicit specification of the concepts and relationships in a specific domain.\u003C\u002Fdiv>\u003Cdiv>Context: In knowledge representation systems, an ontology is used to specify concepts, properties, and the relationships among concepts, enabling different systems to understand and exchange information according to a unified semantics.\u003C\u002Fdiv>\", \"term\": \"Ontology\", \"definition\": \"An ontology is a formal, explicit specification of the concepts and relationships in a specific domain.\", \"context_text\": \"In knowledge representation systems, an ontology is used to specify concepts, properties, and the relationships among concepts, enabling different systems to understand and exchange information according to a unified semantics.\", \"source_kind\": \"Concept card\", \"chapter_title\": \"Knowledge Representation and Semantic Modeling\"}","Basic","This term belongs to the fields of artificial intelligence knowledge representation and semantic modeling, emphasizing formal modeling of domain knowledge rather than referring generally to a specific knowledge base or database.","{\"answer\":\"Deep learning is a subordinate concept of machine learning; that is, deep learning falls within the scope of machine learning. The two are not in a parallel relationship: machine learning is a broader umbrella term, while deep learning specifically refers to a class of machine learning methods that use neural networks with multi-layer representation structures to learn features layer by layer.\",\"keywords\":\"[\\\"Deep learning\\\", \\\"Machine learning\\\", \\\"Hypernym-hyponym relation\\\", \\\"Neural networks\\\", \\\"Feature representation\\\", \\\"Artificial intelligence\\\"]\",\"question\":\"From the perspective of hypernym-hyponym relations, what is the relationship between deep learning and machine learning?\",\"is_answerable\":\"true\",\"question_type\":\"Hypernym-hyponym relation\",\"related_terms\":\"[\\\"Machine learning\\\"]\",\"relation_type\":\"Hyponym\",\"evidence_quote\":\"Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\",\"source_material\":\"{\\\"source_fragment\\\": \\\"\u003Carticle>\u003Ch3>Deep learning\u003C\\\u002Fh3>\u003Cp>Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\u003C\\\u002Fp>\u003Cp>In artificial intelligence courses, machine learning is a higher-level concept, and deep learning falls within the scope of machine learning; convolutional neural networks and recurrent neural networks are common model structures in deep learning.\u003C\\\u002Fp>\u003C\\\u002Farticle>\\\", \\\"term\\\": \\\"Deep learning\\\", \\\"definition\\\": \\\"Deep learning is a branch of machine learning that uses neural networks with multi-layer representation structures to learn feature representations layer by layer from data.\\\", \\\"context_text\\\": \\\"In artificial intelligence courses, machine learning is a higher-level concept, and deep learning falls within the scope of machine learning; convolutional neural networks and recurrent neural networks are common model structures in deep learning.\\\", \\\"source_kind\\\": \\\"Chapter explanation\\\", \\\"chapter_title\\\": \\\"Main Paradigms of Machine Learning\\\"}\",\"difficulty_level\":\"Intermediate\",\"discipline_context\":\"In the disciplinary context of artificial intelligence and machine learning, machine learning covers a variety of methods for learning patterns from data, and deep learning is one branch that relies on multi-layer neural networks for representation learning.\"}","MOBIUSI INC","https:\u002F\u002Fstatic.mobiusi.com\u002Fmobiwusi\u002F20250829\u002F7bf4ac62b4533e2b809998a6f1c5491e.png?Expires=1792463130&OSSAccessKeyId=LTAI5tH8MQqhgeiAtrzCHrxM&Signature=v6Tr8jBrdm4s5wU1SnnyGsYg%2Bhw%3D&x-oss-process=image%2Fresize%2Cm_fill%2Cw_35%2Ch_35",[717,723],{"type":718,"title":719,"count_data":720},"bar","Length distribution",[721],{"name":722,"value":483},"1k lower",{"type":724,"title":725,"count_data":726},"pie","Language distribution",[727],{"name":728,"value":483},"English"]